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The AE Scienceand Technology

JOURNAL OF
ACOUSTIC
EMISSION
Vol.37/January-December 2020

Editors: M.A. Hamstad (AEWG) and G. Manthei (EWGAE)

Sandia’s TX-100 Turbine Blade Testing
Endorsed by
AEWG
and
EWGAE

Published byAcoustic Emission Group
Encino, CA USA©2020 Acoustic Emission GroupJournal of Acoustic Emission
Volume 37 (2020)

Contents
Tutorial
37-001

Alan G. Beattie, Acoustic Emission Testing of Structures using Source Location
1 – 8537-086
R. Vidya Sagar and Swarnangshu Ghosh, Relating Damage Assessed by Using
Intensity Signal Analysis of Acoustic Emission with Surface Strain in Concrete and Ultrasonic
Pulse Velocity in Reinforced Concrete Beams under Monotonically Increasing Load86 - 108
Selected Papers from the Proceedings of E-GLEA-10
Jose Ruzzante and Antolino Gallego, Topical Editors
37-S001
Basgall A., Ferrari, G., and Gómez, M.P., Neural Networks applied to themonitoring of drilling processes in geological material samples, J. Acoust. Emiss. 37, 2020, S1S10
37-S011Bonelli Toro, A. G., Gómez M. P., Machine learning applied to acoustic emission
for tool wear classification during milling of composite materials, J. Acoust. Emiss. 37, 2020,
S11-S20
37-S021Groth, E. B., Shumacher, G., Idzi J., Rojo Tanzi, B. N., Clarke, T. G. R., Lima, T.
R. S. and Jacques, R. C., Acoustic emission numerical analysis applied to structural healthmonitoring of laminar structures (Guided waves), J. Acoust. Emiss. 37, 2020, S21-S29
37-S030
da Silva, D.S., Groth, E.B., Walter, J.S., Lutckmeier, F., Zuglian, G. F., Jaques, R.
C., de Lima, T.R.S. and Clarke, T.G., Study of fiber Bragg gratings (FBG) as acoustic emission
sensor to inspect and monitoring fiber-glass reinforced polymers (FGRP) degradation, J. Acoust.
Emiss. 37, 2020, S30-S37
37-S038
Veca, A. C., Valese, N., Ruzzante, J., Albalat, A., Marino, A., and Reyes Reyes,
M. V., Autonomous acoustic system for passive acoustic monitoring of cetaceans, J. Acoust.
Emiss. 37, 2020, S38-S54.
37-S055
Zitto, M.E., Sassano, M.P., Muszkats, J.P., Filipussi, D.A., and Piotrkowski, R.,
Energy and shannon entropy in acoustic emission signals for damage evaluation in andesite rock,J. Acoust. Emiss. 37, 2020, S55-S64
37-S065
López Pumarega, M. I., Ruzzante, J. E., and Gómez, M. P., La Emisión Acústica,
su Historia en la Argentina, J. Acoust. Emiss. 37, 2020, S65-S76. (in Spanish)
Contents of the Proceedings of E-GLEA 10 (Índice)
Cover photographs: D. T. Griffith, G. Smith, M. Casias, S. Reese, and T. W. Simmermacher,
Sandia Report, SAND2005-6454, 2006 and A.G. Beattie (37-001).

Tutorial

Acoustic Emission Testing of Structures using Source Location
Alan G. Beattie
Sandia National Laboratories, Albuquerque, NM
Abstract
This is a tutorial article and covers the physical principles encountered in the field of acoustic
emission (AE). This is followed by instrumentation and signal analysis methods. The second half
is devoted to structural AE testing that relies on source location methods with detailed presentation of case histories. Three test setup examples and results on fiber-reinforced wind turbine
blades and pressure vessels are provided.
Keywords: physical principles, instrumentation, signal analysis methods, source location, fiberreinforced wind turbine blades, pressure vessels.
Foreword
The use of acoustic emission (AE) techniques in both non-destructive testing (NDT) and research and development has expanded rapidly since its first real investigation by Kaiser [1] overseventy years ago. It was recognized early that one might be able to triangulate to the source of anemission by detecting the same sound wave at several different sensors. An early successful usageof triangulation in an actual NDT test was reported by Green [2] in 1966 on a hydrotest of a Saturn
S-II propellant tank. Since then the art of acoustic emission source location has rapidly expanded.
Part of this expansion has been pushed by the many advances in the field of personal computers.
The technology has changed from simple analog systems to sophisticated digital systems. A premium has been placed on acquiring data and using universal type programs, included in the commercial systems, to analyze the data. These programs are a marvelous advance over the first analysis programs included in the early source location systems but they cannot cover all of the possible types of analysis in the field of acoustic emission testing.
The author has been using AE techniques at Sandia National Laboratories in a wide variety of
applications for most of the last forty years. Many of these applications required the developmentof specialized testing and/or analysis techniques. Obviously, as in any new field, not all worked,
but a general approach was slowly developed. This monograph will describe in considerable detailthe testing and analysis techniques developed in two areas: Wind Energy and Small Pressure Vessels. These applications use multiple sensors to perform acoustic emission source location. All
involve analysis of the digital data. When the programs included in the early commercial systems
were not readily adaptable for the analysis, custom programs in FORTRAN were written. As thefield has evolved, many of the techniques of these programs have been incorporated in the software
furnished with the commercial systems. However, the details of the commercial software are hidden in executable files. A few of the Sandia developed FORTRAN programs are included to illustrate the methods and assumptions used in AE programs. These are working programs and can beused with necessary minor adaptations to fit a test. No apologies are made for using FORTRAN.
It is one of the easier languages, in which to follow the workings of a code. And it is still used
despite the decline and death of many computer languages in the last fifty years.
This monograph first covers a general discussion of acoustic emission and AE sources. It thengoes into a general discussion of the properties of acoustic waves in materials. This is the basic
J. Acoustic Emission, 37 (2020)

1©2020 Acoustic Emission Groupbackground, which one should have to understand what is occurring in an acoustic test, either
ultrasonics or acoustic emission. It is included to aid readers, who are new to the field of acoustics
in solids, and can serve as reference material for others. Briefly covered are acoustic waves insolids including different acoustic modes, reflection and refraction of waves, attenuation, superposition and dispersion of waves, resonance effects and other phenomena, which will be encountered in an AE test. There is also a discussion of sensors, couplants, preamplifiers and other topics
involved in performing an AE test. There is a brief section on AE source mechanisms and one on
various parameters of AE waveforms, which are used in most commercial systems. All of thismaterial was covered in an early review article [3] and makes no pretense of being new or different.
It is hoped that the most useful parts of the monograph will be the discussions of the design, execution and analysis of several types of AE tests. No two tests are ever identical but the general
methodology is widely applicable.
Selected Reading List from J. of Acoustic Emission (JAE). (These are available for download atwww.aewg.org. Download the volume needed; e.g., get vol. 22 for Tensi article.)
H.M. Tensi, The Kaiser Effect and Its Scientific Background, J. AE, 2004, 22: S1-S16.
T.F. Drouillard, A History of Acoustic Emission, J. AE, 1996, 14: 1-34.
Chapter 1 Introduction to Acoustic Emission
Acoustic emission (AE) is the study and practical use of acoustic waves generated by a material
subjected to an external stress. The actual use of the phenomena in technology dates back to earlyminers, who exited a mine when the rocks or supporting timbers started groaning. Tin cry when atin bar was bent was known soon after the production of metallic tin. In 1947, C.S. Barrett [4]mapped a low temperature phase transition in Lithium-Magnesium alloys by sticking a phonograph needle into the crystal and recording the output as the temperature was changed. J. Kaiser
investigated the signals produced by samples undergoing tensile testing and discovered the Kaiser
effect, that no signals were generated by a sample upon the second loading until the previous maximum load was exceeded. After Kaiser’s thesis was published in 1950, several groups started investigating the phenomena for possible use in testing structures. In the early 1960’s, Allen Greenand a group at Aerojet Corp. started using acoustic emission in the testing of Saturn Rocket propellant tanks. They used a form of triangulation using the arrival times of the acoustic pulse at
several AE sensors. This was the direct precursor of the work that is described in this paper.
Acoustic emission is the generation of an acoustic wave by the rapid change in the stress stateof some region in the material. This is usually caused by the application of an external stimulus tothe material. The material can be a solid, liquid, gas or plasma and the external stress can be appliedmechanically, thermally, magnetically, etc. The only requirement is that the stress change is rapid
enough that some energy is transmitted to the surrounding material and dissipates as an acoustic
wave. On a macroscopic scale, this definition includes earthquakes and thunder and on a microscopic scale, it includes the fracture of crystallites and martensitic phase transformations. The mostimportant characteristic of the phenomena is that the occurrence of the burst of energy is completely determined by the local conditions, the local stress field and the physical state of the region.
The result of this is that the exact time that the energy burst occurs cannot be determined beforehand. Not only is the time of occurrence indeterminate, but also the exact details of the released
energy. In general, the event generating the emission is irreversible. Neither a ruptured geologic
fault nor a fractured crystallite in a metal plate will heal spontaneously.
2

Once the acoustic wave is generated, it then travels throughout the material and can be detected
at considerable distances from its point of origin. As it travels from its source to the point of detection, it is subjected to all the characteristics and variations of its acoustic path. Its energy isattenuated by geometric spreading and scattering by both microscopic and macroscopic variationsin the structure of the material as well as other kinds of attenuations, which may be present. Its
frequency content is generated by the source and modified as it travels the acoustic path. The
primary information carried by the wave is the time of arrival and the acoustic energy detected ateach sensor on the structure. The excitation of the sensor indicates that something happened in thespecimen at a specific time. The amplitude gives an indication of the level of the disturbance. The
apparent location of the source and all other characteristics of the detected signal are modified by
the characteristics of the acoustic path to each sensor as well as the characteristics of each sensor.
The result is that while in a highly controlled laboratory experiment, a reasonable estimate can bemade of the characteristics of the emission source, in a test on an actual structure, we are limited
to what are basically statistical estimates from multiple emissions to tell us what is happening inthe specimen. Statistical analysis is not generally used in the study of acoustic emissions, but thestate of the specimen as well as the location coordinates of the sources are inferred from averagesof calculated values from multiple emissions.
Acoustic energy is ubiquitous. It travels through all materials where there is an interaction
between atoms. Only a hard vacuum does not transmit acoustic energy. The distance that an acoustic signal can travel depends on its initial amplitude and the attenuation of the material that comprises the object or path to the object. This is an advantage in that an AE test can cover an entire
structure and not just a small region of it. It is a disadvantage in that many acoustic signals, whichhave nothing to do with the test, can be present and detected. Acoustic isolation of the test object
is highly desirable. Most AE testing filters out acoustic signals at frequencies below 20 kHz, thus
ignoring background noise and verbal communication in the test area.
Finally, an AE test occurs in real time. The test does not find preexisting defects but detect (orflaw) growth as it occurs. If the stress application simulates the conditions, which test structure isdesigned to experience in actual operation, serious acoustic emission will indicate pathologicalflaws, which could lead to failure of part or all of the structure. The amount of emission seen andthe locations of the emission sources will depend both on the design of the structure and the materials used in its fabrication. An example is the bending of a strip of glass and a strip of fiber reinforced plastic (FRP) with identical dimensions. The strip of glass would likely give a single highamplitude burst of emission just at failure while the FRP would show a period of low-level emissions followed by a few higher-level emissions and then one or more high amplitude emissions as
the strip started to fail and then tore apart or snapped. The interpretation of the emissions that aredetected is the job of the test engineer. The value of the information obtained from a test is determined first by the design of the test and then by the analysis of detected emission. On complex
structures both the test design and interpretation are seldom simple. It is the purpose of this monograph to aid the test engineer in both areas.
1.1 Sources of Acoustic Emission:
Acoustic emissions are acoustic waves generated by a rapid change in the stress state of aregion in a material. Acoustic waves are one of two non-electromagnetic methods of transmittingenergy through a material. The other method is thermal diffusion. A difference between the two isthat thermal diffusion involves direct energy transfer between individual atoms while an acoustic
wave transfers energy by a cooperative motion of atoms. An acoustic wave can be as simple as a3

pressure pulse in a gas or liquid or as complicated as the many modes of transmission in a boundedsolid. The generation of an acoustic wave almost invariably involves a region of atoms instead ofa single atom.
The most common acoustic waves are sound waves in air and water. These are produced byvibration of a surface such as the vocal apparatus of an animal, a leaf in the wind or the diaphragmof a speaker. These sources are quasi-continuous, involving a modulated vibration of an interface
between the generating region and the transmitting medium. Another type of acoustic wave isgenerated by a sudden disturbance in or on the material itself. The disturbance can be described asa transient event and produces a transient acoustic wave. Such a transient wave is what we define
as acoustic emission. It may have the form of a single damped wave with a complex frequency
content or appear as many small transient events, which sum into quasi-continuous noise. Its primary characteristics are that it is generated in the medium which transmits it and it is transient innature. Any sudden movement of a group of atoms at velocities near the sound velocity in a material can produce a transient acoustic wave. The apparent quasi–continuous signals, which are often
seen, are generated by the overlap of many transient events instead of a continuous vibration of asurface. Figure 1 shows waveforms from a transient event (often called burst emission) and froma superposition of many transient events (continuous emission). The long decay of the transient
event is produced by reflections of the original wave in the complex metal specimen (and within
the sensor used).
The size of the region generating an emission can vary from a small row or plate of atoms
moving into a new crystal position during a martensitic phase transition to a fracture in a subduction zone between two continental plates. The energy released in an AE event will be, very roughly,
proportional to the volume of the source. The energy range of these waves ranges from smaller
than an electron volt to the energy contained in a thunder clap or that of the recent earthquakes inJapan and Chile. The frequencies of these waves are generally related inversely to the volume of
the generating region, ranging from a few mHz for earthquakes to several MHz in fine grain metals. An important characteristic of acoustic emission in solids is that the fracture of the region
occurs when the local stress tensor exceeds the strength of the region for that tensor. In other words,
the exact time when the emission occurs is strictly dependent upon the local conditions. In a metal,
for example, the precise environment of every crystallite will be different and the fracture of one
crystallite or the boundary between two crystallites will make small changes in the local stresstensors in the other crystallites. This complete dependence upon the local conditions means that it
is impossible to predict the exact time that any one emission will occur or what the time interval
between any two emissions will be. Acoustic emissions occur randomly in time. The parametersof the detected emission depend not only on the characteristics of the emission source but also
upon the characteristics and geometry of the medium between the source and sensor, and upon thecharacteristics of the sensor and the couplant between sensor and medium. The signal characteristics detected by different identical sensors located at various positions on a specimen will often
have large variations for a single emission. The more complex the medium, in which the AE isgenerated and transmitted, the more likely there will be large differences between detected signals
from the same AE at different sensors. The combination of random origination in time and lack of
duplicate signals are fundamental characteristics of AE. As such, they have a profound effect on
the type of analysis used on the detected emissions.

4Figure 1 Examples of burst emission and continuous emission from a high-strength aluminumalloy.
In structural metals, both intergranular and transgranular cracking of metal crystallites cangenerate low level acoustic emission. Emission will also be generated at inclusions both by fractureof the inclusion and the breaking of the bonds between the inclusion and metal. Crack growth
where at least several crystallites are involved can generate emission of moderate to large amplitudes. The presence of corrosion on a metal surface can be detected by the fracture of brittle corrosion byproducts while active corrosion can generate emission from bubble formation and collapse. In high stress environments, twinning can occur in some metals like Mg and Ti and this can
produce emission. Creep deformation in metals may involve the movement of dislocations. However, the creep rate in structural materials under normal usage is usually far too slow to generate
AE useable for flaw detection except at the tertiary stage where creep cracks develop. Creep also
causes the cracking of brittle oxide films. These creep-induced cracks can be detected.
Source mechanisms in FRP include; matrix cracking, debonds between the matrix and fibers,
fiber fracture and crack propagation, and delamination. These source mechanisms can all be activated by the application of an external stress. Based upon the idea that the emission amplitude isrelated to the volume of the source, matrix cracking should produce the lowest amplitude emission,
followed by matrix-fiber debonding. Fiber breakage would be louder still, partly due to the amountof energy released by the fracture of a high strength fiber. Finally, crack propagation, which includes all three of the previous mechanisms, would produce the highest amplitude emissions. This5

appears to generally be the case, but trying to quantify it as a general rule has never worked well.
This is probably because FRP is usually laid up by hand and never comes close to the structural
uniformity of a well annealed metal. As a general rule, the first loading of an FRP structure will
generate AE at loads well below the strength of the structure. The most likely cause of this emission is local regions with high residual stresses which developed during the curing process. These
stresses are relieved by matrix cracks in the FRP during the first loading. They have little effect on
the strength of the FRP and the emission usually stops before the design loads are reached. Thisemission is seldom seen upon reloading. Another source of emission in FRP structures is often
seen during cyclic loading. This emission appears in the middle ranges of the cyclic load where
the rate of change in applied stress is highest, instead of at the peak loads. It appears to be causedby friction between small regions in the structure, which are not bonded. This type of emission canbe present during the entire cyclic load test. However, it does not correlate with structural damage
in the test object. Structural damage is associated with emission, which occurs near the peak loads,
especially during the rising load.
The above discussion shows that most AE mechanisms involve a permanent change in the
micro structure of the material. Once a micro-fracture occurs, it will not happen again unless there
is some sort of healing mechanism occurring. Therefore, acoustic emission appears irreversible.
The Kaiser effect, where the re-stressing of a specimen will not generate AE until the previous
load level has been exceeded, is caused by this irreversibility. The Kaiser effect holds very wellfor the immediate re-stressing of a metal specimen but not so well for composites. The problem isthat the reapplication of an external stress does not necessarily take the specimen along the same
micro-stress path. One often sees emission on subsequent loadings at loads lower than previously
reached. The ratio of the load value when emission starts on subsequent loadings over the maximum value of the previous loading is known as the Felicity ratio and it is an indicator of possible
damage induced by the previous loading. Many NDT tests of FRP structures apply the test load ina series of steps, returning to zero between each step. The appearance of Felicity ratios much lessthan unity is a good indication that significant damage has occurred.
Selected Reading List from JAE Articles
C.R. Heiple and S.H. Carpenter, Acoustic Emission Produced by Deformation of Metals and Alloys - A Review: Part I and II, J. AE, 6: 177-204, 215-237, 1987.
M.A. Hamstad, A Discussion of the Basic Understanding of the Felicity Effect in Fiber Composites, J. AE, 5: 95-102, 1986.
K. Ono, Structural Integrity Evaluation Using Acoustic Emission, J. AE, 25: 1-20, 2007.
T.J. Fowler, J.A. Blessing, P.J. Conlisk and T.L. Swanson, The MONPAC System, J. AE, 8: 1-8,
1989.
M.R. Gorman, Modal AE Analysis of Fracture and Failure in Composite Materials, and the Quality
and Life of High Pressure Composite Pressure Vessels, J. AE, 29: 1-28, 2011.
Chapter 2 Acoustic Waves in Solids
Acoustic emissions are vibrational waves generated in materials. They travel through the material and are detected by a sensor of some type. In plasmas, liquids and gases, the wave motion isusually simple and the sensor can be immersed in the material. In solids, the wave usually travels
through the material until it reaches a surface. There a sensor detects the surface motion producedby the wave. Wave motion in solids can be far from simple. Solids have boundaries and the6interaction of waves and the boundaries will depend upon the acoustic properties and the geometryof the solid. Acoustic wave motion is a cooperative movement of the atoms in a material. The
simplest type of acoustic wave occurs when a shell of material around a region is suddenly compressed by a disturbance in the region. This compression can be either negative or positive, suchas a bubble imploding or exploding. The material in this shell experiences a change in its density.
This density change is then passed to the next shell by coupling between the atoms. The density inthe first shell then rebounds, usually going past the original value of the density to a smaller densitychange in the opposite direction. This variation in the density is transmitted to the next shell andso on, throughout the material. The speed with which the disturbance propagates is determined by
the strength of the coupling between the atoms or molecules of the material and the density of thematerial. This type of wave is known as a compressional wave and occurs in all materials, solids,
liquids, gases and plasmas. As materials become more rigid and ordered, more types of waves are
possible and the introduction of boundaries in a material will introduce other complications into
the wave motion. However, all wave motion is based upon the cooperative movement of groupsof atoms. This is in contrast to thermal energy where the motion of adjoining groups of atoms is
not coupled and the energy is transferred between individual atoms or molecules. Heat transfer isa diffuse flow of motional energy through the material and involves much higher frequencies thanseen in acoustic emission.
2-1 Characteristics of Acoustic Waves:
Much of the complexity seen in AE signals is generated as the wave travels through the medium. Any understanding of AE signals requires knowledge of the characteristics of acoustic
waves. The place to start is the properties of the medium, through which the wave travels. All
materials are collections of atoms. The atoms are held together by attractive forces but also prevented from approaching each other too closely by short range repulsive forces. The superpositionof these forces results in an equilibrium position for the atom where it has its lowest energy andtherefore the most stable configuration of the material. In crystals, long range forces between theatoms result in defined locations for these equilibrium positions. The result is a crystal structure.
In a glass or a liquid, the structure is amorphous but there is an approximate distance between
atoms resulting in a uniform density. In a liquid, only the density is defined, the ability of the atoms
to change positions is measured by the viscosity of the liquid and can range from very low to veryhigh. A glass is just a liquid with extremely high viscosity. A gas can be thought of as a liquid withlow viscosity and a density, which is defined by its container. The acoustic properties of a material
depend upon the density of the material and the long-range coupling constants between the atomsof the material. The viscosity in solids causes the attenuation effect on the acoustic properties [5].
The long-range correlations in an acoustic wave result in many atoms in a small region being
displaced in the same direction from their equilibrium positions. This displacement is a local strain
in the crystal. The strain is a dynamic one as its direction and magnitude are constantly changing
as the atoms move. When the atomic motion is pseudo-oscillatory, so is the dynamic strain. An
acoustic wave is an oscillating strain moving through a material. Because stress and strain arealways directly related in a material there is also an oscillating stress field. Therefore, an acoustic
wave can be described as either a dynamic stress field or strain field in a material.
2-1-1. Wave Motion:
The most familiar depiction of a wave is a sinusoidal curve such as shown in Fig. 2a. The
amplitude oscillates between positive and negative limits at a fixed rate, known as the frequency
and the curve extends indefinitely. This curve can equally well be plotted as a function of space or7

time. In a medium, a wave has both a spatial and a time component. An equation for such a curveis
Y = A sin (ωt – kx)(2-1a)
where A is the amplitude, ω (called angular frequency) is 2π times the frequency, f, and k (calledwavenumber) is 2π over the wavelength, λ. The frequency, f, wavelength, λ, and wave velocity, v,
are related byv = λ·f.

(2-1b)

Figure 2 (a) simple sine wave, (b) sum of two sine waves, (c) transient wave, (d) spectrum oftransient wave.
The wave described in eq. (2-1a) propagates in one direction only. In three dimensions, thewave front, which is a surface of constant phase for the wave, is a plane perpendicular to the X
axis. Such a wave is known as a plane wave. Most waves originating at a point in an extended
medium initially have a spherical wave front. However, at some distance from the point of origin,
the spherical surface will approximate a plane over a small area. For simplicity, in the rest of this
discussion, plane waves will be assumed.
If two waves exist in a medium simultaneously, their amplitudes will add algebraically. Fig.
2b shows the sum of two waves
Y = A1 sin ω1t + A2 sin ω2t(2-2)

where only the time component is plotted for clarity. One can, in this fashion, represent a complex
wave form as a sum of simple waves. It has been long known that any arbitrary function, which
does not contain a discontinuity, can be represented by an infinite sum of sinusoidal curves known
as a Fourier series. One form of such a series can be written as
8

f(t) = Ao/2 +Σ An sin (nωnt + αn)(2-3)

where the An is the amplitude of the n-th sine curve, ωn its angular frequency and αn its phase.
There is no restriction that the curve or wave form represented by the Fourier series be continuous.
A transient wave such as that shown in Fig. 2c can be described by a Fourier series. One very
useful method of analyzing a wave is to look at its frequency components. A plot of the amplitudeof the frequency components, An in eq. (2-3), against the frequency f is known as the frequency
spectrum of the wave. Fig. 2d shows the frequency spectrum for the wave shown in Fig. 2c, indicating two peaks exist at 80 and 170 kHz.
3-1-2. Acoustic Media:
An acoustic wave only exists in a material, whether it is a solid, liquid, gas or plasma. Its
velocity is determined by the characteristics of the material. The stronger the force between neighboring atoms, the more closely coupled will be their motion. On the other hand, the larger the massof the atoms, the more force must be applied for the same acceleration. Because a wave is a synchronized movement of a large number of atoms, it is actually the density of the material, ρ, rather
than the mass of the individual atoms which governs wave motion. Thus, the wave velocity should
be directly proportional to the atomic restoring force between the atoms or molecules and inversely
proportional to the density. The actual relationship isvi = (Ci/ρ)0.5(2-4)

where vi is the velocity for the i-th type of wave and Ci is known as the elastic constant for thattype of wave. The elastic constant is a measure of the strength of the coupling between atoms forthat particular kind of motion. Different relative motions of the atoms will have different values ofthe elastic constant. Another property of the material is the characteristic acoustic impedance. Thisis defined as
Zi = ρvi = (ρCi)0.5.

(2-5)

The reflection and transmission of acoustic waves at an interface depend upon the characteristic acoustic impedances of the two materials. Acoustic velocities, acoustic impedances and densities for some materials often seen in AE tests are given in Table 1.
Table 1 Acoustic velocities and impedances for longitudinal, shear andRayleigh (surface) waves for several materials
Material
Aluminum
Brass
Steel
Nylon
Lucite
Water
Air

Vl
6.42
4.70
5.94
2.62
2.68
1.50
0.33

Vs
3.04
2.11
3.25
1.07
1.10
-

Vr
2.87
1.99
3.03
1.01
1.04
-

ρ
2.70
8.60
7.80
1.11
1.18
1.00
0.00123

Zl
17.30
40.60
46.50
2.86
3.16
1.50
0.0004

Zs
8.20
18.30
25.40
1.18
1.30
-

Zr
7.70
17.10
23.60
1.12
1.23
-

Velocities are in units of mm/μs, densities are in units of Mg/m3, acoustic impedances are in units of
kg/m2μs (= MPa·s/m = Mrayl).

92-2 Types of Acoustic Waves:
The path traced out by a particle under the influence of an acoustic wave can generally be
represented by an ellipse with one of its axes oriented along the direction of travel of the wave.
The type of wave is determined by the relationship between the particle motion and the directionof travel of the wave for bulk materials. For materials with boundaries, the exact details of the
wave motion will be determined by the materials properties, the geometry of the material and thefrequencies of the acoustic wave.
Waves traveling through an extended medium (one whose dimensions are much larger than
the acoustic wavelength) are called bulk waves. There are two types of pure bulk waves calledlongitudinal (compressional) and shear (transverse) waves. In both these waves, the minor axis of
the elliptical atomic paths collapses toward zero, resulting in an approximate linear oscillatory
motion. Longitudinal waves are waves where the average of this motion over a region in the material is parallel to the direction of wave propagation and shear waves have the average motion
perpendicular to the direction of propagation. (It should be noted that the motion of atoms aroundtheir equilibrium position is very complicated with much higher frequency components thanacoustic waves. The acoustic waves are averages over a very large number of atoms in the lattice.
The average acoustic motion will be ellipses around the lattice position but there is no requirementthat the axes of the ellipse correspond to symmetry directions in the lattice). These waves areillustrated in Figs. 3a and 3b. Since the relative motions of the particles in these two waves aredifferent, the elastic constants and therefore the wave velocities also differ. Generally, the shear
velocity is slightly greater than one half of the longitudinal velocity. Waves often have both shear
and longitudinal components. However, these components each travel at their own velocity. In a
non-attenuating, non-dispersive medium (v is independent of f), a transient wave, sampled at somedistance from its point of origin, may appear to be two separate waves, one longitudinal and one
shear. This is illustrated in Fig. 4. The anisotropy of the coupling forces between atoms at thesurface of a bounded solid will produce additional types of waves. A surface wave has its maximum amplitude at the surface of the solid with its amplitude decreasing with distance from thesurface. The plane of the particle motion ellipse can be either parallel (Love waves) or perpendicular (Rayleigh waves) to the surface. However, because most AE sensors detect motion perpendicular to the surface, the parallel component is seldom seen. The velocity of Rayleigh waves isslightly lower than the shear velocity. If the solid is bounded by two surfaces so that it is a plate
and the thickness of the plate is on the order of a few acoustic wavelengths or less, plate waves(Lamb waves) can occur. A plate wave is essentially two synchronized surface waves which canbe synchronized either symmetrically or anti symmetrically. Particle motions in Rayleigh waves
and plate waves are illustrated in Fig. 5.

Figure 3 Particle displacement for bulk acoustic waves, (a) compressional wave, (b) shear wave.

10Bulk waves, surface waves and plate (Lamb) waves are the most important types of waves
seen in the field of acoustic emission. However, these are not the only types of waves which canbe found in solids. In general, bounded solids of moderately symmetrical geometry can support
unique types of waves.

Figure 4 Waveform with compression and shear wave. (a) at origin, (b) some distance from origin.

Figure 5 Particle displacements for acoustic waves. (a) Rayleigh wave. (b) Plate (Lamb) wave,
first symmetric mode, So. (c) Plate (Lamb) wave, first antisymmetric mode, Ao.
2-3 Dispersion and Group Velocity:
The velocity defined in Eq. 2 is the phase velocity. For unbounded media and surface waves
on a single surface, this velocity is the only wave velocity and it is independent of frequency.
However, all waves traveling in bounded media, where the physical dimensions are within an orderof magnitude of the acoustic wavelength, are dispersive, that is, the phase velocity is a function of
frequency. This is illustrated in Fig. 6 where the frequency dependences of the velocities for symmetric and anti-symmetric plate waves are shown. Dispersion would have little effect on continuous waves. However, acoustic emissions are packets of waves, which can be thought of as a superposition of continuous waves, as shown in eq. (2-3). If each wave train, making up the packet,
travels at a different velocity, the wave packet will change shape as it travels through the medium.
The result is that the same AE signal may look quite different when detected by the same sensor
11

at different positions. Energy in a wave packet, does not travel at the phase velocity, but at thegroup velocity. The phase velocity can be defined by rewriting eq. (2-4) as
vp = ω/k(2-6)

while the group velocity is defined asvg = dω/dk.

(2-7)

In the absence of dispersion, these are the same velocity, but in most bounded solids the groupvelocity will be less than the phase velocity. This can have real effects in AE when one is attempting source location by measuring the differences of arrival times at two or more sensors. One canalso get interesting results when trying to compute the locations of AE sources by using a phasevelocity for a thin sheet of metal.

Figure 6 Phase velocities for six different plate wave modes (So, S1, S2, Ao, A1, and A2) plottedagainst the ratio of plate thickness to shear wavelength. Plotted for steel with a Poisson’s ratio of
0.28. The longitudinal, extensional, shear and Raleigh wave velocities are shown as vl, ve, vs andvr.
2-4 Attenuation:
A wave packet is generated with a well-defined energy. As the packet propagates away fromits source, the energy content will remain constant in the absence of any dissipative mechanisms.
12

However, if the wave front of the packet is expanding, the energy per unit area on the wave front
must decrease so that the total energy on the wave front is conserved. The rate of this decrease will
depend upon the geometry of the medium. In three dimensions, the energy per unit area will decrease as the square of the distance from the source while in two dimensions, it will decreaselinearly with this distance. If the packet is confined to one dimension, as in propagation down arod, the energy per unit area will be independent of the distance from the source. Normally, when
thinking about acoustic waves, one assumes a plane wave traveling in only one dimension. Therefore, this geometrical effect on the wave packets energy is ignored. However, in an acoustic emission test, where neither the location of the source nor, often, the geometry of the sample is under
the control of the investigator, this geometrical effect should be considered in any attempt to measure the energy of the generated wave packet. The attenuation of a plane wave arises from dissipative mechanisms or scattering as the wave propagates. In a homogeneous medium, these losses
usually occur as a fixed percentage of the wave packet energy per unit length of travel. Mathematically, this is an exponential decrease in the wave amplitude with distance and can be expressed as
A = Ao exp (–αx)

orA = Ao exp (–βt),(2-8)

where α is an attenuation constant per unit length and β is an attenuation constant per unit time.
The two constants are related by the acoustic velocity, β = αv. Both forms of the attenuation constant are seen in the literature. Knopoff [5] showed that α is related to the viscosity (h) of the
Kelvin-Voigt solid by
α = ω2 h/2vE’,(2-9)

in terms of angular frequency ω, the acoustic velocity v and the elastic constant E’.
There are many types of acoustic attenuation mechanisms and most have some form of frequency dependence. Fortunately, in the normal acoustic emission frequency range of 50 kHz to 1
MHz, both the frequency dependence and the magnitude of many of these attenuation mechanisms
are small in metallic structural materials. However, in composites and geological materials, theattenuation can be a severely limiting factor in AE tests, often restricting the useable frequency
range to 100 kHz and below.
2-5 Interfaces; Reflection, Transmission and Mode Conversion:
If a plane wave strikes a surface between two materials with different acoustic impedances,
part of the wave will be reflected and part transmitted. The intensities of the reflected and transmitted components are given by:
At = 4Z1Z2 /(Z1 + Z2)2(2-10a)Ar = ((Z1 – Z2)/(Z1 + Z2))2(2-10b)where the Z's are the acoustic impedances of the materials. It should be noted that these equations
are symmetrical; it does not matter which medium the wave is traveling in when it hits the interface. The differences in acoustic impedances can result in large differences in the acoustic intensities transmitted through interfaces. For example, the transmitted intensity of longitudinal waves
is 78% for a steel-aluminum, 12% for a steel-water and 0.004% for a steel-air interface.
13

When a plane wave strikes the interface, the angles of the reflected and transmitted waves aregoverned by Snell's law
sin θ1/v1 = sin θ2/v2(2-11)

where θ1 is the angle of incidence or reflection, θ2 is the angle of refraction, and the vi are thevelocities in the materials. In eq. (2-11), a transmitted velocity is positive and a reflected one,negative. The particle motion anywhere on a wave front of a plane wave is the same. It wants toremain the same even when the wave passes an interface. However, at an interface the direction of
propagation will change even though the particle motion does not. For a wave perpendicular to thesurface (θ1 = 0o), this results in a phase change of l80° in the relative motion of the particle to thewave direction. This does not change the character of the wave. For non-perpendicular angles ofincidence, the reflected and transmitted waves will have both longitudinal and shear components(unless the particle motion is parallel to the interface) because of the change in the angle between
the particle motion and the propagation direction. This is illustrated in Fig. 7. The process of generating both modes of bulk waves upon reflection or refraction is known as mode conversion. In
acoustic emission, where there is no control of the wave path, the almost inevitable result of mode
conversion is that the wave reaching the sensor is composed of both longitudinal and shear components, no matter what its original polarization was. In most situations, surface waves are alsopresent. Since mode conversion occurs at almost every reflection, it is an almost continuous process as the wave propagates in a bounded medium. Because of this continuous transformation
between modes traveling at different wave velocities, the transient waveform will lengthen in time
as it travels instead of dividing into separate longitudinal and shear components as shown in Fig.
4 earlier.

Figure 7 Reflected and transmitted waves across an interface. The incident wave is a longitudinal
wave with an angle of incidence of 45o. The double arrows show the direction of particle motion
associated with each wave.
142-6 Resonance:
The phenomenon of resonance occurs when there are reflected waves and regular geometries.
For example, place a plate of a material between two other materials such as a steel plate submerged in water. When an acoustic wave is directed at it, there will be transmitted and reflected
waves at each interface. Figure 8a shows how the waves will bounce back and forth in the plate.
The number of reflections will depend both on the acoustic impedances of the steel and water andupon the attenuation in the steel. If the plate thickness is one half of a wavelength, as shown in
Fig. 8b, each reflected wave will be in phase and the strains will algebraically add. If there are agreat many reflections, the peak strain can reach a very high level. If there are only a few reflections, the frequency of this wave need not be exactly that of a half wavelength to get some reenforcement but the greater the number of reflections, the narrower will be the allowed frequency
range at maximum strain and the larger this maximum strain will be. This is illustrated in Fig. 8c.
The sharp high amplitude peak is said to have a high Q where Q is related as the ratio of energy
stored per cycle to the energy dissipated per cycle. This increase of the stress or strain level in amaterial at a half-λ thickness is known as a resonance and fo is the resonant frequency. From Fig.
8b, we see that at resonance, the average strain throughout the plate is a maximum. Resonances
can occur anywhere that a regular geometry allows acoustic waves to reflect in such a way that thestrains from several reflections of the wave are superimposed. In piezoelectric materials, such as
quartz and LiNO3, very high Q resonances allow precise generation of single frequency waves.

Figure 8 Reflected and transmitted waves inside a plate immersed in water. (a) Successive reflections have been displaced for clarity. (b) Strain in a plate one half wavelength thick at f = fo. (c)amplitude as a function of frequency in the plate for high and low Q materials. (d) Strain in theplate of (b) at f = 2fo and f = 3fo.
Selected Reading List from J. AE and Open Access Paper
W. H. Prosser, M. R. Gorman and D. H. Humes, Acoustic Emission Signals in Thin Plates Produced by Impact Damage, J. AE, 1999, 17, 29-36.
K. Ono, A Comprehensive Report on Ultrasonic Attenuation of Engineering Materials, Including
Metals, Ceramics, Polymers, Fiber-Reinforced Composites, Wood, and Rocks, Appl. Sci. 2020,
10, 2230; doi:10.3390/app10072230. Values of vl and α of 300+ materials are tabulated.

15

Chapter 3 Detection of Acoustic Emission
3-1 Sensors:
A sensor is a device, which generates an electrical signal when it is stimulated by an acoustic
wave. The exact relationship between the characteristics of the wave and those of the signal will
depend upon both the sensor and the wave. An ideal sensor would produce a voltage-time curve
identical to the amplitude-time curve of the wave at the point where the sensor is located. No
sensor approaches this ideal although for certain types of acoustic waves, laser interferometry will
come close. Many available sensors operate quite well for specified types of waves over limited
ranges of parameters. Because of the wide range of frequencies and different acoustic modes contained in most AE signals, the choice of a sensor is usually not critical in detecting AE. However,
the optimal choice of a sensor will always improve the data and may be the difference between a
successful or unsuccessful test or experiment where low amplitude emissions are involved. AE
sensors can be based upon several physical principles. The signals can be generated by electromagnetic devices such as phonograph pickups, by capacitive microphones, by magnetostrictive
devices, by piezoelectric devices and by the use of laser interferometers to detect the surface displacement of the sample. For use in NDT applications, most of AE sensors are piezoelectric andthe rest of this discussion will be limited to such devices.
3-2 Piezoelectricity:
Piezoelectricity [6] is the name given to the coupling between strain and electric polarization,
which occurs in many crystals. It is a geometrical effect and occurs only in materials, which lacka center of crystal symmetry. This is not a severe restriction as 2l of the 32 classes of crystal
structure lack a center of symmetry. In such crystals, a strain will shift the centers of positive andnegative charge distribution so that they no longer coincide. This produces an electric dipole moment throughout the crystal. The polarization of a crystal is defined as the dipole moment per unit
volume. When a polarization exists in a non-conducting crystal, electric charges will appear on
certain surfaces. Conducting electrodes on these surfaces allow the measurement of the charge,
which is directly proportional to the strain. The effect is symmetric in that application of charges
to the electrodes changes the strain. Since the stress field and the strain field in a material are
directly related, the piezoelectric effect can equally be defined as the coupling between the stressand the polarization in a crystal. The exact stress or strain, which is measured (or generated), canbe chosen by a careful selection of the crystal surfaces to deposit electrodes (usually done by vacuum deposition). A characteristic, which is highly desirable in a measurement, is linearity. Piezoelectricity more than meets this requirement. A single crystal sensor appears linear over more thannine orders of magnitude.
Initially, all piezoelectric devices were made from single crystals. The most useful of thesewere quartz, Rochelle salt and ammonium dihydrogen phosphate. Later, a class of materials known
as ferroelectrics (piezoelectric materials, which have a polarization even in the absence of a strain)
were investigated and methods were discovered, in which ceramics made of ferroelectrics, could
be given a uniform direction of polarization similar to that found in a piezoelectric crystal. It became possible to produce ferroelectric ceramics with many properties superior to piezoelectric
single crystals. The result has been that almost all AE sensors are made from ferroelectric ceramics
of various types. The most common one is lead zirconate-titanate, often referred to as PZT.

16

3-3 Size Effects:
An infinitesimal piece of piezoelectric material with many different sets of electrodes totally
imbedded in a sample would come close to the ideal sensor. However, when we scale up the piezoelectric to manageable size, put on one set of electrodes and place it on the outside surface ofthe sample, we rapidly depart from that ideal. The physical size of the sensor leads to two main
effects. The first is resonance and the second is strain averaging. Both can become important when
the physical dimensions of the sensor approach or exceed the wavelength of the acoustic wave.
Since the output of a piezoelectric crystal is proportional to the strain, and to the average strain fora crystal of finite dimensions, the maximum output of a sensor occurs at its resonant frequencies.
The fundamental resonate frequency of a plate occurs when the plate thickness is one-half wavelength (λ/2) as shown in Fig. 8b. The reflected wave is in phase with the incident wave at thesurface and the strains add. If the frequency is increased until there is one full wavelength in the
crystal there will again be strain re-enforcement due to the reflected waves. However, we can see
in Fig. 8d that while the strain level may be very great at this frequency, 2fo, the average strainover the crystal exactly cancels so that the output of the sensor is zero. Increasing the frequency to
3fo, we see 1.5 λ in the crystal, re-enforcement again occurring, and the average strain over twothirds of the crystal cancelling but the average strain over that last third being a maximum. The
result is that a piezoelectric sensor will have a maximum output whenever the thickness, d, is
d = (2n - l)·λ/2(3-1)

and no output whend = n·λ(3-2)

Thus, a sensor can be operated either at its fundamental frequency, fo, or its odd harmonic frequencies nfo, n being odd. The Q of the transducer depends only on the number of reflections in thesensor and therefore the Q is independent of the harmonic at which the sensor is operating as longas the material of the sensor does not show a frequency dependent attenuation. The sensor will
always have an output at frequencies below the fundamental frequency, fo. At frequencies less thanabout 3fo/4, the resonance will have no effect and the output will be essentially independent of
frequency. In reality a material cannot be strained in one dimension without producing strains inother directions as shown in Fig. 9a. Many AE sensors use this to get a maximum response. The
piezoelectric element is in the shape of a squat cylinder (a round disk). A wave with a verticaldisplacement on the cylinder face will excite the radial resonance. This resonance will then give alarge output signal. This type of sensor is a very sensitive detector of acoustic emission. However,it should not be used to measure the frequency spectrum nor the actual wave form of the acoustic
wave since this cross coupling of vibration modes can give a distorted representation of the wave.
Piezoelectricity is quite complex and a large part of its history in acoustics has been a searchfor particular crystal orientations where only certain strains will generate charge on a pair of electrodes. Modern AE sensors, made of polarized ferroelectric ceramics, also suffer from multiple
resonances. However, because the ceramics have an intrinsically low Q neither the peaks nor valleys are especially sharp. Thus, most sensors have a broad but highly "colored" spectral response
curve as shown in Fig..9b.

17

Figure 9 (a) Deformation of a material showing multiaxial strain resulting from uniaxial force.
(b) spectral response of an AE sensor.
There is another important aspect of strain averaging by a sensor. This is known as the apertureeffect [10a]. Fig. 10a shows a block with a sensor mounted on it. If the sensor is excited with acompressional wave moving perpendicular to its surface, the entire sensor face will move in phase
and, excluding resonances, the average strain in the sensor will be independent of frequency. Next
look at a Rayleigh wave which is traveling parallel to the sensor face and has the particle motion
perpendicular to the sensor face. In this case, the strain distribution in the transducer will vary as
a function of distance along the wave. Fig. 10b shows the strain variation where the diameter ofthe sensor is less than λ/2. Here, the output is still proportional to the amplitude of the wave. In
Fig. 10c, we see the case where the diameter of the sensor is larger than the wavelength. In thiscase, for every complete wave-length under the sensor, the strain averages to zero. Only the extra
fraction of the wavelength under the sensor contributes to its output. This averaging essentiallyreduces the effective area of the sensor and the higher the frequency, the greater the reduction.
Another effect is that at certain frequencies, dependent upon the shape of the sensor and the acoustic velocity of the sample, the total strain averages to zero. These effects can be expressed in termsof the Bessel function [10a] and are illustrated in Fig. 10d where the response for this type of
surface wave is plotted for a sensor with a flat response to compressional waves perpendicular toits face. It is obvious that the high frequency response of such a sensor is going to depend drastically upon the angle of incidence, with which the wave strikes the sensor. This averaging effectdepends upon the acoustic wave-length in the material. Therefore, the sensor response not only isgoing to vary with frequency and angle of incidence, but it is going to vary when used with different materials. The best answer to this problem is to make the sensor physically small. For steel, a3-mm diameter sensor should work reasonably well below 500 kHz. The inevitable tradeoff is thatthe smaller sensor has a lower capacitance and thus, as will be discussed later, a reduced effective
sensitivity. Another approach has a more complicated polarization geometry than a simple disk
polarized between its faces. Some sensors of this type are commercially available and the reader
should contact the various manufacturers for details and specifications (the geometry and materials
in such sensors are often treated as trade secrets).

18

Figure 10 (a) block with sensor mounted, compressional wave hits perpendicular to sensor face
and Rayleigh wave travels parallel to face. (b) Strain in on blocks surface from Rayleigh wave
with wavelength much longer than sensor diameter. (c) Strain on surface from Rayleigh wavelength shorter than sensor diameter. (d) Sensor output as a function of frequency of Rayleigh waves
with equal amplitudes [10a].
3-4 Couplants:
To this point, it has been assumed that the sensor has simply been placed on the surface of thematerial containing the acoustic wave. When this is tried, you find that the sensor produces no ora very weak signal. If a thin layer of a fluid is placed between the sensor and the surface, a muchlarger signal is obtained. The use of some type of couplant is essential for the detection of lowlevel AE signals. Physically, this can be explained by looking at the acoustic wave as a pressure
wave transmitted across two surfaces in contact. On a microscopic scale, the surfaces of the sensor
and the material are quite rough so that only a few spots actually touch when they are in contact.
Stress is force per unit area and the actual area transmitting a force is very small. If the microscopic
gaps are filled with a fluid, the pressure will be uniformly transferred between the surfaces. For ashear wave with a variable strain component parallel to the surfaces, again very little strain will betransferred between the surfaces because of the few points in actual contact. In this case, filling
the gaps with a low viscosity liquid will not help much since such a liquid will not support a shear
stress. However, a high viscosity liquid or a solid will help transmit the parallel strain between
surfaces. The purpose of a couplant is to insure good contact between two surfaces on a microscopic level.
Much is written about couplants for both ultrasonics and AE and much of this is either wrong
or misleading. One basic problem is in using the terms bond and couplant interchangeably, a practice of which many people, including the author, have been guilty. Strictly, a couplant is any material, which aids the transmittal of acoustic waves between two surfaces, while a bond is a couplant, which physically holds the sensor to the surface. Water is a couplant and cured epoxy resin
is a bond. Many problems have come about from using a bond in an inapplicable way. If a rigid
bond is used to attach a sensor to a sample, which elastically deforms during the test, the normal
result is a broken bond and poor or no sensitivity to the acoustic wave. Similarly, in an experiment
where the temperature is changed appreciably, the use of a rigid bonding material can lead to19

broken bonds due to differential thermal expansion between the sensor and the sample. Bonding
agents, then, must be chosen with great care, and the primary emphasis put upon the compatibilityof the materials under the test conditions. Usually, if the bond will hold the sensor on, it will be anadequate couplant. For a compressional wave, any fluid will act as a couplant. A highly viscous
fluid will transfer some shear stress across the boundary, which may or may not be an advantage.
The author tested a large number of couplants with compressional waves [7]. Almost all couplants
showed an increase in the signal strength over no couplant of 30 ± 2 dB. The variation was little
more than the uncertainty of the measurement. The most practical rule is to use as a couplant, athin layer of any viscous fluid, which wets both surfaces. The sensor should be held against thesurface with some pressure furnished by magnets, springs, tape, rubber bands, etc. The secret is touse as thin a layer as possible. If a rigid bond is used, there must be minimal differential expansion
between the two surfaces.
A flexible bond can be used. Over the years, the author has had excellent results with GE
Silicone II sealant. It is available as a clear household glue, which will set up within about 12 hours
when used between a 25-mm diameter sensor and a metal or plastic surface. In one test, a perpendicular sinusoidal acceleration of about 100 G produced no bond failures using this glue. It is quite
flexible and a sensor can be removed from a surface by sliding a knife blade or wood chisel between the sensor and the surface. In Table 2, a few commonly used couplants are listed along withthe temperature range where they can be used [6,7].
Table 2 Some common acoustic emission couplants and the approximate temperature range
where they can be used.
Couplant
Dow Corning V-9 Resin
High Vacuum Stop Cock Grease
Ultrasonic Couplants
Petroleum Gel
Dow Corning 200 Fluid
Dental Cement
50 % Indium – 50 % Galium mixture
GE Silicone IIApproximate Temperature Range~ -40o C to 100o C~ -40o C to 200o C
Room Temperature
Room Temperature-273o C to -70o C and -30o C to 200o C~ 0o C to ~50o C~20o C to 700o C
5o C to 200o C

3-5 Temperature Effects:
The temperature dependence of piezoelectricity is complicated and a detailed discussion of its
effect on sensors is beyond the scope of this article [5]. However, there are certain effects which
can lead to problems when a sensor is used at different temperatures. First, ferroelectric materials[8], such as the PZT ceramics, have a temperature (the Curie temperature), above which the material transforms to another, and usually non-ferroelectric, phase. Taking a ferroelectric sensor
through the Curie temperature will remove the polarization, destroying the piezoelectric propertiesof the sensor, and may shatter the ceramic as well. Ferroelectric sensors will usually work well up
to temperatures within 50°C of the Curie temperature, if the other materials in the sensor can standthe temperature. The Curie temperatures of PZT ceramics lie between 300 and 400°C.
Ferroelectric ceramics are polycrystalline. Each crystallite may have one or more ferroelectricdomains in it. These domains are regions where the spontaneous polarization is all in one direction.
This polarization can only be along certain directions in the crystal structure. When the ceramic is20

polled, these domains are aligned as closely as the crystal orientation allows to the direction ofpolarization. Because of the random orientation of the crystallites, there are always going to be afair number where there may be several possible orientations of the domain, which are almost
equally close to the direction of polarization in the ceramic. Small strains may be enough to cause
the domain to change orientation. Such a flip of a domain may cause a very small change in thepolarization of the sensor. However, this change is the same order of magnitude as the change
caused by a small acoustic wave. In general, it is impossible to distinguish an electric signal caused
by a flipped domain from one caused by AE. The strain necessary to produce a domain flip cancome from changing the temperature of a sensor. Thus, changing the temperature of a ceramic
sensor can produce signals, indistinguishable from AE, which arise in the sensor and not the sample. Empirically, the temperature change necessary to produce appreciable amounts of these domain flip signals is around 100°C in PZT. This effect does not prevent ferroelectric ceramic sensors
from being used at different temperatures but thermal equilibrium should be achieved before data
is taken. If one wishes to measure AE while changing temperature, it is recommended that ferroelectric ceramics only be used for temperature deviations of less than 100˚C. To measure AE overlarger temperature deviations, a sensor made of a single crystal, such as quartz or lithium niobate
should be used.
3-6 Sensor Sensitivity - Effects of Cables:
The sensitivity of a sensor is governed by the intrinsic sensitivity of the piezoelectric material,
the dimensions of the piezoelectric element and the design and materials used in its case. Practically, one receives from a manufacturer a measured response curve to a standard signal and thecapacitance of the sensor. This curve is often presented as if it were independent of the measurement technique. However, the sensitivity of a sensor will always depend, in part, upon the equipment with which it is used. The open circuit voltage produced by a sensor is a property of thepiezoelectric element and isVo(S) = Q(S)/Co,(3-3)

where Q is the charge produced by a strain S and Co is the capacitance of the sensor. When connected to a preamplifier, the actual voltage across the input resistor of the preamplifier (if the input
resistance is large) is
V(S) = Q(S)/(Co + Cc + CI)(3-4)

where Cc is the cable capacitance and CI is the input capacitance of the preamplifier. AE sensors
usually have a capacitance that falls between l00 and 1500 pF. Preamplifier input capacitances
range between 20 and 40 pF. The capacitance per unit length of coaxial cables depends upon thecables' impedance with approximate values of 100 pF/m for 50-ohm cable, 66 pF/m for 75-ohm
cable and 50 pF/m for 95-ohm cable. To see the range of this reduction in sensitivity assume an
input capacitance of 30 pF and 1.5 m of 50-ohm cable. For a sensor with a capacitance of 100 pF,
the output voltage would be V = 0.356Vo or a decrease in sensitivity of 9 dB. A sensor of l600 pF
capacitance would have V = 0.9 Vo, a decrease in sensitivity of l dB. Thus, the loss in sensitivity
can be appreciable for low capacitance sensors. Since most of the extra capacitance comes fromthe cable, it is a good practice to keep the length of the cable between the sensor and preamplifier
as short as possible. This is also a problem for small sensors as the capacitance is directly proportional to the area of the disk.
21

Presently, most sensor manufacturers provide sensors with integral preamplifiers to prevent
this cable effect. However, electronic components have a lower temperature tolerance than piezoelectric ceramics. Typical allowable temperature is from –50 to 100˚C.
3-7 Sensor Sensitivity - Effect of Preamplifier Noise:

Figure 11 Frequency response of sensor-preamplifier combination. The top is the signal response
and the rest are no signal responses with the preamp input circuit open, sensor only or 50-ohm load
only.
The spectral response curve of a sensor-cable-preamplifier combination to an AE signal isshown in Fig. 11. Also shown are three response curves of the preamplifier without an AE signal.
The exact shape of the spectral response of a sensor to an AE signal will depend to some extent on
the preamplifier input characteristics and on the cable capacitance. Too much cable capacitancewill tend to short out the high frequencies relative to the low frequencies. Different preamplifiers
will give different response curves even with the same cable-sensor combination, although thedifference is small between most commercial acoustic emission preamplifiers. The use of preamplifiers not designed for AE can have an appreciable effect on a sensor's spectral response characteristic as the input impedance can be as low as 50 Ω.
The three preamplifier spectral response curves in Fig. 11 are, from the top down, the open
circuit response, the response with cable and sensor in the absence of an AE signal and the responseof the preamplifier with the input shorted with a 50-Ω resistor. The noise in a shorted preamplifieris generated by current fluctuations in the first amplification device. If the input is not shorted, thenoise can be regarded as being generated by current fluctuations in the input resistor. The rootmean-square (rms) noise voltage for such a resistor is given by [9]Vn2 = 4kTRΔf

(3-5)
22

where k is the Boltzmann’s constant, T is the absolute temperature, R is the resistance and Δf isthe bandwidth of the preamplifier. If the input stage of the preamplifier is open, R is the input
resistance of that stage but if there is a sensor connected, R is some equivalent resistance. The
capacitance of the sensor and cable will tend to decrease the higher noise frequencies. The peaksin the sensor curve in Fig. 11 are caused by the mechanical resonances of the sensor raising theimpedance of the sensor cable combination at the resonant frequencies. The noise level of a preamplifier is commonly specified by the manufacturers as the output rms voltage, Vo, divided by
the gain, G, when the input is shorted.
Vs = Vo/G.

(3-6)

The noise contributed by the sensor-cable combination can be estimated from eq. (3-5). A
value of 295 K should be used as the temperature and the output impedance of the sensor-cablecombination used as the equivalent resistance, R. This output impedance can easily be measured
by a simple circuit. A variable resistance in parallel with the preamplifier input is reduced until the
preamplifier output is one-half the level with no parallel resistor. The output impedance of thesensor cable combination is then equal to the input impedance of the preamplifier, which is essentially the value of the parallel resistor network formed by the preamplifier input resistance and thevalue of the variable resistor. For most AE sensors this impedance will be between 50 and 1000
Ω. Equation (3.5) can be rewritten
Vn = 0.004 (ZoΔf)0.5(3-7)

where Zo, the sensor impedance, is in kΩ and Δf is in Hz. The approximate noise voltage of thepreamplifier-sensor-cable combination will be
Vnoise = (Vs2 + Vn2)0.5.

(3-8)

This noise voltage is measured by an rms voltmeter, which measured heating power of random
signal. It assumes that the noise voltage is flat over the bandwidth, Δf. In Fig. 11, we saw that thisis not strictly the case and that the noise level at certain frequencies may be 6 to 8 dB higher than
the rest of the bandwidth. As long as the AE signals contain a large range of frequencies, thisshould not matter but if they are confined to one narrow frequency band and are at relatively low
level, the noise peaks may present problems.
3-8 Sensor Calibration:
All manufacturers furnish calibration curves with their sensors. The currently accepted units
for Rayleigh waves are dB referenced to l volt per meter per second. Many manufacturers will also
furnish a calibration to normally incident compression waves with units of dB referenced to l volt
per μbar. The acoustic signal used to calibrate the sensors is either a surface wave or a compression
wave. A typical calibration curve for the compression waves is shown in Fig. 11. Calibration
curves for the same transducer will not be the same for calibrations in Rayleigh velocity (meters/sec) or in pressure (μbar) because these were measured for different incident wave modes. For
Rayleigh waves, the wave motion is parallel to the surface, while compression waves move perpendicular to the surface. Another reason is that pressure is proportional to strain, which is related
to the surface displacement while velocity is related to the time derivative of surface displacement.
For a constant frequency, the relationships for a given wave mode are23

Vdisplacement ≈ D sin ωt(3-9)

Vvelocity ≈ d/dt (D sin ωt) = D ω cos ωt(3-10)

where D is the surface displacement and V is the surface velocity. For the compressional wave
mode, for example, the velocity calibration curve is approximately the pressure calibration curve
multiplied by the frequency. This tends to make the velocity curve seem flatter since it does not
fall off as fast at higher frequencies. In selecting a sensor, one should have some idea of both thefrequency range and the type of waves to be expected. Calibration curves can then be compared
provided that they are for the same type of source and the same units.
The National Institute of Standards and Technology (NIST) [10] developed a sensor calibration
for Rayleigh waves. This involves a point displacement in the center of a large steel block to produce the wave. The sensor is placed close enough to the source (100 mm) to allow a differentiation
between the direct wave and reflected waves from the edge or the bottom of the block. The outputof the sensor is digitized and the frequency spectrum of the received wave is calculated. This spectrum is compared to that of a reference sensor placed at a comparable position on the block. The
reference sensor is a capacitive sensor, which is sensitive only to the average displacement of theblock surface produced by the Rayleigh wave. This calibration is valid only for waves travelingon the surface of the block with a displacement perpendicular to the surface of the block. These
are the most common waves seen in AE testing. Furthermore, the calibration is strictly valid only
for steel or other materials with the similar acoustic impedances to steel. However, the NIST calibrations do give a valid comparison of the characteristics of different sensors. For the testing ofmost structures, the frequency and sensitivity characteristics of the sensors given by the NIST
calibrations are more than adequate. However, any attempt to determine AE source characteristics
from waveforms detected by routine AE sensors should include a far deeper study into the relationship between acoustic waves and piezoelectric sensors than is given here. Current studies [11]indicate that the shape of the wave front, the angle of incidence of the displacement to the surfaceof the sensor and even the construction details of the sensor can affect the output waveforms of
the sensor.
Selected Reading List from J. AE Papers
K. Ono. Through-Transmission Characteristics of AE Sensor Couplants, J. AE, 2017, 34: 1-11,
2017.
K. Ono, Receiving Sensitivities of Acoustic Emission Sensors: A data compilation, J. AE, 2019,36, 1-8.

24

Chapter 4 Instrumentation
4-1 Acoustic Emission Systems:
Acoustic emission instrumentation has changed drastically in the last sixty years. The basic
detector of acoustic waves is still a piezoelectric sensor and a preamplifier. Analysis instrumentation was initially an amplifier, an oscilloscope and an electronic counter. Computerized systems
were then developed where signal parameters such as signal arrival time, rise time, peak amplitude,
duration etc. were measured with analog circuits. These allowed the calculation of the locations of
AE sources as well as more sophisticated displays and methods of analysis. However, they werelimited by both the relatively slow processing speeds and small memories of the early computers
and the need for separate analog circuits for the detection of each desired AE parameter. Current
computer systems are much faster and have far greater memory capacity. Instead of analog circuits
for the AE signal parameters, they directly digitize the signals from the preamplifiers. This allows
the use of any possible signal parameters in the analysis, for which one can write a code. A commercial system may list twenty or more possible signal characteristics. In practice, however, theanalysis of AE signals is often confined to using digitized versions of the old analog parameters.
Besides speed and versatility, the new computer technology has resulted in much smaller AE
systems. Twenty-four channel systems, which started out requiring at least a full instrumentation
rack, now consist of three eight channel PCIe cards and a notebook computer. Eight channels ona single card is currently a reasonable limit. This limit is partly determined by the number of coaxial connectors that one can fit on the input of a card and partly by the amount of circuitry stillrequired for each channel. At present, most systems still require coaxial cables between the circuitboards and the sensor-preamplifier combinations. There are some systems, which use rf broadcast
connections to the sensors, but these are not yet common. Therefore, in the rest of this paper, anotebook computer with a separate box containing multi-channel digital cards and coaxial cables
to the sensors is assumed. Eight channel cards are not quite as versatile as four or two channelcards – limitations being a slower maximum digitization rate and shorter maximum lengths for
digitized signals. However, these superior capabilities of the smaller cards are more important inlaboratory experiments than in field tests.
There have been a large number of commercial AE systems manufactured. Systems, which
digitize the AE signals have been available since the mid-1990s. Three major manufactures sellingin the United States have been Physical Acoustics Corporation, Vallen Systeme and Digital Wave
Corporation. Other systems have been produced in many countries, Japan, China, Russia, Englandand Israel, to name a few. All of these systems perform the same basic functions although they
may use different nomenclature and methods of analysis. This paper will be based upon the operation of Physical Acoustic Corporation (PAC) systems [12], with which the author is most familiar.
It is hoped that the ideas expressed here will be fairly easy to apply to other systems.
4-2 System Operation:
The basic measurement in a multi-channel AE system is the system time. This starts when thetest starts and stops when it ends. It uses a 4.0 MHz clock in the PAC systems giving a time
resolution of 0.25 µs. Every time a sensor detects a signal, the exact time of arrival in test time isrecorded. Every channel of the system is independent, thus if a strong electromagnetic noise signal
hit the system, every channel could record the same arrival time. Each channel also has its own
clock, which starts when the trigger threshold of that channel is first crossed. The channel clock25

runs until the programmed maximum signal length is reached. This is used to determine the signal
rise time and the signal length. Each channel has a set PDT (peak definition time). When the signal
amplitude reaches a maximum and starts to decline, the card notes the time and maximum amplitude, then waits an additional PDT to see if the previous signal amplitude is exceeded. If it is, thePDT clock is reset and the measurement continues. If there is no higher amplitude signal within
the PDT, the previous amplitude is the peak amplitude and the time from the signal start is definedas the signal rise time. As the signal continues, the card always records the time of the last thresholdcrossing. If there is no further crossing with in the HDT (hit definition time), the last recorded time
defines the signal length. After the HDT has been reached, the card waits until the HLT (hit lockouttime) expires. At this point, the card is reset and ready to acquire the next signal. The HLT time
prevents the channel from treating a reflection of the first signal as a new emission. However, an
excessively long HLT time can prevent the capture of rapidly occurring emissions. In most AE
tests, the exact values of these three times are not important, but the values of the settings should
be considered, especially if a high average repetition rate with short emissions is expected. Simultaneously with these time measurements, the card is also digitizing the signal and storing it, if
waveforms are desired.
Both the time measurements and the digitization of the emission waveform start when a preset
threshold is exceeded. The digitization runs until a preset number of samples is exceeded. The
digitizer is actually running continuously with the samples sweeping through the memory. Whenthe card is triggered, the card stores the number of words set for the record length but it starts therecord some time (say 50 to 200 µs) before the trigger occurred. In this manner one has a recordof the waveform including 50+ µs before the original trigger time. Thus, the actual start of the
waveform can be examined, if necessary, to determine how accurately the first threshold crossing
defines the leading edge of the signal. The signal length is defined by the last time that the signal
crosses the threshold. However, signals are often longer than the set digitized word length. Therefore, the signal length is usually measured from the system clock, not from the digital record. The
rise time and peak amplitude can just as well be measured from the digitized signal as from thesystem clock.
The shift from analog to digital AE systems was made possible by advances in signal digitizers.
When the first computerized AE systems were designed the available signal digitizers ran at speeds
up to one MHz and had eight bit words. With a sign bit, an 8-bit word gives a precision of one partin 128. This gives a dynamic range of 42 dB, a little over two orders of magnitude. Modern systems
have signal digitizers, which run up to 40 MHz and have 16-bit words, which, at one part in 32,768,
gives them a dynamic range of 90 dB or four and a third orders of magnitude. This performance ismore than adequate for the usual AE test. Most engineering materials have relatively high acoustic
attenuations at frequencies above 1 MHz. The acoustic attenuation of plastics and composites restricts useable emissions to below 500 kHz. The dynamic range of AE signals from structural tests
is usually somewhat less than 80 dB between the background noise level and the largest signal
detected. The current digitizers thus have a satisfactory performance for most AE testing. One
limiting factor is the number of words in a digital signal record. The record length is chosen when
designing the test, subject to a hardware limit, which is usually between 4,000 and 16,000 words.
For high digitization rates and large signals, the digital signal is often truncated before it drops into
the background noise. This is not usually a major problem as most of the energy in the transient
signals occurs at the beginning of the record with the latter part of the transient consisting of a low
amplitude exponential decay into the background noise.

26

The limitation on the digitation rate is set by the frequency of the AE signals emitted by the
specimen. The frequency content of the signals is usually determined by the materials used in thespecimen. The upper frequency of an AE event will be governed by the size of the region involved
and the speed with which the movement occurs. On a microscopic scale, this can lead to quite highfrequencies. However, in tests involving structural materials, the detected frequencies will dependon the attenuation characteristics of the materials involved. For metals such as aluminum, steel
and titanium, frequencies up to 1 MHz are often detected. For lead and copper, maximum detected
frequencies will be no more than 500 kHz and for plastics and composites, the maximum detected
frequencies will be about 200 kHz. This does not mean that higher frequencies will not be present,
only that they will be minor components of the larger amplitude signals. This combined with the
Nyquist criteria (which states that the digital record must have at least two samples per wavelengthof the highest frequency recorded to avoid inherent distortion) suggests that sampling rates of 2.0
MHz for plastics and composites and 10 MHz for metals are as high as is needed. The penalty forsignals containing frequencies higher than half the Nyquist frequency is that these higher frequency components are aliased, folded back, into the lower frequency range. In the worst cases,
this can result in gross distortions of the original signal. If a lower sampling frequency is used, thelow-pass filters in the system should be set for considerably less than the Nyquist frequency.
It was mentioned that several AE parameters were described and used from early days of AE,
when signal shapes were confined to the face of an oscilloscope. To facilitate the discussions of
AE testing in this paper several of the terms and parameters are listed with definitions and discussions of their usefulness.
An AE hit is defined as a transient signal from a sensor which rises above the detection threshold received at an AE channel.
Its arrival time is the time when the hit signal first exceeds the detection threshold.
The detection threshold (Vt) is set during the hardware set up. It is the value (in dB above 1.0µV) at which that channel will detect a signal and trigger the signal digitizer. Normally all channels
in the test have the same detection threshold but if during a test a channel develops a noise problem,
it can be raised so that the channel only sees larger signals. The detection threshold is limited by
the background noise in the system. If there is no acoustic noise in the specimen, the limit will be,
at best, the noise level in the preamplifier. From equation (3-5), it is seen that the noise signal in
the input of the preamplifier is proportional to the square root of the input impedance, the absolutetemperature and the band width. For AE preamplifiers and sensors, this noise voltage will vary
between, very roughly, 5 and 15 µV. This would suggest that for a quiet specimen, a detection
threshold of 30 dB (32 μV) would be reasonable. (The AE Halon Bottle tester uses a 25-dB threshold with a narrow bandwidth and the bottle is enclosed in a metal oven, which helps reduce external
rf noise)
An AE event group is defined as a number of hits from different sensors, which occur withina preset time window. Every hit is checked to see if it is in an event group or starts one. This timeis set in the hardware during set up and determined by the test engineer. An example is a multichannel test of a sphere. To prevent several hits from the same channel, the time window is set atthe time an AE signal with the shear velocity (the slowest wave) takes to travel half way aroundthe sphere. Any signal that occurs after this time, measured from the first hit in the group, will not
be included in the group.
An AE located event is defined as an event group, for which the system program can calculate
a location on the specimen from the arrival times in the event group.
A cluster is a group of located events, which occur in a defined region in the specimen. The
area or volume of the cluster can either be defined in the post-test analysis or defined in the system
program, which allows the system to identify the cluster in real time. The AEWin software can be27set to identify clusters of different sizes and monitor them as to size or count content but not absolute energy content.
Figure 12 shows a typical AE signal with several defined signal parameters. Obvious are:the signal duration (ts), which is defined as the time between the first and last threshold crossing,
the signal peak amplitude (marked A+), the largest amplitude of the signal and is usually
given in dB in reference to 1 µV,
the signal rise time(t+), which is defined as the time between the first crossing and the time ofthe signal peak amplitude.

Figure 12 AE signal showing the AE count (the number of crossings of the trigger threshold, n =20), the risetime τ+, the peak amplitude A+ and the signal duration τs.
For long signals, such as a large impact on a piece of aluminum, the time it takes for the signal
to damp down to below the detection threshold is far longer than the longest digital record available
can hold. A very short signal of <50 μs indicates that the signal is probably an electrical noise
spike and not an AE signal while a signal length greater than 10 ms suggests either a frictional
origin or a very low attenuation for the AE signal. The peak amplitudes are a good indicator of
how strong the emissions are. Peak amplitudes below 40 dB are very weak for most tests and peak
amplitudes above 70 dB often are indications of some damage in the specimen. From Fig. 4b whichshows the time difference between peaks of an ideal signal containing both compressional andshear waves, it would appear possible to determine how far a signal had travelled from the signal
rise time. Unfortunately, real AE signals are seldom that simple. The rise time parameter does haveanother use. If a channel is re-armed slightly after a signal has reached it, the resulting waveform
is usually a decaying wave. In such cases, the rise time it often registers is the time from when the28threshold is crossed to the first oscillatory peak. For normal signals the rise time will be several
wavelengths long. Therefore, if the rise time is short, less than 10 to 15 µs, the channeled triggeredin the middle of a signal and the registered arrival time for that channel is probably wrong. All thedata from that signal can be ignored unless it is a very large amplitude signal. If a signal has a very
long rise time, especially greater than half the signal length, it is almost certainly not a transientand such an AE signal can also be ignored. The signal rise time is the main indicator, which can
indicate whether the received signal is actually from a transient event in the sample or it is distorted
or extraneous.
The AE count (the number of dots in Fig. 12, n = 20) was one of the first parameters used in
the AE field. It is simply the output of an electronic counter applied to an AE signal. Every time
the signal crosses a preset threshold in one direction (decreasing or increasing) a count is registered. If this count is plotted continuously, it gives a history of the emission from the specimen. If
a count per unit time is plotted, one has a history of the rate of emission. It is one of the simplest
and most useful measures of AE. It is also almost impossible to directly relate it to any physicalparameter. The AE count depends upon the detection threshold, characteristics of the sensor, thegain and bandwidth of the preamplifier, the quality of the acoustic coupling and the characteristics
and geometry of the specimen. The one thing which can be said for the AE count from an AE
signal is that if the signal has only a single count, it is probably electronic noise. An AE signal
almost always contains several oscillations.
The signal energy is easy to define but hard to measure with an analog circuit. It can be definedas the integral of the voltage squared vs time curve over the signal length. The problem with ananalog measurement is the dynamic range of the signals. If an AE test has a dynamic range of 80
dB, which is relatively common, then the energy range of those signals will cover at least eightorders of magnitude or 10 µV to 1 kV. Analog circuits with such a dynamic range were not available. If AE signals all had the same signal characteristics then it might be possible to design analog
circuitry to measure the energy. But AE signals come in a wide variety of shapes and sizes in thesame test. For a digitized signal the signal energy is defined as
E = (Δt/R)·Σi Vᵢ2(t) = (Δt/R)·As(4-1)

where Δt is the sample width in seconds, R is the resistance in ohms, Vᵢ(t) is the sample amplitudein volts and the summation is over T, the total signal length in seconds. As is the summation of thesquares of the sample voltages. For a Physical Acoustics Corp. system, this E is called the absoluteenergy in units of 10-21 J (or atto J) by multiplying with a constant, 9.31 x 10-4. If the constant ischanged to 6.70 x 10-6, the energy is given units of MeV (million electron volts), a unit the author
finds more useful. The one problem with this measurement is that it only covers the digitized signal
length. If the transient signal is longer, the calculated energy will be somewhat less than the actual
signal energy. However, most of the signal energy in a transient is near the beginning of the digitalrecord. The small percentage of the signal energy lost by truncating the signal is insignificant
compared to the other uncertainties in the measurement of the signal energy.
It must be stressed that while the signal energy is given in physical units, it is not the actualenergy of the emission, only the energy converted by the piezoelectric sensor. The actual emission
energy was radiated throughout the specimen and only that actually hitting the face of the sensor
is measured. The energy detected by the sensor is strongly affected by the distance between thesource and the sensor. In the analyses in this paper, the absolute energies from the three nearest
sensors to the emission source are summed to give a better estimate of the actual strength of thesource. This sum is what is graphed as energy in the analysis chapters.
29

Figure 13 The spectra (c) of a damped sine wave (a) and (d) of an AE signal (b).
The last signal “parameter” which is useful to calculate from the digitized waveform is the
Signal Spectrum. This is a Fourier transform of the digitized signal. The spectrum of the signal(Fig. 13b) is shown in Fig. 13d. In theory, one should be able to learn a lot about the characteristicsof the source from the spectrum. In practice a back calculation to the actual characteristics of thesource is almost impossible. The reason is that for the calculation, one needs to know the response
function of the sensor for the exact combination of acoustic waves, which excites it. Also neededis the response function of the specimen for all the acoustic paths of the generated acoustic waves,which reach the sensor. There have been attempts to back calculate the sensor response functionin the laboratory but these are very far from an actual test. The topic of the detailed interaction of
acoustic waves and sensors has been explored in recent papers by Ono et al. [11]. This work has
shown that the subject is far more complicated than was originally thought. The author was a farstronger advocate of spectral analysis in AE thirty years ago than he is now.
4-3 Noise suppression:
One of the major problems in AE testing is unwanted noise. Noise can be defined as any signal
detected by the AE system, which is not a desired response to the applied stimulus. This includes
all types of electronic interference and acoustic signals not produced by the stimulus as well as
undesired responses to the applied stimulus. In the past, electronic noise has been a major problem.
The defining electrical characteristics of AE signals are: random occurrence in time, random frequencies, amplitudes and durations and waveforms whose sole common characteristic is that they
are transients. Even transient signals, if they occur fast enough, can be superimposed and appear
as an apparent continuous signal. The response to such electrical noise has been improved shielding on sensors and preamplifiers, using double shielded coaxial cable when needed and much improved filtering in system power supplies. This has eliminated much interference from radio andtelevision stations, ignition systems on internal combustion engines and extraneous signals on input power lines. However, such problems as ground loops in the AE cables and multiple and30

poorly connected power grounds in the work facility are often encountered. Almost any industrial
area can have unexpected sources of electrical noise, which would not be a problem if one were
not looking for wideband signals down to the 10-µV level.
AE signals occur randomly and have no definite characteristics except their transient origin somany traditional electronic methods of noise reduction are not possible. Narrow bandwidth electronics, any type of frequency locks and predetermined time windows either do not work or eliminate real emission signals. Frequencies below 20 kHz are avoided for good reason. Most AE signals, with the exception of earthquakes, contain adequate energy above 20 kHz. It is desirable tobe able to converse during a test and there is no need to detect the normal acoustic background ofa shop. Radio and TV stations were once a problem but using good coaxial cables and shielding
on both preamplifiers and sensors has almost eliminated their interference. Modern systems havevery good filters on their power supplies but there are still situations in industrial locations where
noise on the input power lines is suspect. When suspected radio frequency noise is still detectedeven when the sensors are in a sealed can filled with foam or, better yet, a vacuum and there is still
noise, one good rule is to ground everything even remotely connected to the system. Still, it ispossible to spend years chasing that randomly occurring signal with little success.
Electromagnetic signals, as they are detected, are usually short, often with a single spike witha length of a few µs or less. Most acoustic signals have several oscillations at least. Filtering out
all signals with only one count usually does not remove any important AE signals. Another usefulfilter is the signal duration. Assuming that an AE signal has over three cycles of the dominate
frequency, one could filter out any signals shorter than 10 μs for a frequency of 300 kHz or 50 μs
for a frequency of 60 kHz. These two filter times have worked well for the Halon Bottle tester and
FRP tests.
Another problem is unwanted acoustical noise. Any two surfaces, which are in contact andhave the slight movement between them, can generate relatively large acoustic signals and suchsignals can travel long distances in metals. Impacts on surfaces can generate large signals. A rainstorm hitting a railroad track a quarter of a mile from the sensor has been seen. Any test involving
motion, such as rotation of a bearing or cycling of a wind turbine blade is going to have somecontinuous type acoustic signals. For a good bearing, the normal signals will be below the 20-kHz
low-pass filter in the signal channel. Any bearing signals that pass this filter may indicate problems
with the bearing. In fatigue cycling of FRP, such as a wind turbine blade, large noise signals areoften seen. If they are plotted against where they appear in the loading cycle, many appear in the
middle of the cycle where the load is changing the fastest. This suggests that there may be somesmall regions of the blade, which are rubbing together during the loading. On the other hand, signals from defects in the part usually appear during the rising load at or near the peak. This is where
flaw growth signals should be found. Current AE systems contain provisions for an electronicallycontrolled time gate. Using the load signal as input to this gate, it can be set so that the system only
accepts data during part of the load cycle. In wind turbine blade fatigue tests, this gate is open for
only the top 10 % of the load. This has worked well at filtering unwanted signals. Almost no
signals have been seen as the load decreases from 100 % to 90 %.
A final comment about noise signals is that no detected signal should be removed from a data
set as being noise until one has a good idea of its origin. Every acoustic signal is produced by somemechanism. In any real test, that mechanism should be identified and understood before a signal
is discarded. Often signals can be attributed to noise only after their locations have been graphed.
31

Chapter 5 Setting up the Test and Emission System
Acoustic emission tests are performed for a variety of reasons. In this monograph, only testsof structures are considered. There are three basic types of structural tests:
1. Proof tests where the object of the test is to prove that the structure is sound up to a proofload.
2. Failure tests where the object is to find at what load the structure starts to fail.
3. Fatigue tests where a cyclic load is applied to the structure to obtain some idea as to what
the working lifetime of the structure is.
AE monitoring of a proof test will indicate whether the proof load itself is producing any damage
in the structure. In tests to failure, whether static or cyclic, AE monitoring can indicate whether it
is a sudden or a prolonged failure and if there are several other regions, which are starting to failwhen the main failure takes place. Such monitoring can also provide some information on themechanisms of the failure.
The design of an AE test involves tradeoffs in both the physical set up of the test and in thetype of analysis to use. Most of the time the geometry and construction of the specimen and themethod of applying the test stress will simplify the choices. Other limitations will be the characteristics of the AE system, the number of channels in the system and the sensors available. For
clarity, various topics will be discussed separately although in a real test, they all interact to somedegree.
5-1 Test Assessment:
The first step in setting up an AE test of a structure is a study of the design and function of thestructure. What is its function? How well does it appear designed for that function? What is thetest designed to determine? Does the proposed loading duplicate the stresses seen in actual usage?
An AE test can only detect growing flaws. They must be activated the same way in the test as innormal operation. If mechanism for the test load does not correspond to the normal operation of
the structure, the value of the test is questionable. The loading rate must also be considered. If therate is too fast, the emission can swamp the instrumentation, too slow and the test can take forever.
Finally, what are the structural materials? Is the AE test operator familiar with them or not? If not,is there literature available which could provide information such as previous AE tests on the material? Or, are there samples, which could be tested? Of all these questions, the most important are
how the load is applied and the material properties. Only then should the choice of sensors andsensor location be determined.
5-2 Acoustic Properties of Materials:
Structural materials can be divided into categories; Metals, ceramics, glasses and composites.
Composites include both metal-metal and fiber reinforced plastic materials (FRP). Metals are generally crystalline, composed of many small crystallites (grains) of random orientations and different compositions. On a macroscopic scale, metals are relatively homogeneous. The acoustic wavelengths for the frequencies normally used in AE testing are much larger than the crystallites. Thus,
the acoustic properties of metals are averages over large number of crystallites and will be independent of frequency and direction of propagation. These bulk properties, both acoustic velocities
and impedances, are homogeneous with a single compressional and a single shear velocity. Acoustic attenuations, however, arise from a large number of mechanisms and cannot be so easily characterized. Ceramics are similar to metals in that on a microscopic scale, they are composed of32

randomly oriented crystallites, but with some porosities. Glasses have an amorphous structure withan average density and coupling coefficient between atoms. Both have bulk acoustic properties
similar to metals. They differ from metals in that both are quite brittle and tend to fracture suddenly
when their elastic limit is reached. Metals are ductile to some degree and undergo inelastic deformation before fracture appears. Plastics are seldom used by themselves as critical structural materials. They could be classified as very ductile glasses and are usually one component of composites.
Composites differ from metals in being composed of several materials, which are segregated into
distinct substructures. In general, these substructures are larger than metallic crystallites which
results in acoustic properties that are not homogenous. The different materials have different mechanical and acoustic properties. The bulk velocities of composites are anisotropic and can show
definite frequency effects. One difference often seen in composites is that the elastic limit is not
as abrupt as in ceramics or metals. Composites can experience a very long period between the load
where the elastic limit is reached and the failure load. There have been fatigue tests on wind turbine
blades where the composite matrix in the blade skin started to turn to powder before structural
failure occurred.
5-3 Acoustic Emission Frequencies:
The acoustic frequencies generated by AE signals cover a wide spectral range, from much lessthan one Hz in earthquakes to well over a MHz in metals and ceramics. Typically, AE testing
restricts itself to a low frequency cutoff of about 20 kHz and a high frequency cutoff dependent
upon the material being examined. The low frequency cutoff is usually set so that it excludes much
environmental noise produced in a test area, such as voices, machinery noise and other environmentally produced acoustic noises. The upper limit is usually determined by the material and thetest objective. Typical frequency ranges of 100 to 500 kHz are used in ceramics and metals and 20
to 100 kHz in composites. The choice is often determined by what sensors are available. If locational accuracy is wanted, higher frequency sensors are suggested.
5-3 Simple Attenuation and Velocity Measurements:
Because of the variation of acoustic properties with both material and geometry, it is advisable
to measure the acoustic velocities and attenuations in the actual test specimen. The following procedure can give a reasonably accurate measurement. Mount two of the test sensors to be used on
the specimen in an area free of welds, variations in specimen thickness and other features, which
could interfere with the acoustic transmission path. The distance between the sensors should be
between 200 mm for composites and 1000 mm for metals. Make five pencil-lead breaks (PLBs)
about 5 to 10 cm away from each of the sensors. The breaks should be approximately on the line
between the sensors. This procedure is designed to have at least similar acoustic waveforms exciting the two sensors. Record the arrival times and absolute energies for each PLB. The velocity is
the difference in arrival times over the distance between sensors and the attenuation is ten times
the log (energy2/energy1) over the distance. The averages of the two number sets should give reasonable values for the velocity and attenuation for that path. The trigger threshold used for themeasurements should be about the same as is to be used in the test. The velocity value will probably
have a smaller variation than attenuation value. For composites, the velocity and attenuation shouldbe measured in at least three directions, along the main fiber axis, perpendicular to that axis and
45 degrees between the two. A variation in the velocity between the two perpendicular axes of 10% or less may not make much difference in source location accuracy, so an average of the threevelocities can be used. However, a difference of 30 % or more can make a large difference so ananisotropic velocity calculation should be considered. If this is necessary, velocity measurements
33

should be made every 10˚ on the specimen. A FORTRAN program using an anisotropic velocity
is included in the Appendix. Programs, which can use an anisotropic velocity in locations, mayalso be included in the system software.
5-4 Sensor Layout:
The object in most AE structural tests is to cover all significant regions where failures could
occur. It is important to cover the areas of interest so that all parts of the area are within acoustic
range of at least three sensors. Where the areas are separated, multiple location programs can beused. Where it is desirable to display adjoining areas separately, such as the upper and lower surfaces of a wing or turbine blade, common sensors can be used in separate programs (check in thesystem instruction manual for the correct settings to use common sensors in separate location programs). The easiest starting point for laying out sensors is to start with an overall sensor patternsuch as interlocking triangles or interlocking rectangles. The triangular pattern covers somewhat
more area per sensor while the rectangular pattern has a somewhat greater probability of at least
four sensors hits by an emission, desirable for an over-determined data set. Once the overall patternis set, the actual locations can be determined from the features of the surface. Not only is it notnecessary to adhere rigorously to the starting geometric layout, it is helpful for the actual locationsof the sensors to differ somewhat from the geometric points. When using a nonlinear least-squares
program to locate the sources, having several sensors lying on a straight line encourages location
artifacts.
An important parameter in setting up a test is the acoustic attenuation of the specimen surface.
This will suggest the maximum sensor separation. The attenuation should be measured, if the value
is not already known. For accurate location with an approximation calculation, an over-determineddata set would have at least four hits per event for 2D location or five hits per event for 3D location.
Therefore, the sensor spacing must be small enough to allow sensors outside of the immediate
triangle around an intense source to be excited. A rule of thumb is that the maximum distance
between two sensors should have an acoustic attenuation between 30 and 40 dB. Thus, an attenuation of 0.1 dB/mm would suggest a maximum separation of 400 mm while an attenuation of 0.01
dB/mm could have a separation of 4 m on average between sensors on a triangle. This maximum
distance is a goal, not an absolute requirement. Any attenuation measurement on a composite
should not be believed to better than on order of magnitude because of differences in acoustic
scattering with different construction layups of the components.
5-5 AE System Setup:
The setup of the AE system consists of two parts, the AE acquisition parameters and the realtime graphs. The acquisition parameters include the trigger threshold, the timing parameters, possibly the gain and the channel bandwidth and waveform settings. The trigger threshold is set in dB
above 1.0 µV out of the sensor. It can be either a fixed value or a fixed value above the backgroundnoise level, a so-called floating threshold. A floating threshold is not recommended for multichannel tests. It is hard enough to get hit data sets from multiple sensors, which all trigger at approximately the same point on the waveforms without having the threshold of each sensor bouncing up
and down. The fixed threshold should be set a few dB above the trigger level of the noise level of
specimen. All sensors should have the same trigger level. Occasionally during a test, the noiselevel of one channel will increase to the point of continually triggering the channel. When thishappens, the trigger level can be raised for that channel. However, AE events located using that34

channel will have a greater location error. For a very high energy event, the absolute energy value
is probably still valid even though the location is more uncertain.
The timing parameters PDT, HDT and HLT in PAC systems, are not critical for most tests.
PDT, the peak definition parameter, is often set at 200 μs. This parameter helps measure the risetime, which ranges between 50 and 1000 μs for emission in an FRP test. One use of the rise timeis to help filter out hits, which have started just before the channel was activated. Hits with risetimes of less than a few cycles of the waveform are suspect. HDT, the hit definition time, helps
determine when the hit has ended. The standard value of 800 μs seems reasonable. HLT, the hit
lockout time determines how soon the channel can re-arm after a hit has ended. Most tests withrelatively slow load increase rates show repeated events on a channel with spacings of at least
several milliseconds. A re-arm gap of 1 or 2 ms has seldom produced a recognized problem. However, a gap of 20 ms could start missing data. The max duration setting should be set higher thanthe longest duration expected to be seen in the test. For FRP, a setting of 10 ms should be longenough. For metals such as aluminum and steel, a value of 50 ms or longer may be needed toinclude the full signal length of high amplitude hits.

Figure 14 Electronic bandpass characteristics of different filters as a function of frequency.
Most of the gain is in the preamplifier and is often fixed. With 90 dB of dynamic range in thesystem, the typical preamplifier gain of 40 dB is usually adequate. Additional gain on the computer
board seems redundant. The band width should be chosen to eliminate most room noise and any
truly high frequency noise (1 MHz +). It must be remembered that the upper and lower band width
figures are for the knees of the curve as shown in Fig. 14. A large amplitude signal with energy
outside of that bandwidth will still pass appreciable energy through the filter.

35

Waveform settings are the digitization rate and the number of words in a sample. The digitization rate should 1 to 2 MHz for composites and 4 to 6 MHz for metals. The problem with highdigitization rates is that more words are needed for the same length sample. Current PAC eightchannel boards are limited to 4000-word samples, which allows a 4-ms sample at 1 MHz, but only
a 1.33-ms sample at the boards maximum sampling rate of 3 MHz. Another problem is that a hiton an AE system uses only about 30 bytes of memory to store it in the computer while a 4000word waveform takes over 8 k-bytes of memory. If there are many low-amplitude emissions detected, data files, which include waveforms, can rapidly become unmanageable. For example, a 5megabyte file will contain about 500 hits with 4000-word waveforms or up to 200,000 hits without
waveforms. A fatigue test of a composite structure near the failure point can record those 200,000
hits in little more than one minute.
Another useful feature of an AE system is front-end filters. These can be invaluable in a noisy
environment. They automatically exclude hits that fall outside of preset windows for signal parameters. The most useful that I have found are the AE count, the signal duration and for source
location, the rise time. A count of one for a hit is usually meaningless – a simple excursion of abackground noise spike above the threshold. Such hits have low amplitudes and durations. They
may not appear very often and the only problem is that they may lock the channel out for theduration of the HLT time. However, some electrical equipment can generate short, intense rf
spikes. A little oxide on a coaxial connector or a minor cut in a cable shield may enable the cableto act as antennas for the preamp inputs. If they occur at harmonics of 60 cycles, they can completely lock out a channel. A front-end filter eliminating all hits with one count can be very usefuland have almost no effect on the detection of real emission.
A front-end filter rejecting signals with durations of less than three wavelengths of the dominate frequency seen in the test can remove other forms of electrical noise, which are short transients. The rationale for this is that most AE signals contain considerably more than three oscillations. Filtering out signals of less than 50 μs in FRP tests or 10 μs in the Halon bottle tester has
worked very well. Filtering out signals with rise times of less than a few wavelengths during location tests can removes signal, which have started before the channel trigger was reset. These signals, while they are real emissions, have arrival times, which, at best, confuse the location algorithms. While such filters are useful, they should be used with restraint.
One essential parameter for any load test is an electrical signal proportional to the load. Witha direct hydraulic load drive, there is a load cell and the hydraulic controller will have signal fromthe load cell. This may or may not be easily available to the AE system but it is necessary. Where
no load signal is available, such as a resonance fatigue test, a signal from a strain gauge located ina region where the strain is a linear function of the load, can be used. The gauge can be calibrated
by applying a static load to the blade. Note that in a resonance test, regions far from the point
where the oscillating load is applied may not be in phase with the load signal.
On a proof test or load-to-failure test, the background noise level is usually constant and independent of load. The real filtering need is to set the trigger threshold a few dB above the noiselevel. However, in fatigue tests, there can be a large amount of noise generated where the load islow but the rate of change of the load is high. Any structural damage due to the load should occur
near the maximum load. These other emissions may be due to rubbing friction in the specimen andhave nothing to do with induced damage. They can be greatly reduced by operating the system inthe voltage-time-gate mode using the load signal as the reference. Most load induced flaw signals
occur when the load is within 5% of its maximum value. By using the voltage-time-gate to shut
36

out the system unless the load is above 90 to 95 % of its maximum value most of this noise willbe ignored. As a proof of the premise, almost all of the emission occurs on the rising or the peakload. Almost nothing is seen as the load falls from 100% to 90% of its maximum. A word ofcaution. In a resonance fatigue test, the signal driving the voltage-time-gate should be provided
from the area of the specimen where failure is expected. The displacement signal from an area far
from the driving load can have a considerable phase difference from that of the driving load. The
other alternative is to filter out only signals near the maximum change in the driving load.
5-6 Example of Setup:
The best way to illustrate an AE test setup is to go through a simulation setup. The specimen will be a 9-m long FRP wind turbine blade. This blade was designed at Sandia, named Blade
System Design Study (BSDS) blade and tested at the National Wind Technology Center, Boulder,
CO. Acoustic velocities along the length of the blade of 3.0 mm/μs and of 2.4 mm/μs along thechord will be assumed, For this example, the velocity variation with angle will be that programedin the AEWin 2D Anisotropic program. Separate location calculations for the compression andtension side of the blade will be used. It will be assumed that the majority of the damage producedin the blade will occur between 0.5 and 3.5 m, as seen in the majority of 9-m long blade tests. The
sensor lay out is shown in Fig. 15. It consists of 6 rows of 4 sensors each surrounding the blade
every 0.5 m along the blade. The first row is on the chord at 0.5 m from the root. For consistency,
the sensor number sequence is started with #1 placed on top of the shear web at station 0.5 m on
the compression side of the blade. The sensors are then placed in rows, numbered clockwise looking from the end of the blade, on blade stations every 0.5 m. The 2nd, 4th and 6th rows are rotated
45 degrees from the top of the shear web. The chord measurements for the sensors on the two sides
are referred to the positions of the top and bottom of the shear web. The convention used is thatthe measurement is positive toward the leading edge and negative toward the trailing edge on bothsides of the blade. The sensors in front are placed as close to being on the leading edge as possible
and those on the rear are placed either on the top or bottom of the trailing edge surface, as isconvenient. Measurements of the trailing edge positions of the sensors should be either to thecenter of the sensors face or the point on the other side of the blade directly below the sensor’s
center. All sensor positions should be measured to the nearest mm if possible. Actual errors of afew mm are usually not important to the overall test.

37

Figure 15 Layout diagram of 24 sensors on the BSDS wind turbine blade.
Next, the location programs can be set up. The compression side will use the following sensors:
1, 2, 4, 5, 8, 9, 10, 12, 13, 16, 17, 18, 20, 21, and 24. The tension side will use sensors: 2, 3, 4, 6,
7, 10, 11, 12, 14, 15, 18, 19, 20, 22 and 23. Unless the shear web is perpendicular to both surfaces,
the leading and trailing edge sensors may have slightly different location values on the two surfaces. Using the AEWin program, set up two channel groups, compression and tension, and enter
the sensor numbers of the groups into the computer. Set the location type for each group to 2D
anisotropic then click the set all points button for each group. This allows the edge sensors to be
used in the calculations of both sides. Then set the HLT parameter to 0.0 for both groups. This
allows Hits to occur at the same time for both location groups. Go to the location setup view andenter the sensor locations. Notice that in the AEWin software, the two velocities vx and vy are
entered in different tables; the vx in the general location set up table and the vy in the sensor viewtable.
The next item is to setup the real time graphs on the computer. Most of the more useful graphs
are discussed in some depth in Chapter 7. For a single load test, a graph of the summation of the
AE count as a function of load is probably the most useful. Then come location graphs for bothsides, summation of absolute energy for located events graphs for both sides, AE hits vs. sensor
graph and distribution graphs for both hits and events vs. peak amplitude. In a fatigue test, a scatter
point graph of the parametric voltage for each hit vs time along with a similar graph for counts per
hit can be useful both to see how well a load filter is working and to monitor the overall emission
rate. A note of caution, an AE system has a capability to generate a huge number of graphs. Eachof these graphs needs memory and computational power. It is quite possible to greatly slow down
the system by setting up too many graphs. Modern computers are quite fast but too many simultaneous graphs can leave the system far behind the test in real time. Conservatively, a dozen realtime graphs should be about the maximum used. Many more graphs can be used in replay, but ata cost of a rapidly increasing replay time per file.

38

Finally, there is the problem of multiple files. AEWin allows the specification of maximum
file size and whether the files are separate or a continuation. A 5 to 10 Mbyte file size is convenient
for analysis and transporting. The author has found that separate files are easier to handle thancontinued files. Separate files are easily grouped together with an AEWin utility to make one large
file when needed. If the files are recorded in continued mode, the continuing time base can makethe individual files more cumbersome to analyze.
Chapter 6 Source Location
The main function of an AE test is to identify flaw growth in a structure as it undergoes anincreasing or continuing stress. Ideally, the test should both locate the flaws and describe their
growth rate as the stress level increases or the stress state continues in time. On simple structuresa single AE sensor can report how the structure itself is behaving. However, complex structures
will have many possible flaw sites. If it is large or constructed of multiple materials or materials
with high acoustic attenuations, there will be many structures, which cannot be monitored by asingle sensor. Thus, most of the growth in the field of AE in the last 40 years has been in the designof multi-sensor systems and their analysis techniques. The focus of these techniques has been thelocation of AE sources on large structures. Starting with the early work of Green et al. at AeroJet
Corp., a primary structure of interest was pressure vessels. Now, almost any structure that experiences changes of stress in normal operation is a candidate for AE testing and most of these tests
involve source location.
The basic idea in source location is to cover a surface with a network of sensors. If one candetermine the arrival times of an AE signal at several sensors, then, knowing the acoustic velocity,
it is possible to triangulate back to the location of the source of that emission. The idea is simple
but there are already several assumptions that have been made. First, an isotropic material is assumed so that there is one acoustic velocity. Second, the shortest path between the flaw and thesensor can be determined. Third, the AE signal is simple and its exact time of arrival can be measured. And fourth, the resulting set of nonlinear equations can easily be solved.
The assumption of an isotropic material for a metal pressure vessel seems safe. However, asingle known velocity and a known acoustic path may not be. The shortest distance between a flaw
and a sensor may go through or around a vessel penetration or a weld. The change in the acoustic
path may be small but it is real. The acoustic velocity in a plate is not a constant but is a functionof frequency (cf. Fig. 6 for plate wave velocity). The geometry and speed of the fracture of theflaw is unknown. There may be several frequencies and an unknown polarization in the excited
acoustic wave. As it travels toward the sensor, the frequency and polarization components of an
acoustic wave can change with distance. The result is that the waveforms detected at the different
sensors may be quite different from each other. The amplitude of the wave that first crosses thetrigger level of the electronics will not always occur at the same portion of the waveform. Again,
there will be variations in the triggering time of each sensor from what would be predicted bywhatever model is being used in the analysis.
The equations for the distance between two points on a surface are nonlinear even for a flat
plane. To locate a point on a plane, one has two unknowns. However, in an AE experiment, one
records arrival times of the acoustic wave at the sensors, but in order to know distances, the startingtime of the wave must be known. Therefore, to triangulate the location of an AE source, one has
three unknowns, the two coordinates of the source on the plane and the time that the emission was
generated by that source. One problem with sets of nonlinear equations is there are at least several
39

solutions to the set of equations if there is any solution. Real experimental data is full of small
uncertainties. Triggering on slightly different parts of the waveform at different sensors as wellminor path differences from those predicted by the model can give data sets, which have strange
solutions or no solutions. Further, analytic solutions to even an exact set of nonlinear equations do
not come easily, if at all.
Another major problem is that many structures have varied geometries and may be multiply
connected. One can get analytic solutions for the shortest path between two points for planes andspherical surfaces. One can also combine these two types of surfaces into cylinders with hemispherical end caps. Beyond these shapes, one has to approximate. Large surfaces, which have
gradual curves, can be approximated by planes. An aircraft wing or wind turbine blade can beapproximated by either two independent surfaces or one surface folded down the middle. Large
curved surfaces can be approximated by a group of joined plates. The beauty of AE testing is thatexact modeling of a surface is not necessary. Even if the model is far from a one-to-one approximation of the actual surface, all emission from one spot on the surface should located at one placeon the model. While the location on the model may be some distance from the corresponding pointon the structure, the important fact is that all the emissions that were located at that point actuallyoriginated from the same location on the structure. Thus, a grouping or cluster of points on themodel corresponds to an actual location on the structure. While the two locations may differ, thefact that there is an active flaw at a point on the structure is real. If the exact location of the flaw
is needed, one can inject signals into the structure at various points until the same arrival sequenceat the different sensors is found.
A way around the problem of analytical solutions is to use a numerical approximation. One
type is known as nonlinear least-squares fitting routines. Such a calculation starts from a trial solution. In an AE calculation, this is usually a point inside the triangle formed by the first three
sensors hit. The routine starts with this trial solution and calculates the travel times of the wave
from that point to the sensors. It then takes the sum of the squares of the differences between thecalculated travel times and measured arrival times for each sensor. If the sum is not zero, it will
make small shifts in the trial solution and from the changes in the sum, estimate what a better trial
solution would be. This iteration is repeated until the sum ether reaches zero (a real solution) or aminimum (the best estimated solution). Because the trial solution was essentially a best guess fromthe physics of the test, the solution arrived at is usually the most probable location of the source.
Often, a nonlinear least-squares routine will calculate a goodness of fit parameter, which is an
estimate of how good the answer may be. Because of errors in the data, it is always possible to getsolutions, which are located well off the surface of interest. If the fitting parameter is reasonable
and the solution lies within the bounds of the sensor network, one can assume that the solution is
the most probable location of the source. An over-determined data set, using hits from additional
sensors outside the triangle of the first three hits, will generally improve the accuracy of the calculation. However, using more than six to eight sensors starts to degrade the calculation because
the traveling waveforms have often lost most of their relationship to the original waveform andthe triggering point may have little correlation to that of the initial waveform. Thus, the measured
time of flight may have little to do with the actual acoustic velocity.
Another approximation usually used in source location is that the acoustic velocity is isotropic.
This is usually not the case for composites and may not be the case for metallic structures if they
are made of plates with different thicknesses. A nonlinear least-squares program has the capabilityof using anisotropic velocities. A routine, which uses the velocity distribution, shown in Fig. 33,
is included in the Appendix. Because of the large amount of variation in AE data from composites,40

small anisotropies in the velocity are not that important. However, if the anisotropy in the velocity
is greater than 10 %, using it in the location calculation may improve the fit. An anisotropic velocity routine is available in AEWin software has a fixed angular dependence of the velocity. In many,
but not all, composites, this should give adequate results.
An AE cluster is a group of emission sources, which occur near the same location on the
structure. Current commercial AE programs include various methods for detecting and defining
such a cluster. The importance of a cluster cannot be over-emphasized. A growing flaw will generate a series of emissions. The actual source of these emissions may be a very localized source,
such as the tip of a growing crack or a more diffuse source, such as a patch of corrosion. Becauseof minor errors in the measured arrival times, both types of sources can appear as a cluster ofsources over a small but definite area. It is up to the designer of the test to define a cluster. The
usual definition is at least some number of located sources occurring in a defined area on the surface of the specimen. It is often easier to define the clusters during or after the test. The mostimportant characteristic of a cluster is, first, that it exists. The presence of a cluster indicates that
there is a specific region on the specimen, which is generating acoustic emission. The second is
the size of the cluster. This can either be the number of events or the amount of an AE parametercontained in the cluster. Most commercial programs allow the system to grade the clusters basedon either the number of events contained or amount of the parameter measured. The third is the
behavior of the cluster with increasing stress. The sum of the number of events in the cluster plottedagainst stress or time can give strong indication as to whether that cluster is produced by a flaw
undergoing steady state, i.e. controlled growth, or uncontrolled growth leading to failure of thespecimen. If the emission is fairly uniform such that the variation of the AE count or peak amplitude is not much over an order of magnitude, the number of located events in the cluster is anadequate measure of its growth. However, if the variation is large, as occurs in composites, a plotof the absolute energy of cluster members is a better measure of its behavior. Unfortunately, atleast with PAC AEWin software, plots of the absolute energy of a cluster as a function of load ortime are not available.
The author has been using a nonlinear least-squares fitting routine in data analysis since themid-1970’s. It was used by several physicists at Sandia at the time and was applied to AE analysis.
The name of the routine is NRL but over the last 35 years in the course of many moves, all references to its origins have been lost. The program is the foundation of the current analysis techniques
and a FORTRAN listing of the location program, which was used on the Sandia Sensor Blade test
is in the Appendix. This program can locate emission sources and plot the locations with the absolute energy of each color coded as to order of magnitude. It can also plot the absolute energy ofthe sources located in a cluster as a function of load or time. These graphs have been very usefulin trying to analyze the results of BSDS wind turbine blade structural tests. A discussion of nonlinear least-squares programs, with examples, is given in “Numerical Recipes in FORTRAN” [13].
Selected Reading List from J. AE Articles
K. S. Downs and M. A. Hamstad, Correlation of Regions of Acoustic Emission Activity with Burst
Locations for Spherical Graphite/Epoxy Pressure Vessels, J. AE., 1995, 13, 56-66.
A. Wanner and M.R. Gorman (eds.), Materials Research with Advanced Acoustic Emission Techniques, J. AE, 1996, 14. Twelve papers from a Workshop at Schloss Ringberg, Germany.
M. Ge, Analysis of Source Location Algorithms, Part 1 and 2, J. AE, 2003, 21, 14-28, 29-51.

41

Chapter 7 Analysis
The primary purpose of an AE test on a structure is the detection and monitoring of flaws,
which affect the integrity of the structure. An AE test is by definition not a nondestructive test as
the detected emissions are produced by irreversible changes in the material under test. However,the sensitivity of an AE test is usually high enough to detect flaws long before they reach a severity
level, which will affect the strength of the structure.
A flaw can be defined as any defect or error in a structure, which can affect the strength orwell-being of the structure while it is in normal use. For the field of AE, the term flaw will refer
only to flaws, which are influenced by the working loads on the structure. A flaw such as a cracked
step in the ladder leading to a crane is serious, but is not influenced by the load hoisted by thecrane. The growth of a flaw, as influenced by the load, can roughly be divided into three categories;
controlled flaw growth,
uncontrolled flaw growth andcatastrophic flaw growth.
As an example, take a piece of metal composed of many random crystallites. Micro-flaw
growth can involve either the fracture of a crystallite, the fracture of a boundary between two
crystallites, the fracture of a boundary between a crystallite and an inclusion in the metal or thefracture of the inclusion. All of these cases will have a stress level where an applied stress tensor
will cause a fracture. To be accurate, there will probably be several stress levels where different
stress tensors will cause a fracture of the same crystallite. The fracture of a single crystallite willusually rearrange the stress tensors on the surrounding crystallites. This rearrangement may leave
the surrounding micro-stress tensors essentially unchanged or change the micro-stress level onsome crystallites to some degree. An increase in a micro-stress level may be enough to fractureother crystallites. In such a case, an avalanche to macro-fracture can be the end result. Whatever
the result, it is totally dependent upon the local stress environment and local material environment.
There is no way of predicting the exact stress level or the exact location of the first or any othermicro-fracture.
Controlled flaw growth will occur when the fracture or group of fractures does not produce
a rearrangement of the local stress fields, which causes further fractures. The emission rate willremain relatively constant or even drop as the stress level is increased. A decrease in the rate usually means that there were a relatively small number of regions where built-in higher micro-stresslevels were left from the manufacturing process. When these have all fractured, the emission rate
will decrease until the fracture stress level of more serious flaws is reached. Such regions where
the micro-stress levels are significantly higher than the mean are often found in new FRP structuresand seldom in metal structures. In their absence, most AE tests start out with no emission (above
background noise level) and then proceed into a controlled flaw growth regime with a relatively
steady emission rate over some range of applied external stress. When the emission rate starts toincrease with stress, uncontrolled flaw growth has started.
In uncontrolled flaw growth, the micro-fractures generally produce an increase in the local
stress fields of the surrounding regions. As the external stress field is raised, the rate of emissions
increases. This rate increase may be slow at first but then tends toward an exponential increase. If
the exponential increase is not stopped, the test will rapidly proceed to the failure at the flaw. If
the load increase is slow, there usually will be enough time in this uncontrolled flaw growth region
to stop the test and prevent a serious failure. This is one of the prime purposes of many AE tests.
42

In catastrophic failure, the exponential increase in flaw growth may be quite rapid. The speedof the failure is a property of the material. In brittle fracture, there is little or no AE preceding the
failure. A typical example is the fracture of glass. An AE test may tell when a small glass part ina structure breaks but it will seldom give advanced warning of brittle fracture.
Analysis of an AE test consists primarily of looking for the occurrence of AE and then measuring the rate of that emission with respect to the change of the test stress level. In structures wheremulti-channel tests are used, the location of the emission and the measurement of the emission rate
for each located area is the goal. An analysis can either be done in real time if the prevention of
structural failure is the test objective or in post-test analysis. Real-time analysis depends primarily
on an operator’s experience and knowledge. It can become interesting, especially where the loading involves large energy levels, and can probably only be learned by experience.
Acoustic emission tests can be divided into two types, depending upon the loading. The first
type has a single load to either a test load or failure. The load may be applied continuously or insteps. In FRP tests, the steps may be separated by returns to a zero load between each step. This
allows a determination of the Felicity ratio at the beginning of each step. In a continuous load withsteps, the load hold between steps allows the determination of how fast the emission stops as theload increase stops. As a specimen nears failure, the emission will often continue for a period oftime after the hold starts. When the emission does not stop during the hold period, that is a goodtime to declare the test over and reduce the load, unless the object of the test is to actually fail the
specimen. The second type of loading is the fatigue test. The load is periodically cycled over a
fixed load range. This range may be constant over the test but often it is increased after predetermined cycle intervals. The test may be monitored continuously or the AE system can be activatedonly at near peak loads with a voltage-controlled gate. The practice of detecting emissions only at
peak loads restricts the observed emission sources to those produced by actual flaw growth andeliminates frictional sources, which are at a maximum during the periods of rapid load change. In
fatigue tests, there is usually an interval where the emission rate from flaw growth increases slowly
with time. However, there can be exceptions to this rule. The author has analyzed one fatigue test
where the failure occurred quite suddenly and only extensive post-test analysis revealed some prefailure emission.
7-1 Test Graphics:
Analysis of a test in real time is up to the operator of the AE system. The operator is totallydependent upon the graphs, which have been set up on the system. In the early days of AE testing,
two of the most important instruments of the test setup were the oscilloscope and the audio channel.
The scope would show the output of one AE channel. One could get a good idea from the scopeof how rapidly the emission was occurring, its amplitude and the general shape of the emission
bursts. Non-emission noise signals showed up immediately. Impending failure of the specimen
was quite evident as the whole scope would light up. The audio channel would use a beat frequency
to bring the main AE frequencies into the audio range. An experienced operator could obtain additional information about the status of the test from changes in the audio amplitude and frequency.
From these instruments and an x-y recorder, which plotted summed AE counts against load ortime, an experienced operator could give a good picture of what was happening in the test.
Currently, the AE systems are digital and the oscilloscope and audio instrumentation is missing. They are replaced by real time graphs on the computer screen. The following displays areuseful in both real time and post-test analysis.
43

7-1-1: AE parameters vs. load or time graphs
A graph of the AE hits, events, counts or absolute energy versus the load or time is essential.
The author prefers the graph of the cumulative hits etc. over the graph of the rate of hits etc. because it is easier to visually differentiate a cumulative graph than to visually integrate a rate graph.
The parameter vs. time graph is the classic graph, which has been used almost from the start of AE
testing. The choice of which AE parameter is used will depend on what is expected out of the test.
AE hits or counts will show all detected AE with counts being more sensitive to the intensity ofthe emission. An AE events count is sensitive to emission from actual flaws but may miss regions
where only one or two sensors are activated. Counts or absolute energy per event will give themost information about the state of flaw growth but can become confusing when there are multiple
flaws. A rate of absolute energy per load interval graph is probably the most useful but must be
confined to post-test analysis since it requires a knowledge of the total behavior of all clusters.
Changes in the slope of the cumulative graph indicate changes in the AE rate. A steady rate in
the absence of external noise indicates controlled flaw growth while an increasing rate indicates
uncontrolled flaw growth. When the slope of the curve appears to increase toward an exponential,or shows a distinct knee, structural failure is usually approaching. Figure 16 shows a steady emission rate in Fig. 16a and an increasing emission rate in Fig. 16b. These are graphs of the numberof located events as a function of temperature (load) in two Halon Bottle tests. Both bottles failed
the test but for different reasons. In Fig. 16a, the number of AE events in a cluster exceeded apreset maximum and in Fig. 16b, both the slope of the event vs temperature curve for a cluster andthe number of events in the cluster exceeded preset maxima. The test in Fig. 16b was terminatedearly by the computer program to prevent catastrophic failure.

Figure 16a Stable flaw growth in Halon bottle.

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Figure 16b Transition from stable flaw growth to unstable flaw growth in a Halon bottle.
7-1-2: Peak amplitude distributions
Graphs of the peak amplitude distributions of AE hits and events are useful. Figures 17a and17b show peak amplitude distributions of a small portion of the BSDS blade fatigue test. The AE
hit distribution shows most of the hits were below 65 dB peak amplitude while the events fell into
two groups, one near 54 dB and one near 71 dB. There were a large number of hits compared to
the number of located events for each peak. However, there were many more events per hit near
71 dB than near 54 dB. This implies the growth of two different flaws, which should be apparent
from a location graph. A location graph (see Figure 19) shows low amplitude events located above
sensor 3 and the high amplitude events located around sensor 5.

Figures 17a and b Amplitude distributions of hits and events from the same BSDS data.
45

7-1-3: A bar graph of the number of hits per channel.
Figures 18a and 18b show a linear and a log plot of the hits per channel of the same data as
shown in Fig. 17. The linear plot Fig. 18a shows that channel 13 is receiving most of the hits. It is
so dominant that most of the other sensors appear inactive. Using a log plot of the same data, Fig.
18b, we see that is not the case and that only sensors 11, 15, 21 and 23 appear totally inactive while
sensors 6, 17 and 22 have only a few hits. It also appears that the part of the structure covered by
sensors 14 thru 24 is showing very little emission. This data was from a fatigue test, so a temporary

Figure 18a Hits vs Channel: linear scale.

Figure 18b Hits vs Channel: logarithmic scale.
46

stoppage of the loading did not affect the test. The low amplitude channels were tested with PLBs
shortly before this data set was recorded. All channels tested good except channel 11. It was determined that the electronics on channel 11 were not working but the sensor was and the sensor at
position 11 was connected to channel 13, which had a very low activity, and the switch was inserted to the layout file. Whether the excess signals seen on channel 13 in Fig. 18 was caused bythis switch is not known. However, the data in Fig. 18 and resulting PLB tests did show that thelack of emission from sensors 14 to 24 was real and not an electronic problem.
7-1-4: Location graphs in real time.
These graphs plot the calculated location of each event as it occurs. The location calculation
can be setup as a single graph or multiple graphs. The graphs setup for the system monitor probably
will not have a one to one correspondence to the structure.

Figure 19 Location graph of data from BSDS wind turbine blade fatigue test.
This is not very important for real time monitoring. Usually most emissions originating in one
region on a structure will have their calculated locations in a similar region on a graph even though
the two regions may have somewhat different locations on the structure. The appearance of a cluster of events on a real time graph indicates a growing flaw and the behavior of the events with load
will define how serious the flaw is, even if the calculated location appears to be vague.
Figure 19 is a location graph of the BSDS wind turbine blade fatigue test for 15,000 cycles
early in the next to last loading level for the test. This representation assumes that the leading edgeof the blade is straight and that the blade surface is opened flat out on either side of it. Sensors 2,
10 and 18 are located on the leading edge with sensors 1, 9 and 17 on the spar cap on the lowpressure side and sensors 3, 13 and 19 located on the spar cap of the high-pressure side of theblade. Sensors 4, 12 and 20 are located at the trailing edge. The blade surface has been distorted
but this this representation allows one to see the whole blade surface at one time. The distinct
straight lines in Fig. 19 are artifacts often seen in tests involving source location. Straight lines are47

often artifacts of the calculation while curved lines often arise from RF interference. Both may becaused by beat frequency interference signals, which are locked to the load frequency. They may
appear to be acoustic in origin but the regular patterns often seen in the data indicate that are notsignals from a growing flaw. They are usually seen events, which only contain three hits. As arule, AE from a flaw is random in occurrence. Any regular patterns seen in the raw data either
imply signals with non-flaw origins or events along a structural feature.
Another point that should be mentioned in setting up location graphs like Fig. 19. Software
based upon nonlinear least-squares fitting of the data can use information from any number of
sensors up from the minimum necessary for the geometry of the calculation. For a surface, theminimum number of sensors is three. There will usually be more 3-sensor events than 4-, 5- or 6sensor events, just because of the normal variation in amplitudes of the events. A 3-sensor eventon a surface usually implies an exact calculation. The location graph Fig. 19 was calculated using
all events containing 3 to 6 sensors. If the calculation had been restricted to 4 to 6 sensors, thestraight lines and most of the well filled in curved lines in Fig. 19 disappear. However, the clusteraround sensor 5 and the scattered points above sensor 3 remain.
7-1-5: A scatter point graphs
A scatter point graph as a function of time or the load value for each hit and an accompanying
scatter point graph of the AE count for each hit are also used. These graphs are useful for fatigue
tests. In a fatigue test, flaw growth is only going to occur at or near the peak load. However, in acomplex structure, such as a wind turbine blade, there are often areas in the blade where apparent
frictional emission is generated. This emission is usually most intense at the mid-load region,
where the actual change in the load is a maximum. If the load is offset such that one peak of thecycle is at the normal zero load of the specimen, then the maximum load is at the opposite peak of
the cycle. By using a voltage-controlled gate to only accept data at the top 10 % of the load, one
can greatly reduce the amount of data being collected. If the load is not offset, one may want toaccept data from both the positive and negative load peaks. (One caution, in a resonant fatigue test,
the load signal from one position on the blade may be out of phase with the displacement at thelocation of a flaw. The gate must be set so that it includes the displacement peaks at the flaw
positions, even if it means accepting larger data files.)Figure 20 shows the AE count and load curves for one data file from a test, which had a large
amount of emission occurring throughout the entire load cycle. The recorded data was restricted
to the AE hits, which occurred in the upper peak of the load cycle from a load value of 2.2 to 2.6
volts. The AE count curve (top) shows the times when more energetic emission was occurring andshow that the gaps where the AE system was paused because other data was being taken were notfalling into periods of high emission rates. In this test, there was a large number of signals during
every cycle of the loading but locatable emission was still occurring only every few cycles. The
use of a voltage-controlled gate can reduce the number of unwanted signals collected by a factorof 5 or more. By pausing the AE system when other interfering data is being collected, the ability
to collect exact cycle data is removed. However, by knowing the time interval of the gaps andwhen they occurred, the ability to plot emissions as a function of number of cycles is not lost.

48

Figure 20 Scatter point plots of AE counts (top) and load parameter (bottom) for period of a blade
fatigue test. The data was accepted only with load parameter values of higher than 2.20 V. The
two breaks were where the AE system was paused while other ultrasonic tests were being run on
the blade.
There is one more procedure which can be useful in determining what signals are produced byactual flaw growth and which result from some other mechanism during a fatigue test. A windturbine blade fatigue test applies a large amount of force and motion to a blade and it is sure that
not all of the acoustic signals are produced by material failures. Since AE is a result of random
processes, it is of value to look at the output of such a process. An easy experimental example isthe melting of an ice cube in a glass of water. As each crystallite of ice melts, it changes the local
stress fields in the ice cube. This results in a continuing micro cracking in the ice cube and a steadygeneration of acoustic emission. Plotting the distribution of the absolute energies from the emissions produced by a melting ice cube gives the curve shown in Fig. 21. While there are many more
emissions at the low energy end of the curve, all energy values appear represented. With 7500
emissions, almost every energy value between 0 and 200 units has at least one burst. There are nopeaks in this energy distribution nor are there any gaps. We expect AE from flaw growth to havethis general distribution of energies.

49

Figure 21 Distribution of signal absolute energies from emissions generated by melting ice cubes.
Now look at Fig. 22. This shows the distribution of energies from five sensors spanning two
built-in flaws in a wind turbine blade undergoing fatigue testing. Channels 17 and 21 follow the
distribution seen in the ice cube emission. Channels 18 and 20 have more structure but still show
a broad distribution of emission energies. Even channel 23, which had very little emission, does
show a broad distribution of energies. There is no indication from this data that this is not realemission produced by structural failures in the blade. However, at the root end of this blade where
the strength of the blade is much higher, data from the same set showed a large amount of emission
located in one area of the blade surface. This is seen in Fig. 23 and is distributed in a strange patternof curved lines, which had nothing to do with the blade structure. Sensors 1 and 5 are located on
the opposite side of the blade from sensors 2 and 6. The energy distribution from the six sensors
in the root region is shown in Fig. 24. While sensor 6, which was adjacent to the drive fixture,shows an energy distribution similar to the ice cube, sensors 1 thru 4 show all the energies thatwere detected by each sensor to lie within a narrow energy band. Those AE signals appear to be
produced by some other mechanism than flaw degradation. The conclusion here is that while thesignals from sensor 6 were probably produced by small relative movements between the bladesurface and the drive fixture, located emissions using data from sensors 1 thru 4 were complete
artifacts produced by signals excited by the motion of the fatigue driving mechanism. When thelocated emissions do not appear to make sense, either from a strange location or strange distributions on the blade, this type of analysis can be very useful.

50

Figure 22 Energy distributions from five sensors near built-in flaws in blade.

Figure 23 Apparent flaw locations on high-pressure surface of blade root near drive fixture.

51

Figure 24 Energy distributions for six sensors on the root of blade. Same data as seen in Fig. 23.
These are the main graphs that the author considers useful to the operator running the test. On
a fatigue test, he has often set up graphs of absolute energy rate vs time for both sides of the blade.
Unfortunately, there has seldom been a significant difference in the two graphs and so there is norecommendation for such graphs unless there is a real expectation that different regions of thestructure will be affected differently by the increasing load or cycle count.
7-2 Post Test Analysis:
The first step in post-test analysis should be a critical review of all the data collected during
the test. It is usually valuable to create a time line of the loading sequence and when all eventsoccurred during that sequence. At the point where the emission starts, replay that data and look at
the listing display. If there are a large number of 3-hit locations, look at the delta times or arrival
time differences. If these delta times are almost identical for many of the hits and/or the hit durations or hit energies are very similar for the different located events, the events are probably not
generated by flaws. In a fatigue test, any hits or events, which occur regularly in phase with theload signal, will seldom be produced by flaw growth, even if they occur at the load peaks. Microscopic flaw growth depends upon the local material environment. Regular crack advancement during a recurring load will only be seen near failure, if at all.
It is often useful to combine several sequential files into one larger file, which covers a single
time sequence. AEWin has a “link separate data files” utility, which works well. One word ofcaution, AEWin slows down the replay drastically when the file is too large. If the test is a single
loading of a structure and there were pauses during the loading, look for when the emission did
not stop instantly with a loading pause. This is an early indication of flaw growth and indicateswhere one should start looking for locatable events.

52

Most of post-test analysis consists of trying to determine where the flaws were and what their
behavior under stress was. During the time of the test, one is limited to the graphs which were
implemented for the test and for the current time period. The number of real time graphs is limitedby the speed and capacity of the computer. However, there is no such limitation in post- test andnumber of possible graphs available in current AE software is very large. The start of the post-test
analysis should use the original real time graphs to replay the test. After that, other graphic analysis
should be used as appears desirable.
Unfortunately, there are some analyses, which are often not available. A group of source location programs evolved over the author’s career in the practice of AE testing. An example is the
FORTRAN program discussed in Chapter 6 and listed in the Appendix. One technique used is tocolor code the located emissions on a graph with respect to their absolute energy. Another is todefine specific locations on a location graph as clusters and then to plot a graph of AE parameterversus time or load for each cluster. AEWin does show clusters but does not allow the freedom tochoose an arbitrary size or location for each cluster nor does it allow the plotting of an arbitrary
AE parameter such as the absolute energy of the event for members of the cluster. Finally, AEWin
does allow for anisotropic acoustic velocities but specifies a single shape of the anisotropy. In atleast the TX blades, the acoustic surface velocity anisotropy was unique to the blades. In many AE
tests the results are not ambiguous and the commercial programs can produce acceptable results.
However, most of the AE testing at Sandia has been analyzed with variations of the listed
FORTRAN program and they are used in the following example.
Figure 25 is very similar to Fig. 19. It uses the same data, but covers many more cycles of thenext to last load block. The figure shows four areas that appear to have clusters starting. This datais shown here so that the incipient clusters can be identified. Figure 19 shows the located eventson the tension side of the blade during the last loading of the test. As can be seen, there is so much
located emission that the original clusters are hidden.

Figure 25. Location graph the next to last load block from extended data set of Fig. 19 showing
locations of incipient clusters.
53

Figure 26 Location graph of tension side of blade during last loading. The view is an inverted
image of the top half of Fig. 25. Absolute energies of events are in MeV and the color code isgiven at bottom. The location conventions of the graph are that the shear web position is zero chordon both sides of the blade. Positive values are toward the leading edge and negative values aretoward the trailing edge.
Figure 27 shows the total energy vs cycle graphs for the four clusters seen in Fig. 25. The
cluster on the low-pressure (compression) side of the blade shows a decreasing slope as the test
moves toward failure. This was the major cluster on the low-pressure side of the blade and showsthat the failure was confined to the high-pressure (tension) side of the blade. Most of the energy
release was in cluster HP1 surrounding sensor 7. The major failure appears to have started at about
73,000 cycles. The emission rapidly increased until around 90,000 cycles where it becomes almost
constant until failure. The interesting feature of the failure is that this rapid increase in the energy
release rate is seen in all three clusters on the high-pressure side. Furthermore, there are indications
in all three clusters that the energy release rate slowed down just before failure. When the 12,000
cycles were examined in detail, it was found that the release rate where the failure occurred waslittle different than the release rate of the front high-pressure surface of the blade from 500 to 1500
mm. The conclusion reached was that whole front surface of the blade between 500 mm and 1500
mm was failing and that the actual fracture could have occurred anywhere on that surface. This
blade appeared to be without any serious flaws, most of the surface being of uniform strength andthe actual failure position occurred almost randomly on the blade. The graphs in Figures 26 and
27 were made with the author’s FORTRAN programs. Further details of this test are given in
Chapter 9.
To sum up. The post-test analyses are the most important part of an AE test. In most cases,
the customer is less interested in the test details and analysis as long as the methods are believable. What is important is the information which the AE gives about the test. If a test is not taken
to actual failure, what is the location of the worst flaw and an estimation of how close the specimen was to failure? Or on a proof test, were there any signs of flaw growth at all during the test
and was the test sensitive enough to see minor flaw growth. Fine details of the analysis are often
not needed in a final test report but the estimate of the health of the specimen is all important.

54

Figure 27 Total energy vs cycle graphs for four clusters for the last load value of the BSDS blade
fatigue test.
Selected Reading List from JAE and Open Access Papers
P.R. Blackburn, Acoustic Emission from Fatigue Cracks in Chrome Molybdenum Steel Cylinders, J. AE, 1988, 7, 49-56.
M. J. Peacock, Acoustic Emission Monitoring of a Large Pressure Vessel during a Pneumatic
Re-qualification Test, J. AE, 1989, 8, 11-20.
S. Hewerdine, ICI's Perspective on Acoustic Emission Monitoring, J. AE, 1989, 8, 21-24.
R.K. Miller, R.G. Tobin, D.J. Gross and D.T. Tran, A Summary of Experiences with MONPAC™ Testing by the MQS/Dunegan Testing Group, J. AE, 1989, 8, 25-30.
P.T. Cole, MONPAC - Condition Monitoring for Static Plant - Case Histories, J. AE, 1989, 8,
31-34.
H.-A. Crostack and P. Böhm, Monitoring of a Pressure Vessel (ZB2) by means of Acoustic
Emission, J. AE, 1990, 9, 29-36.
K. S. Downs and M. A. Hamstad, Correlation of Regions of Acoustic Emission Activity with
Burst Locations for Spherical Graphite/Epoxy Pressure Vessels, J. AE. 1995, 13, 56-66.
55

D. Kouroussis, A. Anastassopoulos, P. Vionis and V. Kolovos, Unsupervised pattern recognitionof acoustic emission from full scale testing of a wind turbine blade, J. AE. 2000, 18, 217-223.
V. Godinez, S. Vahaviolos, R.D. Finlayson, R.K. Miller, and M.F. Carlos, Improved source location methods for pressure vessels, J. AE. 2000, 18, 272-279.
A. Sato et al., Development of Abnormality Detection Technology for Electric Generation Steam
Turbines, J. AE, 2001, 19, 202-208.
M. Takemoto, H. Cho and H. Suzuki, Lamb-Wave Acoustic Emission For Condition Monitoring
Of Tank Bottom Plates, J. AE, 2006, 24, 12-21.
K. Ono, Review on structural health evaluation with acoustic emission. Appl. Sci. 2018, 8, 958;
doi:10.3390/app8060958.
Chapter 8 TX Blade Fatigue Test
Next three chapters present three test cases in detail. The first example of an AE test setup andanalysis is the fatigue test of the TX blade [14]. This was a 9-m wind turbine blade with a special
skin designed to rotate the tip angle of incidence as the load on the blade increased. The modification was a single layer of carbon fibers on the skin surfaces, which were oriented at 20˚ from theaxis of the blade. The first TX blade was subjected to a static load test to obtain structural characteristics of the blade. That blade failed near the 1-m station on the low-pressure surface. There wasalso significant AE activity on the high-pressure side near the root and on both surfaces near the
4.6-m station where the internal shear web with its accompanying spar caps ended. These results
suggested local stress concentrations at these locations. Previous fatigue tests on this type and sizeof wind turbine blade had been carried out with a hydraulic actuator driving the free end of theblade up and down. However, the actuator had to be both powerful and have a large stroke. For
this test, the blade was firmly fixed at the base, a static load was placed near the tip and a hydraulicfixture driving two moderate weights up and down was mounted on a saddle about 1.6 m from thebase of the blade. The resonant frequency of the first flap mode of the blade was measured and thetwo weights were driven up and down at this frequency. At resonance, the tip of the blade had alarge amplitude swing. To conform with previous tests, the blade was tested with the low-pressure
side down and a weight mounted towards the end of the blade. This weight was adjusted so thatthat the actual load on the low-pressure side was always positive. With this geometry, the failure
was expected to be similar to that of the static test, occurring around 1 to 2 m from the base on the
low-pressure side of the blade.
A 24-channel PAC DISP system using AEWin software was used for data acquisition. The
sensors were PAC R6I, which contain a built-in preamplifier. Their response covers the bandwidthof 30 to 150 kHz, peaking around 60 kHz. The load signal was taken from a dedicated strain gauge
mounted on the surface of the blade near the driving actuators. It was calibrated with a dead weighttest load and the output amplified and fed to the DISP system. The decision about which areas of
the blade to monitor was based upon the results of the static test of the TX blade. Locations of thesensors, the driving fixture position and construction details of the blades upper and lower surfaces
are shown in Fig. 28. The low-pressure surface from 500 mm to 3500 mm was instrumented with
13 sensors. This array covers the region where failure had occurred in many of the previous 9-m
blade tests. Finite element analyses of the blade had indicated the possibility of problems aroundthe abrupt end of the shear web at 4.6 m. An array of four sensors was placed on the low-pressure
side of the blade centered around 4.6-m station and an array of three sensors was centered around
4.6 m on the high-pressure surface. Finally, an array of four sensors was located on the highpressure side of the blade just inboard of the driving fixture. These placements were based upon56

our estimation that the failure would be on the low-pressure side of the blade but because we had
not previously monitored a blade driven into resonance, we should cover all bases. The sensors
were bonded, as usual, with GE Silicone II adhesive. The sensor placement coordinates were referenced to the inboard end of the blade and the approximate center line of the blade as defined by
the center of the spar cap above and below the internal shear web. Acoustic velocities were measured on the skin behind the shear web both perpendicular and parallel to the shear web. These
velocities were quite close to each other at 3.1 mm/μs parallel and 2.9 mm/μs perpendicular andwere used in the setup of the AEWin software. They were close enough together that there was nohesitation to use 3.0 mm/μs in the location software, which assumed an isotropic acoustic velocity.
The collected data set included AE hit, arrival time, sensor number, rise time, AE count, signal
duration, signal peak amplitude and signal absolute energy. During most of the test, waveform
data was collected. The digitization rate was 2.0 MHz and the first 2 ms of each wave was digitized
and recorded. Almost all of the AE signals were shorter than 2 ms because of the high attenuationof the blade skin.
The test was monitored by several NDT methods besides AE. This required the AE system tostop taking data when the other methods were in use. At least two of the other systems generated
acoustic waves in the blade. Because of these gaps, the cycle counting feature of the software could
not be employed. All cycle data had to be obtained from the NREL operators log and the time datafrom the AE system (start, stop and running times). The inclusion of the waveform data createdhuge data files. (one hit contained 28 bytes of data without waveforms and about 8.1 kbytes including waveforms). They were sent by TCP over the internet in batches of one hundred 5 Mbyte
files at a time. Toward the end of the test, the waveform acquisition was stopped and each 5 Mbyte
file then contained about 16,135 hits instead of 600 hits with waveforms.
The test results were far from what had been expected. After minor emission on the few initial
cycles, almost all the emission seen came from the region around the end of the shear web at 4.6
m. The majority was from the high-pressure surface. Sensor 19, which was approximately overthe end of the web, showed almost continuous low-level emission from the first cycle. It wasthought at first to be rubbing noise but the waveform and its location near the peak load of eachcycle indicated that it was probably real emission. By the time the test reached 2 Mcycles, a surface
crack was developing near sensor 19. This became quite noticeable as can be seen in Fig. 29. In
the post-test analysis, it was found that data from just three sensors did not give very good locationgraphs. The two sensors, 15 and 17, while on the other side of the blade, were just past the leadingedge and were often hit in the hit sequence data. Their inclusion into the calculation much improved the location graphs. Figure 30 shows the final calculated source locations from the sametime period as the picture in Fig. 29. The absolute energy per cycle for the emissions within thetriangle formed by sensors 18, 19 and 20 was calculated from 3 to 4 Mcycles and is shown in Fig.
31. The peak between 3.78 and 3.81 Mcycles indicates where a major fracture of the blade occurred. The damaged blade continued to disintegrate until the test was stopped at 4 Mcycles toprevent the blade from complete failure, which might harm the testing apparatus.

57

Figure 28 Construction diagram and sensor layout for TX Blade fatigue test.

Figures 29. Picture of surface crack on tension side of blade around 2 Mcycles.
58

Figure 30. Final source location calculation of same area of blade around 2 Mcycles.

Figure 31 Energy rate from tension side of blade near end of shear web showing peak when major
damage started.
59

A FORTRAN program was written by the author to include an anisotropic acoustic velocity inthe calculation. A 1-m section of the TX Blade, which had been broken in the static test, wasobtained. An initial velocity measurement had shown that the parallel and perpendicular velocities
were fairly close to each other. However, off the major axes there appeared to be a much larger
variation in the acoustic velocities. It was decided to make an accurate measurement of the velocities. PLB measurements were made of the velocity in the section skin behind the shear web. An
average value for the velocity was calculated every 10˚. The result for the velocity is shown in Fig.
32. This result is quite dramatic considering that the diameter of the carbon fibers in the surface
layer of the blade is almost microscopic. It appears that most of the energy in the Rayleigh wave
created by the PLB must be contained in the surface interface with the air. The effect of the 20˚orientation of the fibers dominates. In order to include this information in the location program, ananalytic function that fits this velocity data was devised. It is included on the accompanying disk
as a subroutine. The graph in Fig. 30 was generated by this program.

Figure 32 Measured acoustic velocity of TX blade as function of angle from blade axis.
This test showed that accurate AE source location is possible on an FRP structure if enoughsensors and an accurate measurement of the velocity are available. Adding some filtering and color
coding the energies of the events improved the location graph shown in Fig. 33 over that shown in
Fig. 30, This graph pins down the location of the major flaw although visually the first graph agrees
better with the visual damage. The major conclusion of the AE test was that the only problems
seen in the blade structure were in the stressed regions where the shear web abruptly ended. Therewas strong evidence that load level at the start of the test was already producing minor damage inthese regions.
60

Figure 33 Same data as Fig. 30, with filtering. Color code black >105 MeV, red > 104 MeV.

61

Chapter 9 Example, BSDS blade
The next example of a test setup and analysis is the fatigue test of the BSDS blade [15]. The
BSDS design is a 9-m blade, which incorporates flatback airfoils in the inboard section of theblade. These airfoils give the blade a thick, structurally efficient geometry. Cross sections of theblade at 0.5 and 1.0 m are shown in Fig. 34. Not shown in the figure is the shear web down thecenter of the blade with carbon fiber reinforced spar caps on the top and bottom of the web. The
fatigue test was performed using a hydraulic cylinder to apply single point cyclic loading to theblade in both the flap and edge orientations. The loads were applied to a saddle located at a station
5.0 m from the base of the blade. Loading frequencies started at 1.5 Hz and had to be slowly
decreased as the load was increased. The final load frequency was 0.65 Hz.

Figure 34 Flat-back airfoils of BSDS blade for stations 500 and 1000 mm.
A static test of a BSDS blade was conducted and acoustically monitored. That blade proved tobe considerably stronger than previously tested 9-m blades. The static load was distributed thru
two wiffel trees to three fixtures on the blade. The inboard fixture was located at approximately
station 3 m. The failure occurred at a load of about 4.9 kN at a location between the inboard andsecond fixtures. This was unexpected so the stub of the blade was reloaded and soon failed nearstation 2 m during the first reload step. Acoustic emission from the first test indicated the start of
failure around a 4.3-kN load on the compression side of between stations 2 and 2.6 m. There were
no sensors beyond station 2.6 m on the first static test so there was no data from the region of thefirst failure. The second loading was without AE instrumentation due to severe budget restrictions.
On the first loading, one sensor was mounted on the tension side of the blade over the shear web
at station 0.97 m. It recorded about an order of magnitude more detected acoustic energy during
the test than the sensor mounted approximately above it on the compression side of the blade.
The static tests indicated that there was appreciable AE from both the compression and tension
sides of the blade and that failure could occur beyond station 3 m although a longer-term hold at
4.3 kN, could have led to failure on the compression side between stations 2.5 and 3 m. The decision was made to monitor the whole surface of the blade between station 0.5 m and station 3 m. It
was thought that the blade failure outside of station 3 m may have been due to the distributed load
applied to the outer end of the blade during the first static test and that single point fatigue loading
would probably cause failure inboard of station 3 m. A pattern of 24 sensors in six rows of four
sensors each was decided upon. The first row would be at station 0.5 m with a sensor above and62below the shear web with the other two placed on the leading and trailing edges. The next row
would be at station 1 m and would be rotated 45o around the center of the blade. This pattern would
continue out to station 3 m. This is the pattern described in the Setup chapter and is shown in Fig.
15.
At the time of this test, it was not known that the AEWin software could run two programs
simultaneously when both layouts contained the same sensor, but used different locations of thesensor in the programs. In order to display the entire surface of the blade and use the leading andtrailing edge sensors for calculations on both sides of the blade, the AE system was set up incylindrical mode, but with the actual sensor locations inserted. It was hoped that this would warp
the cylinder surface into a form equivalent to the blade. This appeared possible since the different
airfoils and construction made the blade much closer to a tube than the other 9-m blades. It would
also allow the author to set up his own programs for separate graphs of the tension and compression
sides of the blade. This approach appeared to work fairly well although it is recommended that
separate programs be used for the two sides of a blade in future tests.
The data collected by the AE system consisted of the sensor number, the arrival time of the
wave at that sensor, the amplitude of the load at the instant of arrival at each sensor, the log of thepeak amplitude of the wave, the rise time and duration of the transient wave, the AE count and thetrue energy of the waveform generated by the sensor. The digitized waveform was recorded forsome of the early data, but as was found in the TX test, the information obtained did not appear
worth the huge increase in the amount of data it would have produced. Other system settings were
the same as in the TX test. The load signal was again taken from a strain gauge mounted on theblade and calibrated with static loads. This signal was used to drive a voltage-controlled window
to allow the system to acquire data only when the load signal was over 90 % of the peak load.
Front-end filtering in place during the data acquisition removed all hits with a single count or aduration less than 50 μs.
The acoustic velocity was measured to check for anisotropy. Surprisingly the velocity wasmeasured to be isotropic at 2.5 mm/μs in the skin beyond 1.5 m from the base of the blade. Near
the base of the blade, the velocity varied from 4.40 mm/μs along the blade axis to 2.85 mm/μs
perpendicular to the blade axis. The spar caps had a velocity of 4.6 mm/μs along the blade axis but
were too narrow to worry about the velocity perpendicular to the axis. These velocities were usedin the FORTRAN programs. In the data acquisition with the cylinder program, a velocity of 2.6mm/μs was used.
The test was run first for 1 Mcycles in the normal flap mode. The blade was then rotated andanother 1 Mcycles in an edge mode where the motion was parallel to the chord of the blade. No
significant emission was seen in either of 10 % percent increases in the load were made, first at ½
Mcycle increments and then at ¼ Mcycle increments. This was continued until the blade finally
failed at level of 418 % of the initial load. This failure occurred at 6.6 Mcycles. During most of
the test, random hits were seen but no located events appeared until 4.945 Mcycles. For a shorttime, events were seen on the tension side of the blade. These soon stopped but resumed at 5.165
Mcycles and located events on the tension side were continuously seen until failure. On the compression side, located events did not appear until 5.85 Mcycles and then continued until failure.

63Figure 35 Location graphs for the tension and compression sides of the blade on the last 200
kcycles of the test.
Test results were complex. There was far more emission from the tension side of the blade.
Figure 35 shows the location graphs for both tension and compression sides of the blade for thelast 100 kcycles of the test. Essentially the whole tension side of the blade from the base to aroundstation 2 m was emitting. A very broad area from the shear web to the leading edge of the blade
and station 1 to 1.3 m appeared to be failing. The compression side shows much less emission withthe very large emissions up near the leading edge. (the three sensors at the top of both graphs are
the same.) There is one tight cluster on the compression side located very close to sensor 5. This
cluster was seen for the last 300 kcycles but did not seem to grow. The energy emission rates
showed a relatively constant rate of about 20 MeV per cycle for the clusters on the compression
side over the last 300 kcycles. On the other hand, on the tension side of the blade, the energy
release rate started at roughly 10 MeV per cycle and ended at around 10,000 MeV per cycle. The
conclusion from these results is that a minor flaw appeared on the compression side of the blade
and emitted steadily throughout the rest of the test. However, it showed no sign of growth andappeared to have no effect on the blade failure.

64

The data from the tension side of the blade showed a steady growth over the entire side between
the base and station 2 m. Figure 23 shows the total energy released over the last 100 kcycles fromthe main cluster regions on both compression and tension sides of the blade. Most of the energy
on the tension side was released in the last 12 kcycles. From the broad distribution of high energy
emissions seen in Fig. 35, the question becomes: was this failure a growing tear or was it a broad
failure over the tension surface? Figure 36 is a plot of the energy released, over the last 12 kcycles,from the whole tension surface in this region and the energy released from the smaller area containing most of the high energy events. They both have a steady slope indicating that the wholesurface area was failing and that the crack seen after the test was just part of that general failure.

Figure 36 Total energy released on tension side during the last 1200 cycles from the entire end of
the blade and just from the failure region
The conclusion reached from the AE data was that the BSDS blade had no major flaws andthat the failure occurred when material in an entire region of the blade reached the end of its fatigue
life.
Chapter 10 Example, Halon Bottle Tester
The last example to be reviewed is Acoustic Emission Halon Bottle Tester [16]. This is a commercially available system that was developed at Sandia National Laboratories. The problem to besolved was how to perform a non-destructive test on commercial aircraft Halon fire extinguisher
bottles. These bottles are spherical metal containers with diameters ranging from 102 to 380 mm.
They are filled with Halon-1301 and over-pressured with Nitrogen gas to around 4.13 MPa. After
filling, the fill port is welded shut. When needed, a squib charge is fired, breaking a rupture disk,
releasing the Halon gas into the cargo hold or engine compartment. The only accepted method of
testing these hermetically sealed bottles was cut them open, empty them and hydrostatically65overpressure them in a water bath in order to measure their expansion, elastic and inelastic. After
passing, the bottles are refilled with Halon and resealed. This procedure is expensive, time consuming and has the potential to leave the bottle in a worse position than before the test. It is also
insensitive. Most of the recent test failures occurred when the bottle exploded in the water bath.
Sandia answered an industry request to develop a better test method. Experiments showed thatover-pressurizing these bottles by heating them in an industrial oven was possible and that someof them produced detectable low-level AE when heated to around 66˚C. These results showed thefeasibility of developing an acoustic emission test for such bottles. The author developed and designed a commercial AE Halon bottle tester with the cooperation of the Air Transport Association,
the bottle manufactures, Federal Express and Physical Acoustics Corp. (PAC). A commercial system built by PAC has been in use at American Airlines for the last 20 years.
The AE test method used was the location of the sources on the sphere and the determinationof whether they were grouped in clusters. The location algorithm was based on spherical geometry,
which meant that the important variable between the bottles was the radius of the sphere. A fixture
was designed, Fig. 38, which held the bottle and let six sensors be moved in and out along radii ofa sphere. The sensors are pressed against the bottle wall by springs. They were mounted 90˚ apartat the six poles of the sphere. With this arrangement, we could use the same fixture and analysis
program for all of the different sized bottles. This fixture was designed to fit into an industrial
oven. The source location program was similar to that described in the chapter on source location.
The sensors used were the PAC nano 30. They are small, 8-mm diameter and 7.4-mm high andhave maximum sensitivity between 200 and 400 kHz. A 250 to 600 kHz bandpass filter was usedin an external preamplifier. The high frequency was chosen for two reasons. First: good location
accuracy was desired. Second: it was found that AE signals in the bottle wall could excite acoustic
waves in the liquid Halon. These would bounce around inside the bottle for relative long times
compared to an AE signal in the bottle wall. The acoustic waves usually had frequencies below
100 kHz. By going to frequencies between 300 to 400 kHz, these waves are completely ignoredby the system. As the sensors are a permanent part of the fixture an attempt was made to dry couple
them to the bottles. A thin disk of hard rubber is glued to their wear plate. This helps the sensor toconform to the slight curvature of the bottle and makes good contact. When things work right, thisis an adequate acoustic coupling, but in a production environment, a thin film of grease improved
the coupling. The AE signals in the bottles are relatively weak. The trigger threshold was set as
low as possible. With a preamplifier gain of 40 dB, a trigger threshold of 25 dB was used. With
these gain and bandpass settings, the noise level of the system starts triggering at 22 dB. A full setof AE parameters, including waveforms, were taken but by the end of the development stage, only
the arrival times at the sensors and the temperature of the bottle wall were used in the analysis, so
the waveforms were dropped from the data sets.

66

Figure 37 Acoustic Emission Halon Bottle Tester, fixture for spheres 102 to 410-mm diameter.

Figure 38 Diagram of Halon Bottle showing located events and cluster outline. This bottle failed
the test badly with 156 events in the cluster but no sign of uncontrolled flaw growth.
67

The first step in the test is to load the bottle in the fixture. There is no preferred orientation of
the sphere. The actual orientation in the fixture will vary between bottles as the operator searches
for a position where none of the sensors touch welds, ports, mounting lugs or stick on labels. Oncethe bottle is in the fixture and the system turned on, the program takes over. An automatic sensor
test (AST) is performed and if all sensors are not adequately coupled the program stops and requests that coupling be checked. The problem may be that the sensor spring tensions are too unevenor the bottle is not centered in the fixture. After adjustment, another attempt is made. The program
will not continue until the coupling meets the programs specifications. Here is where a little greaseon the sensor pad can help. When ready, the program starts the oven heaters and the AE monitoring. An AE event is when four sensors are hit within a specified time. This time is 20 % longer
than it takes for a flexural wave to go half way around the sphere. The program attempts to locate
the source using the extensional velocity (2.05 mm/μs). If that does not work, the flexural velocity(1.18 mm/μs) is tried. No attempt is made to use different velocities for different sensors. If thesource is located, it is then checked for proximity to another source. If it is closer than a 15o arc ofa great circle of the bottle, it is declared a member of a cluster. The center of the cluster is definedas the average position of all of the members of a cluster. After a cluster is defined the program
first checks for membership in all defined clusters then looks for another cluster to be defined.
Figure 38 shows a display of such a cluster on a sphere.
The problem for the analysis of this data became how to grade the clusters. In the developmentof the system, 206 bottles of all sizes had been tested. Some showed no hits but most had a few
hits and maybe one small cluster. An extensive analysis suggested that an important measure was
the number of events in a cluster. The significant signature of the cluster was the distribution of
those events as a function of temperature (which is linearly connected to internal pressure). A
strictly empirical observation was that a suspect cluster had 18 or more events occurring above
43˚C. If the events showed a pseudo exponential behavior, then the bottle was on its way to failure.
Later we added two other criteria. If the cluster contained 36 or more events above 43˚C, the bottled was declared failed, no matter what the temperature dependence of the cluster events was andif the cluster had over 70 events and showed the exponential behavior, the system would be stopped
and the bottle allowed to cool before being removed and scrapped. After the American Airlines
system had been running a few years, data from another 400 bottles was added to the original 206
and the whole set was reanalyzed. No reason was found to change these criteria. The decision onwhether cluster was growing too fast was a simple algorithm. The number of events in the cluster
which occurred between 54˚C and 75˚C was divided by the number of cluster events occurring
between 43˚C and 54˚C. If this ratio was 3.0 or greater, the bottle failed. The bottle failure rate
was about 5 % for the initial run including the American Airline bottles, some of which were up
to 30 years old. After almost all of the bottles had been tested once, the failure rate dropped to 1
% or less.
The rationale behind the AE test was to find incipient cracks in the bottles. The first flaw found
was a small piece of tungsten in a weld on a bottle. At American Airlines several of the failed
bottles were cut open. Almost all were found to have a round area on the inside where there wassurface etching. Wall thickness measurements of these areas showed a decrease in thickness up to
25% in the worst case. The explanation of these areas goes as follows. Most bottles are stored or
installed in the same position. A small amount of corroding agent, if denser than the liquid Halon,
will settle to the bottom. Over a long period of time, it will corrode a circular area whose diameter
depends upon the depth of the pool. The corroding agent is thought to arise from the slow reaction
between the Halon and traces of water in it. The most likely products of such a reaction are hydrofluoric and hydrobromic acids. The bottles are mostly stainless steel so the corrosion is a slow
68

process. Microscopic examination shows etching of the surface with exposure of subsurface
grains. The AE is produced either by flaking of corrosion products or intergranular cracking, whichhave been partially exposed. This mechanism explains the very low amplitude of emission, which
is seen.
A final note. The system has been a success. American Airlines has estimated a saving at its
bottle shop of over a million dollars a year since it has replaced the Hydrostatic test with the
Acoustic Emission Halon Bottle Tester.
Selected Reading List from J. AE
P. Ouellette, S.V. Hoa and L. Li, A Procedure for Acceptance Testing of FRP Balsa Wood Core
Pressure Vessels, J. AE, 1990, 9, 37-44.
Acknowledgments
The author would like to acknowledge the encouragement and support of Mark Rumsey towrite this monograph. The experimental work described was supported directly and indirectly by
the Sandia National Laboratories Wind Energy Department. Much of the testing was conducted at
the National Wind Technology Center near Boulder, Colorado where the aid and advice provided
by Scott Hughes, Mike Jenks, Jeroen van Dam, and Mike Desmond was invaluable. Finally, Mike
Desmond, Dale Berg, Didem Ozevin, and Alex Rinehart reviewed in detail the draft version andgreatly improved the presentation.
References[1]
J. Kaiser. Untersuchungen uber das Auftreten Gerauschen beim Zugversuch. (A Study ofAcoustic Phenomena in Tensile Tests), PhD Thesis. Technische Hochschule of München. Munich Germany, 1950, (in German); translated to English UCRL-Trans-1082(L).
[2]
A.T. Green. “Stress Wave Detection: Saturn S-II”. NASA-CR-61161, Final Report.
Aerojet-General Corporation, Sacramento Calif. Dec 1966. [NTIS: X67-18378][3]
A.G. Beattie. “Acoustic Emission, Principles and Instrumentation”, J. Acoust. Emiss.
1983, 2, 96-128.
[4]
C.S. Barrett. “A Low Temperature Transformation in Lithium”. Physical Review, 1947,
72(3), 245.
[5]Knopoff, L. Q, Review on Geophysics, 1964, 2, 625-660.
[6] a) W.G. Cady. Piezoelectricty, Vol. 1 & 2, Dover Publications, NY. 1946, 2018. b) W.P. Mason. Piezoelectric Crystals and Their Application to Ultrasonics, van Nostrand Co. Princeton
1950[7]a) A.G. Beattie. “Acoustic Emission Couplants I: the ASTM Survey”. J. Acoust. Emiss.
1983, 2, 67. b) A.G. Beattie, J.A. Barron, R.S. Algers and C.C. Feng. “Acoustic Emission Couplants II: Coupling Efficiencies of Assorted Materials”. J. Acoust. Emiss. 1983, 2, 69.
[8]
Wikipedia, “Ferroelectricity”[9]
D.A. Bell. “Noise”, in Handbook of Electronic Engineering, L.E.C. Hughes and F.W.
Holland, Chemical Rubber Co. Cleveland OH, 1967[10] a) D. Eitzen and F.R. Breckenridge, AE Sensors and their Calibration, NDT Handbook,2nd ed., vol. 5, 1987, pp. 126-130. b) F.R. Breckenridge, “Acoustic Emission Transducer Calibration by Means of the Seismic Pulse”, J. Acoust. Emiss. 1982, 1, 87.
69

[11] a) K. Ono, Calibration Methods of Acoustic Emission Sensors, Materials, 2016, 9, 508;
doi:10.3390/ma9070508. b) K. Ono, Rayleigh Wave Calibration of Acoustic Emission Sensors
and Ultrasonic Transducers, Sensors, 2019, 19, 3129; doi:10.3390/s19143129.
[12] “AEWin Software, Installation, Operation and User’s Reference Manual”, Mistras
Group, Princeton Junction, NJ[13] W.H. Press, S.A. Teukolsky, W.T. Vetterling and B.P. Flannery, Numerical Recipes in
Fortran, 2nd ed. Cambridge University Press, 1992.
[14] M.A. Rumsey, J.R. White, R.J. Werlink, A.G, Beattie, C.W. Pitchford, J. van Dam “Experimental Results of Structural Health Monitoring of Wind Turbine Blades”<http://windpower.sandia.gov/asme/AIAA-2008-1348.pdf>, 2008.
[15] D. Berry, T. Ashwill. “Design of 9-Meter Carbon-Fiberglass Prototype Blades: CX-100
and TX-100,” SAND2007-0201 September 2007.
[16] A.G. Beattie, ‘An Acoustic Emission Tester for Aircraft Halon-1301 Fire Extinguisher
Bottles”, J. Acoust. Emiss., 1997, 15: 63.
Appendix
A FORTRAN program listing which reads an AEWin data file from the fatigue test of theSandia Sensor Blade fatigue test is given in this appendix. The program was compiled in Fortran77 by an Absoft compiler and includes the use of PLplot graphics routines. It analyzes data forlocations on the compression side of the blade. Brief notes on this program follow.
The program starts with the measured positions on of all the sensors the blade. It then has a listof sensors on the tension side of the blade, which are not used in this calculation, since this program
is for the compression side of the blade. The data is then read in from a binary .DTA file. The fileis searched for the byte sequence, which defines the start of the AE data. When this is found, thefile is rewound and then positioned at the start of the data. The program then looks for hit datawith the hit parameters that are recorded in this test. A different set of parameters on a test would
require changes in this sequence. The parameters in this sequence are the test time, sensor number,(if the sensor is in the exclusion list, it goes to the next hit), rise time, count, duration, amplitude,
absolute energy and load. There is also some filtering using rise time and duration in this section(which can be omitted). The next step is to search for hits that fall in the same time window. It
accepts event hit sequences with from 3 or 4 hits to a maximum of 14 hits. The sensor number ismatched to the sensor position coordinates and a trial solution found. Here, the trial solution is theaveraged coordinates of the first three sensors hit. This starts the nonlinear least-squares programin the center of the triangle of the first hit sensors. The delta times for the event set are calculated
and the least-squares program called. If the goodness of fit parameter, RSQ, is between 0.7 and1.0 the solution is kept. It is then matched to the energy of the event (the sum of the absoluteenergies of the first three sensors hit) and checked to see if the solution lies within the selectedboundaries on the blade. (On the first run of the program, the boundaries are set for the entire
instrumented compression side surface of the blade. On later runs the coordinates of clusters foundon the first run can be used). After sorting the solution for its energy, a location graph routine iscalled. On succeeding runs, an energy vs number of cycles graph can be called for a cluster.
The only part of the nonlinear least-squares program (NLR), which pertains to the blade, is thesubroutine, FCDEMT. This routine calculates the sensor delta times for each trial solution. It uses
the sensor coordinates in the chosen geometry (planar, spherical, cylindrical with end caps) and amodel for the acoustic velocities. The model in this program goes from a higher velocity at station
0 mm to a lower constant velocity at station 1000 mm and beyond. It also includes a higher velocity
in the spar cap. At the end of this program, another version of FCDEMT is included, which corresponds to the measured velocity in the TX blade shown in Figure 32.
70

Program SSENCMP
Program ssencmp
c This program searches a SAMOS data file for the sandsen blade
tigue test and reads the hit data.
c It calculates the AE source locations for the compression
c surface using the measured velocity, and graphs the locations

c fa-

real*8 rtim,dtim,tim,const,ttim(24)
BYTE indaset(200000),tster(9),id,itm
BYTE buf2(50000),rlen(2),benr(4),dup(10000)
CHARACTER*1 buf(10000)
Integer*4 isen(15),iris(15),icnt(15)Integer*4 idur(15),iamp(15),idu
real*4 load(15),tenr,parm(2),w(6),td(6)Dimension gy(15),dy(30000),gx(30000)
CHARACTER*4 tst
CHARACTER*2048 test
EQUIVALENCE (indaset,test),(tenr,benr)
EQUIVALENCE (tst,tster),(len,rlen),(id,idd)
EQUIVALENCE (buf,dup)
Character*40 nameCommon /zot/xsen(6),ysen(6),pi
common /zat/senx(24),seny(24),nsn(9)common /zut/x1(20000),y1(20000),x2(250000),y2(250000),+x3(150000),y3(150000),x4(150000),y4(150000),x5(13000),y5(13000),+x6(5000),y6(5000),nn1,nn2,nn3,nn4,nn5,nn6!
!

coordinates of all sensors -- nsn -sensors not used on thisside of the blade
Data senx/46.,83.,0.,81.,500.,0.,0.,500.,995.,+
993.,0.,1020.,1460.,0.,0.,1475.,2080.,2055.,0.,+2060.,2473.,0.,0.,2488./
Data seny/0.0,255.,0.,-341.,140.,0.,0.,-140.,-35.,+
280.,0.,-280.,140.,0.,0.,-285.,-36.,385.,0.,-610.,+
195.,0.,0.,-278./
Data nsn/3,6,7,11,14,15,19,22,23/
const=2.5d-7
tster(1)=#07
tster(2)=#00
tster(3)=#80
tster(4)=#00
tster(5)=#00
tster(6)=#00
tster(7)=#00
tster(8)=#00
tster(9)=#00! byte sequence just before data starts

print *,'Enter the name of the input File'
Read(*,1000)name1000 Format( A40)
dtim=0.001200 ! event determination time gate
Open(1,file=name,form='binary',status='old')71

Read(1)(indaset(I),I=1,50000) ! start reading binary data file
num=index(test,tst)
print *,num
rewind(1)read(1)(buf2(i),i=1,num)
do 10 i=1,50
read(1)itm
if(itm.ne.0) thenrlen(1)=itm
go to 19
end if10 continue
nrej=0noffbld=0
n=0
in=0
nev=0
nn1=0
nn2=0
nn3=0
nn4=0
nn5=0
nn6=0
pi=3.1415927
20

continueread(1,err=512)rlen(1)19 read(1,err=512)rlen(2)read(1,err=512)id
if(idd.ne.1)thenread(1,err=512)(buf2(i),i=1,len-1)
go to 20else
read(1,err=512)(buf(i),i=1,len-1)
end ififlg=0rtim=0. !read hit parameters must agree with layout set up
do 25 i=1,6
rtim=rtim+(256.**(i-1))*ICHAR(buf(i))
tim=rtim*const
msen=ICHAR(buf(7))
do 40 i=1,9
if(msen.eq.nsn(i)) goto 20
40 continue
nflg=0mris=ICHAR(buf(8))+256*ICHAR(buf(9))
if(mris.le.5) go to 20
mcnt=ICHAR(buf(10))+256*ICHAR(buf(11))
idu=0
do 27 i=1,4
27 idu=idu+(256**(i-1))*ICHAR(buf(i+11))25

72

mdur=idu
if(mris.ge.mdur/2) go to 20
mamp=ICHAR(buf(16))
do 28 i=1,4
28 benr(i)=dup(i+16)
enr=tenr*6.7041e-6
mpr=ICHAR(buf(22))+256*ICHAR(buf(23))
if(mpr.le.32767)then
par=mpr/32767.
else
par=(mpr-65536)/32767.
end ifaload=10.*parn=n+1
in=in+1
if(in.eq.1)then! determine event members
ttim(in)=tim
isen(in)=msen
iris(in)=mris
icnt(in)=mcnt
gy(in)=enr
idur(in)=mdur
iamp(in)=mampload(in)=aload
go to 20else
if(tim.le.ttim(1)+dtim)thenttim(in)=tim
isen(in)=msen
iris(in)=mris
icnt(in)=mcnt
idur(in)=mdur
iamp(in)=mampgy(in)=enr
load(in)=aload
nflg=1if(in.eq.14) go to 50 !accept no more than 14 his in event
go to 20else
if(in.lt.4)then ! 3 hits min lt, 4 hits min lego to 35else
in=in-1
iflg=1
go to 50
end ifend ifend if
35 ttim(1)=tim
isen(1)=msen
iris(1)=mris
icnt(1)=mcnt
idur(1)=mdur
iamp(1)=mampgy(1)=enrload(1)=aload
do 31 i=2,1473

ttim(i)=0.
isen(i)=0
iris(i)=0
icnt(i)=0
idur(i)=0
iamp(i)=0
gy(i)=0.
31
load(i)=0.
in=1
go to 2050 continue
if(isen(2).eq.20)then ! sensor 20 was not working in this test
in=0
go to 20end ifif(in.gt.3) in=3
do 55 i=1,in
xsen(i)=senx(isen(i))
55 ysen(i)=seny(isen(i))
nparm=2
ndata=in-1
parm(1)=(xsen(1)+xsen(2)+xsen(3))/3.0 !starting loc estimateparm(2)=(ysen(1)+ysen(2)+ysen(3))/3.0
do 67 i=1,667 w(i)=1.0 ! weighting factors for calc not used here
do 69 i=2,in
69 td(i-1)=(ttim(i)-ttim(1))*1.e6 !delta times
CALL NLR(ndata,nparm,td,parm,w,rsq)
if(rsq.ge.0.7) then !accept results with fit parm down to 0.7
go to 80
end ifnrej=nrej+1
if(iflg.eq.1) go to 35
in=0
go to 2080 continue
x=parm(1) ! answers
y=parm(2)! accept window on blade
if(x.lt.650..or.x.gt.950..or.y.gt.250..or.y.lt.-50.) go to 90
energy=gy(1)+gy(2)+gy(3)If(energy.lt.10.)then ! ignore events with very low energy
in=0
go to 20end ifnev=nev+1
write(*,600)nev,ttim(1),x,y,rsq,energy,in,load(1),+(isen(i),i=1,3)600
format(i6,2x,f11.4,' x =',f5.0,' y =',f5.0,' r =',f4.2,+
2x,f11.2,x,i2,x,f4.2,x,3i2)! sort as to energy for graphif(energy.lt.10.) thennn1=nn1+1
x1(nn1)=xy1(nn1)=y74

go to 85
else if(energy.lt.100.)then
nn2=nn2+1
x2(nn2)=xy2(nn2)=yelse if(energy.lt.1000.)then
nn3=nn3+1
x3(nn3)=xy3(nn3)=ygo to 85
else if(energy.lt.10000.)then
nn4=nn4+1
x4(nn4)=xy4(nn4)=ygo to 85
else if(energy .lt.100000.)then
nn5=nn5+1
x5(nn5)=xy5(nn5)=ygo to 85else
nn6=nn6+1
x6(nn6)=xy6(nn6)=y
end if85 continue
gx(nev)=ttim(1)dy(nev)=energy! * 1.86 to convert to cycles

go to 91
90 noffbld=noffbld+1
91 if(iflg.eq.1) go to 35
in=0
go to 20512continue
write( *,1010)n,nev,nrej,noffbld1010 format(i8,' hits, events =',i6,' rej =',i6,'off blade =',i6)!

CALL grafel(gx,dy,nev)!energy graphprint *,nn1,nn2,nn3,nn4,nn5,nn6
CALL cgraf!location graph
END

SUBROUTINE FCDEMT(T,XY) !arrival time calculation, used in NLRCOMMON /zot/xs(6),ys(6),pi
COMMON /WNLGPH/PAD(2754),k
DIMENSION XY(2)
x=XY(1)y=XY(2)xo=x-xs(1)yo=y-ys(1)xk=x-xs(k+1)

75yk=y-ys(k+1)
cso=abs(xo/((xo**2+yo**2)**0.5))
csk=abs(xk/((xk**2+yk**2)**0.5))
d0=(xo**2+yo**2)**0.5
dk=(xk**2+yk**2)**0.5
if(x.le.500.) v=4.0
if(x.gt.500.and.x.le.1000.)then
v=2.55+((1000.-x)/500.)*1.45
else
v=2.55end ifv0=v
vk=v! velocity varies with distance from baseif(abs(y).le.75..and.abs(ys(1)).lt.75.) thenif(abs(y).le.25..and.abs(ys(1)).lt.25.) thenv0=7.0else
va=7.0-v
v0=v+va*(cos(((abs(y)-25.)/50.)*pi)/2.+0.5)
end ifend ifif(abs(y).le.75.and.abs(ys(k+1)).lt.75.) thenif(abs(y).le.25..and.abs(ys(k+1)).lt.25.) thenvk=7.0else
vb=7.0-v
vk=v+vb*(cos(((abs(y)-25.)/50.)*pi)/2.+0.5)
end ifend ifT=dk/vk-d0/v0 !calculated arrival times
RETURNEND! SUBROUTINE NLR non-linear least squares routine
SUBROUTINE NLR(ND,NPARM,DAT,PARM,WEIGHT,RSQ)DIMENSION BS(25),BA(25),YMF(550),P(25),AX(25,25),B(25)DIMENSION DAT(1),PARM(1),WEIGHT(1)COMMON /WNLGPH/ Y(550),YP(550),X(550,2),WT(550),UWP,N,K,M,I
COMMON/WNLGDB/ A(25,25),AY(25,25),DB(25),SA(25),G(25),XLL,
1XKDB,GMA,ZTA,JCL,IFDPO,IFWT
C ........ INITIALIZATION AND DATA INPUT ........
NTM = 0
N=ND
DO 1000 I=1,N
Y(I)=DAT(I)WT(I)=WEIGHT(I)1000 CONTINUE
K=NPARM
DO 1020 I=1,K
B(I)=PARM(I)1020 CONTINUE
903 FORMAT(F1.0,F10.0,F10.0)

76

M=1
NITR=26
IY=0
IFWT=1IFDPO=1
XNK = N-K
KEND=2
ITR = 0
JCL = 0XKDB = 1.0
IF (IFWT.EQ.1) GO TO 24
DO 20 I=1,N
20 IF(WT(I).EQ.0.0) WT(I)=1.0
24 CONTINUE
C ..... DUB IN CRITICAL-VALUE CONSTANTS (CHANGEABLE IN SUBZMT).
DEL = 1.0E-5
EPS = 1.0E-5
FF = 4.0
GMC = 45.0
TAU = 0.001
XL = 0.01
ZTA = 1.0E-31
CALL GETPHI (B,PHI)GO TO 62
C ........ PTP MATRIX, ITERATIONS, AND TESTS FOR FIT ........
60 ITR = ITR+1
IF (NITR.GT.1) NITR=NITR-1
XKDB = 1.0
62 DO 64 I=1,K
G(I) = 0.0
BS(I) = B(I)DO 64 J=1,K
64 A(I,J) = 0.0
C ..... FIND PARTIALS, EITHER ESTIMATED OR ANALYTIC
DO 82 I=1,N
CALL FCDEMT (F,B)
YP(I) = FYMF(I) = Y(I)-F
DO 68 J=1,K
DBW = B(J)*DEL
IF(DBW .EQ. 0.) DBW=DEL
TWS = B(J)
B(J) = B(J)+DBW
CALL FCDEMT (FWS,B)
B(J) = TWS
68 P(J) = (FWS-F)/DBW
C ..... MAKE PARTIALS MATRIX80 DO 82 J=1,K
G(J) = G(J)+YMF(I)*P(J)
1*WT(I)
DO 82 L=J,K
A(L,J) = A(L,J)+P(L)*P(J)
1*WT(I)
82 A(J,L) = A(L,J)
PHIZ = PHIIF(XNK .EQ. 0.) XNK=1.0
SE = SQRT(PHIZ/XNK)77

DO 86 I=1,K
DO 86 J=1,K
86 AX(I,J) = A(I,J)
C ..... SAVE SQUARE ROOTS OF DIAGONAL ELEMENTS
DO 98 I=1,K
if(A(i,i).lt.0.) A(i,i)=0.0
SA(I) = SQRT(A(I,I))
IF (SA(I).EQ.0.0) GO TO 94
G(I) = G(I)/SA(I)DO 92 J=1,K
92 A(I,J) = A(I,J)/SA(I)GO TO 98
94 G(I) = 0.0
DO 96 J=1,K
A(I,J) = 0.0
96 A(J,I) = 0.0
98 CONTINUE
DO 104 J=1,K
IF (SA(J).EQ.0.0) GO TO 102
DO 100 I=1,K
100 A(I,J) = A(I,J)/SA(J)102 A(J,J) = 1.0
DO 104 I=1,K
104 AY(I,J) = A(I,J)
C ..... WRITE TEST PARAMETERS AND FIND INITIAL INCREMENTS
CALL GETDBS (BS,B,XL,PHI,JUMP)
IF(JUMP.EQ.1) GO TO 700
C ..... EPSILON TEST
DO 170 J=1,K
IF (ABS(DB(J)/(ABS(B(J))+TAU)).GE.EPS) GO TO 172
170 CONTINUEGO TO 700
C ..... MAXIMUM NO. OF ITERATIONS (FORCE OFF) TEST
172 IF (NITR.NE.1) GO TO 171
GO TO 700
C ..... DETERMINE PARAMETERS FOR NEXT ITERATION
171 IF (XL.LE.1.0E-08.AND.PHI.LE.PHIZ) GO TO 60
XLS = XL/10.0
CALL GETDBS (BS,BA,XLS,PHI1,JUMP)
IF(JUMP.EQ.1) GO TO 700
IF (PHI1.GT.PHIZ) GO TO 176
XL = XLSPHI = PHI1
DO 174 J=1,K
174 B(J) = BA(J)GO TO 60
176 IF (PHI.LE.PHIZ) GO TO 60
178 XL = 10.0*XL
180 CALL GETDBS (BS,B,XL,PHI,JUMP)
IF(JUMP.EQ.1) GO TO 700
IF (PHI.LE.PHIZ) GO TO 60
IF (GMA.GE.GMC) GO TO 178
XKDB = XKDB/5.0
C ..... GAMMA EPSILON TEST
DO 182 J=1,K
IF (ABS(DB(J)/(ABS(B(J))+TAU)).GE.EPS) GO TO 180
182 CONTINUE

78

C C
C ........ CONFIDENCE LIMITS AND DATA OUTPUT ........
700 IF (JCL-1) 702,10,701
701 CONTINUE
702 CONTINUE
C ..... CALCULATE R SQUARED
SSY = 0.0
SW = 0.0
CHISQ = 0.0
DO 707 I=1,N
SSY = SSY+Y(I)*WT(I)707 SW = SW+WT(I)SW = SSY/SW
SYG = SSY
SSY = 0.0
DO 708 I=1,N
708 SSY = SSY+(Y(I)-SW)**2
if(ssy.eq.0.)ssy=0.0001
RSQ = 1.0-PHIZ/SSY
BL=B(K)
C ..... FIND AND WRITE PTP INVERSE.
CALL GJRMIX (AX,25,K,ZTA,MSNG)
IF (MSNG.EQ.2) GO TO 10
KST = 1
711 KEND = KST+4
IF (KEND.GT.K) KEND=K
IF (KEND.EQ.K) GO TO 713
KST = KST+5
GO TO 711
713 DO 715 J=1,K
IF (AX(J,J).GE.0.0) GO TO 715
GO TO 10715 SA(J) = SQRT(AX(J,J))
C ..... FIND AND WRITE PARAMETER CORRELATION MATRIX.
DO 720 I=1,K
DO 718 J=I,K
WS = SA(I)*SA(J)IF (WS.GT.0.0) GO TO 716
AX(I,J) = 0.0
GO TO 718
716 AX(I,J) = AX(I,J)/WS
718 AX(J,I) = AX(I,J)
AX(I,I) = 1.0
720 SA(I) = SA(I)*SE
KST = -9721 KST = KST+10
KEND = KST+9
IF (KEND.GT.K) KEND=K
IF (KEND.LT.K) GO TO 721
C ..... FIND AND WRITE PARAMETER ERRORS AND SUPPORT PLANES.
WS = K
DO 750 J=1,K
B(J) = BS(J)750 CONTINUE
C ..... NONLINEAR CONFIDENCE LIMITSPC = PHIZ*(1.0+FF*WS/XNK)
XNK = 2.0*(PHIZ-PC)

79

DO 780 J=1,K
ITR = 1
DO 764 L=1,2
SPL = 1.
B(J) = BS(J)+SPL*SA(J)CALL GETPHI (B,PHI)
IF (PHI.LT.PC) GO TO 756
DO 754 IY=1,9
SPL = SPL/2.0
B(J) = BS(J)+SPL*SA(J)CALL GETPHI (B,PHI1)
IF (PHI1.LT.PC) GO TO 762
754 CONTINUEGO TO 760
756 DO 758 IY=1,3
SPL = SPL+1.
B(J) = BS(J)+SPL*SA(J)CALL GETPHI (B,PHI1)
IF (PHI1.GE.PC) GO TO 762
758 CONTINUE
760 ITR = ITR+L
GO TO 764
762 WS = SPL-1.0
if(ws.eq.0.) ws=0.001
OPL = 2.0*((PHIZ+PHI1/WS)/SPL-PHI/WS)
OPU = (PHI*SPL-PHI1/SPL)/WS-PHIZ/SPL*(SPL+1.0)
B(J) = BS(J)+(SQRT(OPU**2-OPL*XNK)-OPU)*SA(J)/OPL
CALL GETPHI (B,PHI)
BA(L) = B(J)
G(L) = PHI
764 CONTINUE
780 B(J) = BS(J)790 CONTINUE
800 CONTINUE
C C
10 CONTINUE
DO 3000 I=1,K
PARM(I)=B(I)3000 CONTINUE
RETURN924 FORMAT(5X,19H... FORCE OFF AFTER,I5,10H ITERATION,A1)END
SUBROUTINE GETPHI (B,PHI)
THE SUBROUTINE CALLED BY THIS SUBPROGRAM IS
FCDEMTDIMENSION B(25)COMMON /WNLGPH/ Y(550),YP(550),X(550,2),WT(550),UWP,N,K,M,I
PHI = 0.0
UWP = 0.0
DO 302 I=1,N
CALL FCDEMT (F,B)
FMYQ = (Y(I)-F)**2
PHI = PHI+FMYQ*WT(I)302 UWP = UWP+FMYQ
RETURN

C
C

80

END
SUBROUTINE GETDBS (BS,B,XL,PHI,JUMP)THE SUBROUTINES CALLED BY THIS SUBPROGRAM ARE
GJRMIX, GETPHIDIMENSION B(25),BS(25)COMMON /WNLGPH/ Y(550),YP(550),X(550,2),WT(550),UWP,N,K,M,I
COMMON /WNLGDB/ A(25,25),AY(25,25),DB(25),SA(25),G(25),XLL,
1XKDB,GMA,ZTA,JCL,IFDPO,IFWTJUMP = 0
XLL = 0.0
DTG = 0.0
GTG = 0.0
DO 202 I=1,K
DO 201 J=1,K
201 A(I,J) = AY(I,J)
202 A(I,I) = A(I,I)+XL
CALL GJRMIX (A,25,K,ZTA,MSNG)
IF (MSNG.EQ.1) GO TO 204
JCL = 1GO TO 602
204 DO 210 I=1,K
DB(I) = 0.0
DO 208 J=1,K
208 DB(I) = DB(I)+A(I,J)*G(J)IF(DB(I) .EQ. 0.) GO TO 210
if(SA(i).eq.0.) SA(i)=0.0001
DB(I) = DB(I)/SA(I)*XKDBDTG = DTG+DB(I)*G(I)GTG = GTG+G(I)**2
XLL = XLL+DB(I)**2
210 B(I) = BS(I)+DB(I)XLL = SQRT(XLL)
IF (K.EQ.1) GO TO 214
IF(GTG .EQ. 0) GO TO 602
CGM = DTG/(XLL*SQRT(GTG))
JGM = 1
IF (CGM.GT.0.0) GO TO 212
JGM = 2
CGM = ABS(CGM)
212 if(cgm.ge.1.0) cgm=0.99
GMA = 57.2957795*(1.5707288+CGM*(-0.2121144+CGM*(0.074261-CGM*
10.0187293)))*SQRT(1.0-CGM)
IF (JGM.EQ.1) GO TO 216
GMA = 180.0-GMA
IF (XL.LT.1.0) GO TO 216
JCL = 2GO TO 602
214 GMA = 0.0
216 CALL GETPHI (B,PHI)IFDPO=1
IF (IFDPO.NE.1) GO TO 600
RETURN602 JUMP = 1
600 RETURN
END

C
C

81

SUBROUTINE GJRMIX (A,N,IM,EPS,MSING)DIMENSION A(N,N),B(25),C(25)INTEGER P(25),Q(25)C .. DETERMINATION OF THE PIVOT ELEMENT.
MSING = 1
DO 10 K=1,IM
PIVOT = 0.0
DO 20 I=K,IM
DO 20 J=K,IM
IF (ABS(A(I,J)).LE.ABS(PIVOT)) GO TO 20
PIVOT = A(I,J)
P(K) = I
Q(K) = J
20 CONTINUE
IF (ABS(PIVOT).LE.EPS) GO TO 40
C .. EXCHANGE OF THE PIVOTAL ROW WITH THE KTH ROW.
IF (P(K).EQ.K) GO TO 80
DO 70 J=1,IM
L = P(K)Z = A(L,J)
A(L,J) = A(K,J)
70 A(K,J) = Z
C .. EXCHANGE OF THE PIVOTAL COLUMN WITH THE KTH COLUMN.
80 IF (Q(K).EQ.K) GO TO 90
DO 100 I=1,IM
L = Q(K)Z = A(I,L)
A(I,L) = A(I,K)
100 A(I,K) = Z
C .. JORDAN STEP.
90 DO 110 J=1,IM
IF (J.NE.K) GO TO 130
B(J) = 1.0/PIVOT
C(J) = 1.0
GO TO 140130 B(J) = -A(K,J)/PIVOT
C(J) = A(J,K)
140 A(K,J) = 0.0
110 A(J,K) = 0.0
DO 10 I=1,IM
DO 10 J=1,IM
10 A(I,J) = A(I,J)+C(I)*B(J)C .. REORDERING THE MATRIX.
DO 155 M=1,IM
K = IM-M+1
IF (P(K).EQ.K) GO TO 170
DO 180 I=1,IM
L = P(K)Z = A(I,L)
A(I,L) = A(I,K)
180 A(I,K) = Z
170 IF (Q(K).EQ.K) GO TO 155
DO 150 J=1,IM
L = Q(K)82

Z = A(L,J)
A(L,J) = A(K,J)
150 A(K,J) = Z
155 CONTINUEGO TO 151
C .. WRITE MESSAGE IF MATRIX IS SINGULAR.
40 CONTINUE
MSING = 2
151 RETURN
END! GRAPHICAL ROUTINES USE PLplot graphics library
SUBROUTINE grafel(eld,enr,nev)
dimension xx(100000),x(200000),y(200000),eld(1),enr(1)do 10 i=1,nev
10 xx(i)=eld(i)do 20 i=1,nevx(2*i-1)=xx(i)x(2*i)=xx(i)y(2*i+1)=enr(i)+y(2*(i-1))y(2*i)=enr(i)+y(2*(i-1))
20 continuey(1)=0.
x(2*nev+1)=xx(nev)ymax=1.05*y(2*nev)
xmin=0.
!
xmax=1.05*eld(nev)xmax=xx(nev)*1.05!
xmin=0call plinit()
call plcol0(15)call plenv(xmin,xmax,0.0,ymax,0,0)
call pllab('Time - Sec','Total Energy - MEV',+ ' Compression Side')
nn=2*nev
Call plline(nn,x,y)Call plend
returnend
SUBROUTINE cgraf
dimension mclr(6),snx(15),sny(15)common /zat/senx(24),seny(24),nsn(9)common /zut/x1(20000),y1(20000),x2(250000),y2(250000),+x3(150000),y3(150000),x4(150000),y4(150000),x5(13000),y5(13000),+x6(5000),y6(5000),nn1,nn2,nn3,nn4,nn5,nn6
data mclr/14,9,3,11,1,15/ ! colors - salmon,blue,green,cyan,red,! black
n=0
do 10 i=1,24
do 9 j=1,983

if(i.eq.nsn(j))go to 10
continuen=n+1
snx(n)=senx(i)sny(n)=seny(i)10 continue
call plinit()
9

call plcol0(15)call plschr(0.,1.0)
call plenv(650.,950.,-50.,250.,1,1)
call pllab('X - mm','Y - mm','compression side')call plcol0(mclr(1))
call plpoin(nn1,x1,y1,42)
call plcol0(mclr(2))
call plpoin(nn2,x2,y2,42)
call plcol0(mclr(3))
call plpoin(nn3,x3,y3,42)
call plcol0(mclr(4))
call plpoin(nn4,x4,y4,42)
call plcol0(mclr(5))
call plpoin(nn5,x5,y5,42)
call plcol0(mclr(6))
call plpoin(nn6,x6,y6,42)
call plcol0(15)call plschr(0.,3.)
call plpoin(15,snx,sny,79)
call plend
returnend

SUBROUTINE FCDEMT for TX blade:Contains acoustic velocity calculated for Figure 32.
SUBROUTINE FCDEMT(T,XY)
COMMON /zot/xs(5),ys(5)COMMON /WNLGPH/PAD(2754),k
DIMENSION XY(2)pi=3.14159265
of20=0.3490659
x=XY(1)y=XY(2)xo=x-xs(1)yo=y-ys(1)xk=x-xs(k+1)
yk=y-ys(k+1)
cso=acos(xo/((xo**2+yo**2)**0.5))
csk=acos(xk/((xk**2+yk**2)**0.5))84

if(yo.gt.0.0) thenango=cso-of20
else
ango=cso+of20
end ifif(yk.gt.0.0) thenangk=csk-of20
else
angk=csk+of20
end ifif(xo.lt.0.0) ango=pi-angoif(xk.lt.0.0) angk=pi-angk
if(ango.le.of20)then
v0=2.10+2.40*exp(-(2.3851*ango)**2.0)
else
v0=2.10+2.40*exp(-(2.4062*abs(ango))**1.15)
end ifif(angk.le.of20)then
vk=2.10+2.40*exp(-(2.3851*angk)**2.0)
else
vk=2.10+2.40*exp(-(2.4062*(angk))**1.15)
end ifd0=(xo**2+yo**2)**0.5
dk=(xk**2+yk**2)**0.5
T=dk/vk-d0/v0
RETURN
END

85

Relating Damage Assessed by Using Intensity Signal Analysis of Acoustic
Emission with Surface Strain in Concrete and Ultrasonic Pulse Velocity in
Reinforced Concrete Beams under Monotonically Increasing Load
R. Vidya Sagar1 and Swarnangshu Ghosh2
1

Department of Civil Engineering, Indian Institute of Science, Bangalore-560012, India(rvsagar@iisc.ac.in), 2Department of Civil Engineering, National Institute of Technology,
Surathkal -575025, India (swarnangshughosh.171cv146@nitk.edu.in)
Abstract
This article reports on the assessment of damage state of reinforced concrete (RC) beams using
the intensity signal analysis (ISA) based on acoustic emission (AE) testing. The influence of shear
reinforcement in RC beams on damage developed was studied. There are few AE based studies
related to distinguishing the damage (accumulation of microcracks) due to tensile and shear
cracking in RC beams. The aim is to relate the surface strain in concrete and the corresponding
damage as the load is increased monotonously. The damage developed in RC beams was
represented with five levels namely Level - A (indication that insignificant AE has been detectedin the region), Level - B, Level - C, Level - D, and Level - E (major defects detected and use of
RC structural member is not recommended). The surface strain in concrete reached its maximum
value quickly in shear failure. The AE sensor (s) present in the location where severe damage
occurred, recorded higher number of AE signals than the other sensors. At the same location,
ultrasonic pulse velocity decreased considerably. The surface strain in concrete reached its
maximum value at the same region. The results reported in this article may be an improvement
related to the knowledge on ISA of AE to monitor damage in RC structures.
Keywords: Damage, reinforced concrete, strain, shear failure, ultrasonic pulse velocity
1. Introduction
Generally, for non-destructive testing of reinforced concrete (RC) structures, visual inspection
is a preliminary method which engineers use in-situ. There are chances of occurrence of subjective
and potentially inaccurate assessments with regards to decisions on a structure’s damage status,
in-situ. It can thereby pose doubts about safety and in some cases can lead to structure’s failure or
sudden collapse. In general, damage can be defined as accumulation of microcracks in a reinforced
concrete structure. To determine whether the RC structure is suitable for its designed use, it isoften necessary to assess damage in the same after 30 - 35 years. From the recorded acoustic
emission (AE) waveforms or parameters, it is possible to identify the AE source location (s), typeof cracks (tensile/shear), characterisation of the cracks (micro/macro), and to assess the status ofdamage in the existing RC structures, in real time [1-5].
1.1 Brief Literature Review
Researchers introduced several AE-based techniques or methods to assess damage level in RC
structures (both in laboratory and in-situ) by using AE waveforms or AE signal parameters [6].
Fowler et al. [7, 8] introduced intensity signal analysis (ISA) of AE to assess the damage status infiber reinforced polymers. Researchers attempted to study the application of ISA of AE for realJ. Acoustic Emission, 37 (2020)

86©2020 Acoustic Emission Grouptime damage monitoring of members in concrete bridges [9,10]. Shahidan et al. [11] studied thetype and level of damage using RA-AF analysis and ISA based analysis of AE, where
RA = (Rise time, RT)/(Peak amplitude of an AE signal) and
Average frequency (AF) = (AE counts)/Duration.
An RC portal frame was tested in the laboratory and ISA of AE was implemented to study thedamage process [12]. To date most of the studies on ISA of AE are related to damage assessmentin RC structures. The influence of shear reinforcement on damage in RC beams when subjected tostatic loading using ISA of AE has not been studied extensively. Also, by connecting the surface
strain in concrete and damage assessed by using ISA of AE, the confidence on this approachincreases.

Fig. 1. Schematic representation of various levels of damage in Intensity Signal analysis of
Acoustic emission; Level A: no damage; Level B: damage detected; Level C: Minor damage;
Level D: Major damage; Level E: Severe damage [7, 9 - 11].
1.2 Acoustic Emission based Intensity Signal Analysis
The ISA of AE is a mathematical method for evaluating the damage in both composite and RC
structures in laboratory and in-situ [7-10]. It results in damage level in RC structure as shown in
Fig. 1. The details about the damage intensity levels recommended by ISA of AE are given in
Table 1. This approach uses an AE parameter, namely, signal strength (picovolt-s), which is thetime integral of absolute signal voltage. AE energy (Volt-s) is the area under rectified signal
envelope. AE absolute energy (aJ) is the square of signal voltage at the sensor before amplification,
divided by 10 kΩ impedance [3, 4, 13]. Signal strength is derived from Eq. 1, and is therefore
proportional to AE energy.
𝑆𝑖𝑔𝑛𝑎𝑙 𝑆𝑡𝑟𝑒𝑛𝑔𝑡ℎ ∝ ∑"#$(1)%#$ (|𝑉! |∆𝑡)where FTC is the first threshold crossing, LTC is the last threshold crossing of an AE waveform,
Vi are the successive readings of the signal voltage and Δt is the time interval between them. Two
AE indices namely historical index (HI) and severity index (Sr) are used in ISA of AE. HI
87

Table 1. Damage classification based on AE intensity levels [8, 10].
AEintensity
levelsA
B

Observation on the test specimen during
the experiment
Insignificant acoustic emission generated.
Minor surface defects such as cracking due
to corrosion.

Indication

Recommendations

No damage

Note for reference infuture tests.
Requires follow-up
evaluation. Evaluation
may be based on further
data analysis orcomplementary nondestructive examination.
Requires follow-up
inspection.
Requires immediate shutdown and follow-up
inspection.

Damage detected

C

Defects appeared.

Minor damage

D

Significant defect developed.

Major damage

E

Major defect developed.

Severe damage

determines the change of AE signal strength rate. Particularly, it measures the slope change of thecurve plotted between cumulative signal strength and time [9, 11].
&

∑#

*

!"
𝐻𝐼 = &'( ∑"$%&'
# *

(2)

"$' !"

where N is number of the AE hits, Soi is signal strength of the ith hit, K is an empirical constantbased on the material used to cast the test specimen. Severity index (Sr) is the mean signal strength
among the largest numerical value of AE signals.
+
Sr = , ∑,./+ 𝑆-.
(3)

where J is an empirical constant based on test specimen material, Som is the signal strength of the mth hit
where the order of m is based on magnitude of the signal strength. For concrete, K and J are related to N as
shown in Table 2 [10, 11].
Table 2. The relation between values of N, K and J for concrete [10].
N (number of hits
recorded by eachchannel)< 50
50 < N< 200
200 < N < 500
N > 500

N and K values
K (constant)
0
N-30
0.85N
N-75

88

J (constant)050

1.3 Aim of the study
RC beams, with and without shear reinforcement, were tested in laboratory to study the damage
by using ISA of AE. The results of this experimental study may result in a better understanding
about the damage which occurred due to tensile failure and shear failure. During tensile failure of
RC beams, vertical cracks appear at the location where maximum bending moment is developed,
and diagonal shear cracks appear near the supports when the beam reaches yielding and collapse
stages. Also, the surface strain in concrete is connected with the damage level. The outcome of thestudy may be useful to identify the damage accumulated in RC structure under static loading.
Subsequently, the engineer can plan for the implementation of suitable retrofitting methods (toextend the life span for further usage) to the RC structure.
2. Experimental program
2.1 Materials
Four RC beams with rectangular cross-section were tested in laboratory under monotonously
increasing loading and simultaneously the generated AE during fracture process in the same wererecorded. Ordinary Portland cement, river sand, coarse aggregate (20 mm) and water were used tocast the test specimens in the laboratory. The 28-day compressive strength of the concrete used tocast the RC beam specimens are given in Table 3.
2.2 Test specimen
The steel reinforcement details, geometrical details (dimensions) of the test specimens are
given in Fig. 2. The cross-sectional area of tensile steel reinforcement in each RC beam specimen
is 276.32 mm2. All RC beams (Specimen - RF1, Specimen - RF2, Specimen - RS1 and Specimen- RS2) were tested under monotonously increasing loading and the loading rate is mentioned inTable 3. Specimens RF1 and RF2 were cast with the same batch of concrete, but specimens RS1
and RS2 were cast with different batches of concrete. The RC beams were cast in wooden moulds
and the specimens were demoulded after 24 hours. The RC beams were cured with water for 28
days. Steel stirrups as shear reinforcement were provided in two test specimens (RF1 and RF2).
The spacing of the steel stirrups in RF1 and RF2 were 160 mm. The other two specimens (RS1and RS2) which did not have shear reinforcement were also tested. These two RC beams specimens
were tested in order to distinguish the damage computed using ISA of AE in the absence of shear
stirrups.
2.3 Test setup
The experimental setup consisted of a servo-controlled hydraulic machine of 1200 kN capacitywith a data acquisition facility. All RC beam specimens were tested in Structures Laboratory,
Department of Civil Engineering, Indian Institute of Science, Bangalore, India, under four-point
bending. The longitudinal surface strain in concrete at (i) close to left support [Section - I], (ii)
mid-span [Section - II], (iii) close to right support [Section - III] at mid height of the beam were
measured manually with a Demec gauge over 99 mm gauge length, at different time intervals
during testing. The location coordinates where the surface strain in concrete were recorded areshown in Table 3.
2.4 AE monitoring setup
The AE signal parameters were recorded using an eight channel AE monitoring system during
the fracture process of the RC beams. Resonant type differential AE sensors (57 kHz) withpreamplifier gain of 40 dB were used. High vacuum silicon grease was used as coupling agent to90

Fig. 2. Schematic diagrams of (a) simply supported RC beam having shear reinforcement (top);(b) simply supported RC beam without shear reinforcement (bottom).

Fig. 3. Schematic diagram of AE sensors on RC beams (a) RF1, RF2, RS1. (b) RS2 (The small
black circles on the surface of the beams represent the sensors).
mount the sensor on the RC beam specimen. A gum tape was used to tie the AE sensor to the test
specimen at the desired location. A fixed threshold of 39 dBAE was set to screen out surrounding
noise. AE system monitoring parameters namely peak definition time of 200 μs, hit definition timeof 800 μs and hit lockout time of 1000 μs were set. The sensor locations on test specimen are
shown schematically in Fig. 3. The frequency bandwidth of the preamplifier operating connectedwith AE sensors was 20 kHz-1200 kHz. The AE sensor had a good sensitivity and frequency
response over the range of 35 kHz-100 kHz. As per calibration chart of each AE sensor supplied91

by the manufacturer of the AE system, authors concluded that sensors had good sensitivity andnearly the same response. The 2D planar location (X and Y coordinates) of the AE sensors on the
RC beam specimens are given in Table 4. The preamplifier gain was set to 40 dB and the sampling
rate was 1 MHz. The total AE energy released was calculated by summing up the AE energy
recorded by the channels used. The study on AE event locations (or source locations) has not been
attempted.
2.5 Ultrasonic Pulse Velocity (UPV) Measurement
Ultrasonic pulse velocities were recorded in the RC beams before and after testing using
PUNDIT (Portable Ultrasonic Non-Destructive Digital Indicating Tester) equipment. Transducersof 54 kHz frequency were used. To ensure the proper transmission of the ultrasonic pulse between
transmitter and receiver, petroleum grease was used as a coupling agent between the concretesurface and face of each transducer. Both the front and back surfaces (2D plane) of the RC beam
were represented with a grid consisting of 3 rows and 23 columns to measure the UPV. Directtransmission arrangement method was used to record the UPV across the RC beam. The path
length was 150 mm. However, the corrections to the UPV recordings due to influence of steel
reinforcement were not included.
3. Results and Discussion
3.1 AE characteristics of RC beams under monotonously increasing load
It is known that AE is a phenomenon where transient elastic stress waves are generated during
fracture process in solids. The AE waveform parameters namely absolute energy, counts, signal
strength, hits, AE energy recorded during the fracture process in RC beams are shown in Table 5.
The influence of shear reinforcement on load carrying capacity can be observed from Fig. 4. The
specimen (RF2) having shear reinforcement exhibited elasto-plastic behaviour during
monotonously increasing loading. However, specimen RS2 exhibited brittle failure after reaching
peak load and failed suddenly. In the same figure, the variation with time for specimen RS2 andRF2 can be observed. And also, a constant bending moment and zero shear force are observed in
the mid span as shown in Fig. 5. Fig. 6a shows the variation of cumulative AE hits with time forbeams RF2 and RS2. A larger number of AE hits were generated for tensile failure than shear
failure. The reason could be attributed to a greater number of micro-cracks which occurred in thespecimen with shear reinforcement, than in the specimen without shear reinforcement. Fig. 6b andFig. 6c shows the variation of cumulative signal strength recorded by all the AE sensors with time
for beams RF1 and RS1 respectively. From Fig. 6b it can be observed that the cumulative signal
strength curve increased linearly till failure of the specimen RF1 (with shear reinforcement). The
cumulative AE signal strength curve in Fig. 6c is divided into five regions namely 0 - a1, a1 - a2, a2- a3, a3 - a4, and a4 – a5. In the first region, there is no rise in the cumulative signal strength curve
indicating fracture process has not begun. In the region a1 – a2, there is a rise in the cumulative
signal strength curve which indicates minor cracking has started. Towards the end of this intervalfrom 190 s, there is a steep rise in the curve which might indicate commencement ofmacrocracking. From a2 – a3, the rise was linear. In the region a3 – a4, when the load wasapproaching the peak, the rate was very high indicating the higher generation of AE signals during
the collapse. In the last region, the curve rose linearly till failure. The load also suddenly dropped
without warning. Even though the load carrying capacity is almost equal for specimens RF1 andRS1, but the cumulative signal strength is higher for specimen RF1 than RS1 which indicates thatspecimen RF1 has more capacity to absorb strain energy during fracture process. The reason could
be due to the presence of shear reinforcement. Fig. 7a and b show the variation of AE energy withtime for specimens RF2 and RS2. At the instant in which specimen RF2 started yielding,92Fig. 4. Load variation with time for RC beams RF2 and RS2.

Fig. 5. (a) Schematic diagram of RC beam; (b) Free body diagram; (c) Shear Force diagram; (d)Bending Moment diagram of the RC beam [P refers to arbitrary load acting on the beam].
93

(b)

Fig. 6. (a) Cumulative AE hits versus time for specimens RF2 and RS2; (b) Variation of AE signal
strength with load and time for specimen with shear reinforcement; (c) Variation of AE signal
strength with load and time for specimen without shear reinforcement.
94

Fig. 7. Variation of load with AE energy for (a) Specimen RF2; (b) Specimen RS2.
considerable AE energy (1.5 × 104 volt-s) is released. However, after yielding, the specimen
withstood loading till 6900 s, but didn’t collapse. At collapse, very high AE energy (2.56 × 104
volt-s) signals were released. But in case of specimen RS2, the elastoplastic behaviour was absent
and higher energy signals (5.5 × 104 volt-s) were released at collapse.
3.2 Damage assessment in RC beams with shear reinforcement using ISA of AE
By following [10, 11], a MATLAB program was developed to compute HI and Sr indices. The
total number of AE hits recorded during fracture process in specimen RF2 were separated channelwise to execute ISA of AE. The loading stages are divided into 4 intervals namely 0 - 0.2Pmax, 0 0.5Pmax, 0 - 0.8Pmax and 0 - Pmax, where Pmax indicates peak load. Subsequently, the AE hits foreach channel are separated from Ntotal (Ntotal refers to total number of hits recorded from 0 - Pmax).
The first loading stage (0 - 0.2Pmax) and the hits corresponding to this stage are considered. By
using Table 2, the expression ∑&!/(0+ 𝑆-! is computed. The values of K and J are then computed
based on N. This is followed by computing the expression ∑&!/+ 𝑆-! . The values of HI and Sr arethen calculated by following Eq. 2 and Eq. 3 respectively. In fact, Sr is the mean of the first fifty
AE signal strength values of hits of the corresponding interval arranged in descending order. The
same process should be repeated for the other loading stages namely 0 - 0.5Pmax, 0 - 0.8Pmax and95

Fig. 8. Intensity signal analysis of AE up to (a) 20%; (b) 50%; (c) 80%; (d) 100% of peak load for specimen RF2.

96

0 - Pmax. After computing HI and Sr for each loading interval, a log-log graph between HI and Sr
is plotted and five levels of damage are introduced.
The AE indices HI and Sr are computed for the generated AE. By following IS 456-2000, thein-service design load for the beam RF2 is 52.26 kN [14]. From Fig. 8a, it can be observed that
Channel - 5, Channel - 6 and Channel - 7 fall in Level - B at 20% of peak load which indicatesminor surface defects whereas Channel - 8 falls in Level - A. The maximum surface strain in
concrete recorded at mid-span (Section - II) at this level of loading is 0.00011 as shown in Fig. 8a.
Upon increasing the loading level to 50% of peak load, Channel - 5 and Channel - 7 now moved
to Level - C and Channel - 8 moved to Level - B as shown in Fig. 8b. The reason could be thefracture process is relatively more severe compared with previous level of loading (0 - 20% of
peak load). The surface strain at this level at Section - II is 0.000502. At 80% of peak load, Channel- 5 just reached Level - D which indicates major damage in its vicinity and the other channels wereat Level - C as shown in Fig. 8c. The surface strain at mid-span (Section - II) at this load level is
0.000895. Upon reaching the peak load, as can be observed from Fig. 8d, Channel - 4 reached
Level - A indicating no damage in its surrounding whereas the other channels reached Level - D
indicating major damages occurring in various locations of the beam and need follow up
inspection. The strain at peak load at Section - II is 0.001523. Even though Channel - 4 is at midspan, it didn’t record noticeable number of AE signals because the AE sensor got detached fromthe test specimen in the middle of the experiment. Figure 9a shows variation of load with surface
strain for RF2. The crushing of concrete at mid-section can be observed from Fig. 9b. The UPV in

(a)

(b)

Fig. 9. (a) Variation of load with surface strain for RF2; (b) RC beam RF2 after experiment.
97

Fig. 10. (a) Variation of UPV along the span of the beam RF2; (b) AE hits recorded by sensors
mounted on specimen RF2.
specimen RF2 before damage is approximately equal to 3.8 km/s and this reflects good quality ofconcrete as per IS: 516 (Part 5/ Sec 1): 2018: Hardened concrete - methods of test as shown in Fig.
10a [15]. Only the bottom row was chosen because the UPV values before testing did not show
considerable fluctuation whereas the top and middle rows showed significant fluctuation in the
UPV values before testing. The changes in UPV values along the span and the channel-wise AE
hits recorded are shown in Fig. 10a and Fig. 10b respectively [16]. It is interesting to note that thechannel location where maximum damage occurred, UPV decreased to a minimum value. The
reason could be when an ultrasonic pulse travels through a concrete-air interface, there is negligible
transmission of sound energy across this interface. Thus, any air-filled void or crack lying between
ultrasonic pulse transmitter and receiver obstructs the transmission of ultrasonic pulse, when the
projected length of the void/crack is greater than the width of transducer and the wavelength of theultrasonic pulse in concrete [17]. When this happens, the first ultrasonic pulse to arrive at receiving
transducer is diffracted around the periphery of the crack/void. It results in the increase of transit
time. Hence UPV decreased due to development of crack. Figs. 11a-d shows the results of ISA of
AE related to specimen RF1. Specimen RF2 was tested at a higher loading rate than specimen
RF1. The in-service design load for beam RF1 is 58.25 kN [14]. Fig. 12 shows the surface strain
at mid-span at mid height of the beam (Section - II). The strain at peak load is 0.0028 as can beseen from Fig. 12. The specimen started yielding when the surface strain reached 0.002823 at
98

(a)

(b)

(c)

Fig. 11. Intensity signal analysis of AE up to (a) 20%; (b) 50%; (c) 80%; (d) 100% of peak load for specimen RF1.

99

Fig. 12. Variation of load with surface strain for RF1.

Fig. 13. (a) Variation of UPV along the span of the beam RF1; (b) AE hits recorded by sensors
mounted on specimen RF1.
100

Section - II. In specimen RF1, the UPV along the span of the beam for the bottom row is 4 km/s
before damage as shown in Fig. 13a. The UPV values before testing for top and middle rows
showed high fluctuation. The UPV reduced considerably in the region near Channel - 5 as can beobserved from Fig. 13a. This can be confirmed from Fig. 13b where it can be observed thatmaximum number of hits were also recorded by Channel - 5 and it reached Level - E as shown in
Fig. 11d.
The surface strain in concrete is maximum at Section - II in case of specimens RF1 and RF2.
The reason could be maximum bending moment takes place at Section - II. However, for
specimens RS2 and RS1, the maximum surface strains were observed at Section - I and Section –III, respectively.

Fig. 14. Intensity signal analysis of AE up to (a) 20%; (b) 50% of peak load for specimen RS2.
3.2 Damage assessment in RC beams without shear reinforcement using ISA of AE
The total number of AE hits were separated channel-wise for specimen RS2 to implement ISA
of AE. The in-service design load for beam RS2 is 53.4 kN [14]. From Fig. 14a, it can be observedthat Channel - 7 falls in Level - B at 20% of peak load which indicates minor surface defects
whereas Channel - 6 falls in Level - A indicating smaller number of AE hits recorded in that region.

101

Fig. 14. Intensity signal analysis of AE up to (c) 80%; (d) 100% of peak load for specimen RS2.
At this level of loading only Channel - 6 and Channel - 7 recorded sufficient AE to compute HI
and Sr. The other channels namely Channel - 8, Channel - 4, Channel - 5 have not recorded
sufficient AE to compute HI and Sr. The reason could be because of negligible fracture process
around those sensors. At 20% of peak load, the maximum strain at section near to left side support(Section – I) is 0.0000471 as shown in Fig. 14a. Upon increasing the loading level to 50% of
peakload, Channel - 7 moved to Level - C as shown in Fig. 14b. Channel - 5 and Channel - 6 now
fell in Level - B. The surface strain in concrete at this loading level at Section - I is 0.0002512. At
80% of peak load, Channel -7 just reached Level - D while Channel - 6 and Channel - 5 reachedthe upper reaches of Level - B as shown in Fig. 14c while Channel - 4 and Channel - 8 entered
Level - A. The surface strain in concrete measured at Section - I was 0.001863 as shown in Fig.
15a. Upon reaching the peak load, Channel - 7 remained at Level - D whereas Channel - 5 andChannel - 6 reached Level - B. However, Channel - 4 and Channel - 8 remained at Level - A,
indicating insignificant AE activity. The maximum surface strain in concrete measured at Section- I was 0.0024 and the specimen collapsed suddenly with the formation of a major diagonal crack102

Fig. 15 (a) Variation of load with strain for specimen RS2; (b) Developed diagonal shear crack inSpecimen RS2.
as shown in Fig. 15b. Also, the UPV suddenly decreased at the location where the major diagonal
shear crack was formed as shown in Fig 16a. Only the middle row is shown because the UPV
before test values showed very high fluctuation for the other two rows. Due to the presence of
crack near to Channel - 5, AE activity was high as shown in Fig. 16b. At the same location, UPV
attained its minimum value.
Figure 17 shows ISA of AE plots for specimen RS1. The in-service design load for beam RS1
is 47.6 kN [14]. Specimen RS1 was tested under displacement control at a rate of 0.04 mm/s.
However, Specimen RS2 was tested at a lower rate of 0.005 mm/s. From the beginning of theexperiment till 20% of peak load, sufficient AE was not recorded to compute HI and Sr indices as
the rate of loading was relatively higher. From Fig. 17a, it can be observed that Channel - 4,
Channel - 6 and Channel - 8 fall in Level - B at 50% of peak load whereas Channel - 7 and Channel- 5 fall in Level - C. From Fig. 17b, upon increasing loading level to 80% of peak load, it can beobserved that Channel - 4, Channel - 6, and Channel - 8 move to Level - C whereas the otherchannels remained at Level - C. Upon reaching peak load, Channel - 6 and Channel - 8 move to
Level - E which indicates propagation of a macro crack and indicates severe damage in the RC
beam as shown in Fig. 17c. The remaining channels fell in Level - D which indicates significant
damage in the regions around those channels. It is known that irreversible changes are the cause
behind generation of AE during fracture process in solids [1-5]. In case of RC structures, thechanges are micro cracking initiation, coalescence of microcracks, propagation of crack,
debonding between steel bar and surrounding concrete. The fracture process of concrete is a103

Fig. 16 (a) Variation of UPV along the span of the beam RS2; (b) AE hits recorded by sensors
mounted on specimen RS2.
complex phenomenon and depends on several parameters [18]. In case of hardened concrete,
fracture process depends on tensile stresses developed in concrete. The tensile stresses vary frompoint to point in concrete medium. In the present case, in specimen RS1, diagonal crack developed
near the right support (Section - III). At the same location where major diagonal crack developed,
Channel - 6 was present. It is interesting to note that UPV value is nearly 4 km/s along the span of
the beam before damage occurred in the specimen. Due to the damage in the beam after the fracture
process, values in the top and middle rows could not be recorded properly and hence only thebottom row values are shown. However, the UPV drastically reduced at same location as shownin Fig. 18a where diagonal crack developed. Even though Channel - 4 recorded maximum numberof AE hits, the amplitude of those hits is low as shown in Fig. 18b. It is also observed that a greaternumber of low amplitude AE hits are recorded than high amplitude hits during the fracture process
in specimens which reflects the Gutenberg - Richter empirical relation [19, 20]. The diagonal shear
crack that was developed in the specimen RS1 is shown in Fig. 18c. Although this observation is
known, it confirms previous researchers’ observation [16]. The observations provide a reference
for conducting non-destructive testing of RC structures in laboratory.

104

3.3 Practical Significance
Generally, in a RC structure, beams transfer the loads along their length from floors and slabs tocolumns. The damage assessment of these structural elements is important as cracking affects
structural performance such as serviceability and durability. After few years of service, in theseRC structural members, damage takes place due to aging, environmental conditions, and heavyloads. The ISA of AE is a suitable NDT method to assess damage in existing RC structures. By
connecting surface strain in concrete with damage level assessed using ISA of AE, the confidence
level on this method increases.
4. Conclusions
The above experimental study primarily focused on ISA of AE to understand the damage
which developed in the presence and absence of shear reinforcement in the RC beams. The major
conclusions are given below.
1. At collapse, higher energy AE signals are released for the RC beams without shear
reinforcement than the beams with shear reinforcement. The reason could be due to sudden
development of major diagonal shear crack due to lack of shear reinforcement.
2. More AE is generated in RC beam specimens with shear reinforcement and less in RC beamspecimens without shear reinforcement. RC beam specimen without shear reinforcement failed
quickly. But RC beam specimen with shear reinforcement failed gradually and exhibited elastoplastic behaviour.
3. As surface strain in concrete increased during the fracture process, the damage reached higher
levels. At the location where the UPV value reduced significantly, the AE sensor present near thesame location recorded maximum AE hits.
By connecting the surface strain in concrete of the RC beams and the damage level, the results of
ISA method can be relied upon. Hence, ISA of AE is a useful method for assessing the safety andintegrity of RC structures in a laboratory.
References
1. C.U. Gross, M. Ohtsu, eds. (2008), Acoustic Emission Testing, Springer-Verlag, Berlin,
Germany.
2. M. Ohtsu (1998) Basics of acoustic emission and applications to concrete engineering,
Mater. Sci. and Res. Int., 4, 131-140.
3. RILEM TC 212-acd (2010a), Acoustic emission and related NDE techniques for crack
detection and damage evaluation in concrete, Measurement method for acoustic emission
signals in concrete, Mater. and Struct., 43, 1177-1181.
4. RILEM TC 212-acd (2010b), Acoustic emission and related NDE techniques for crack
detection and damage evaluation in concrete, Test Method for damage qualification ofreinforced concrete beams by AE, Mater. and Struct. 43, 1183-1186.
5. RILEM TC 212-acd (2010c), Acoustic emission and related NDE techniques for crack
detection and damage evaluation in concrete, Test method for classification of active cracks
in concrete structures by acoustic emission, Mater. and Struct. 43, 1187–1189.
6. A. Behnia, H. K. Chai, T. Shiotani (2014). Advanced structural health monitoring of
concrete structures with the aid of acoustic emission. Constr. Build. Mater. 65, 282-302.

107

7. T.J. Fowler, J.A. Blessing and P.J. Conlisk, New directions in testing, Proc. 3rd
International Symposium on AE from Composite Materials, Paris, France, 1989.
8. J. A. Blessing, T. J. Fowler and F. E. Strauser. Intensity analysis. In: Proc., 4th International
Symposium on AE from Composite Materials, Seattle, Washington, USA, 27 - 31 July
1992, ASNT, Columbus, Ohio, USA.
9. L. Golaski, P. Gebski and K. Ono (2002). Diagnostics of reinforced concrete bridges by
acoustic emission, J. Acoust. Emiss. 20, 83-98.
10. A. Nair and C.S Cai (2010). Acoustic emission monitoring of bridges: Review and casestudies., Eng. Struct., 32, 1704-1714.
11. S. Shahidan, R. Pullin, M.N. Norazura and K. Holford (2013). Damage classification in
reinforced concrete beam by acoustic emission signal analysis, Constr. Build. Mater., 45,
78-86.
12. A. Panjsetooni, N.M. Bunnori, A.H. Vakili, Z. Shirkhani, Z. Shirkhani (2013). Fractureformation evaluation of reinforced concrete structure using acoustic emission technique,Chin. J. Eng., Volume 2013, Article ID 126509, doi.org/10.1155/2013/126509.
13. A.A. Pollock (2014). AE signal features: energy, signal strength, absolute energy and
RMS. TN‐103‐22‐9/11, MISTRAS Group, Princeton Junction, NJ, USA.
14. BIS (Bureau of Indian Standards). July 2000. Plain and Reinforced Concrete- Code ofPractice. IS:456. New Delhi, India.
15. BIS (Bureau of Indian Standards). 2018. Ultrasonic Pulse Velocity Testing. IS:516 (Part5/ Sec 1). New Delhi, India.
16. T. Shiotani, D.G. Aggelis and O. Makishima (2009). Global monitoring of large concrete
structures using acoustic emission and ultrasonic techniques: case study, ASCE J. ofBrid. Eng., 14(3), 188-192. https://doi.org/10.1061/(ASCE)1084-0702(2009)14:3(188).
17. V.M. Malhotra and N. J. Carino (2004). Handbook on Nondestructive Testing ofConcrete, CRC Press/ASTM International, PA, USA.
18. B.L. Karihaloo (1995). Fracture Mechanics and Structural Concrete, Longman Scientificand Technical, NY, USA.
19. A. Carpinteri, G. Lacidogna and N. Pugno (2006). Richter’s laws at the laboratory scaleinterpreted by acoustic emission, Magazine of Concrete Research, 58(9), 619-625.
20. R. Vidya Sagar (2018). Acoustic emission signal ‘peak amplitude-distribution’ analysis
related to concrete fracture under uniaxial compression, Challenge J. Concrete Research
Letters, 9 (3), 85–102. https://doi.org/10.20528/cjcrl.2018.03.003.

108

ACTASde Trabajos Completos

Décimo Encuentro Internacional delGrupo Latinoamericano de Emisión Acústica
14-16 de agosto de 2019
Facultad de Ingeniería, Universidad Nacional del Comahue
UnCo, Neuquén
NEUQUÉN, ARGENTINAISBN 978-987-1323-65-4

COMISIÓN NACIONALDE ENERGÍA ATÓMICA

Selected Papers from the Proceedings of E-GLEA-10
Basgall A., Ferrari, G., and Gómez, M.P., Neural Networks applied to the monitoring ofdrilling processes in geological material samples, J. Acoust. Emiss. 37, 2020, S1-S10
Bonelli Toro, A. G., Gómez M. P., Machine learning applied to acoustic emission for tool
wear classification during milling of composite materials, J. Acoust. Emiss. 37, 2020, S11S20
Groth, E. B., Shumacher, G., Idzi J., Rojo Tanzi, B. N., Clarke, T. G. R., Lima, T. R. S. and
Jacques, R. C., Acoustic emission numerical analysis applied to structural health monitoringof laminar structures (Guided waves), J. Acoust. Emiss. 37, 2020, S21-S29
da Silva, D.S., Groth, E.B., Walter, J.S., Lutckmeier, F., Zuglian, G. F., Jaques, R. C., deLima, T.R.S. and Clarke, T.G., Study of fiber Bragg gratings (FBG) as acoustic emission
sensor to inspect and monitoring fiber-glass reinforced polymers (FGRP) degradation, J.
Acoust. Emiss. 37, 2020, S30-S37
Veca, A. C., Valese, N., Ruzzante, J., Albalat, A., Marino, A., and Reyes Reyes, M. V.,
Autonomous acoustic system for passive acoustic monitoring of cetaceans, J. Acoust. Emiss.
37, 2020, S38-S54.
Zitto, M.E., Sassano, M.P., Muszkats, J.P., Filipussi, D.A., and Piotrkowski, R., Energy andshannon entropy in acoustic emission signals for damage evaluation in andesite rock, J.
Acoust. Emiss. 37, 2020, S55-S64
López Pumarega, M. I., Ruzzante, J. E., and Gómez, M. P., La Emisión Acústica, su Historia
en la Argentina, J. Acoust. Emiss. 37, 2020, S65-S76. (in Spanish)
Contents of the Proceedings of E-GLEA 10 (Índice)

Topical Editors
José Ruzzante and Antolino Gallego

Neural Networks applied to the Monitoring of Drilling Processes inGeological Material SamplesaBasgall, A.a, Ferrari, G.a, Gómez, M.P.a,b

Grupo de Emisión Acústica, Facultad Regional Delta, Universidad Tecnológica Nacional, San
Martín 1171, Campana, Buenos Aires, ARGENTINA.
b
Grupo de Ondas Elásticas – Proyecto ICES, Comisión Nacional de Energía Atómica, Av. Gral. Paz
1499, San Martin (1650), Buenos Aires, ARGENTINA. e-mail: angelbasgall@hotmail.com

ABSTRACTAn Artificial Neural Network (ANN) is a mathematical computational model that, in a limited way,
attempts to mimic the structure and functioning of biological neurons which, broadly speaking, takeelectrical input signals and produce an output signal called “response”. By combining many artificial
neurons, they can have the ability to "learn" specific patterns in signal analysis and predict results. These
networks can be used in a wide range of applications such as object or signal classification, function
approximation, autonomous driving, speech recognition, image or text analysis and processing, etc.
Self-Organized Map (SOM) or Kohonen ANN perform a weighted summation of multidimensional
input data, which are individually affected by a numerical value called weight. Then, in a competition
between neurons, a winner is selected, and a neighborhood weight is adjusted to generate an output
more similar to the input. This generates a two-dimensional map where the information is shownorganized in areas that have similar characteristics. The weights of each neuron are modified
continuously and in different ways during several "epochs" (epoch is the name given to each step of
weighting adjustment) until the response error is minimized. This process is called training, where
specifically for this network, it is an unsupervised type of learning. In this work, SOM ANNs are used
to study acoustic emission (AE) signals from a drilling process of geological materials arranged inlayers bonded with cementitious material. With these elements, Laboratory size specimens(320x320x400 mm) were made. Drilling was carried out with 62 mm diameter drills, with single-wiretungsten carbide inserts, making four holes per multilayer specimen, and the AE was measured during
each drilling. After a detailed training process and rigorous data selection, this ANN showed the ability
to recognize the different layers of each specimen from the processing of the AE signals obtained during
the drilling process.

Keywords: Artificial Neural Network, acoustic emission, Drilling, geological materials.
1. INTRODUCTION
An Artificial Neural Network (ANN) is a modest mathematical computational model based on
its biological homologues, which receive electrical impulses in their dendrites (input) which
are then processed in the body (body of the neuron) and produce an electrical impulse thattravels through the axon (output) and connects with nearby neuron dendrites through chemical
neurotransmitters (weight) that modulate the signal. [Beale et al. 2020] [Zupan and Gasteiger,1993].
Once a mathematical model that models the basic functions of an individual biological neuron
is established, we can combine a large number of these until we acquire the ability to "learn"
specific patterns and infer results. These networks can be used in applications such as
autonomous conduction [Yáñez-Badillo H., 2017] [Faheem, 2013], image analysis andtreatment [Su, 2019], and others [Boldsaikhana, 2011] [Fratini, 2009].
In the present study, a self-organized map or Kohonen ANN was used [Kohonen, 2001]. This
network is characterized by an unsupervised learning, which means that the network
automatically adjusts to the input values without any type of positive or negative stimulationby the user. This type of network results in a two-dimensional map independent of the numberof input parameters. A Kohonen neural network performs multidimensional to twodimensional scaling. The resulting map shows the neurons that determine areas of similarity,

S1

of which the topology, i.e. the distribution of information with similar characteristics, can beanalysed. In this way, a form of easier interpretation is reached for an observer.
This type of ANN contains many parameters to be defined, with the aim of achieving correct
learning of the existing patterns in the data set. Important aspects to consider when "designing"and optimizing Kohonen ANN include the kind of learning, the number of neurons (expressedas an MxN matrix), weight correction, the topology, the geometry of the network, and thetraining epochs.
One of the ways to define an information learning rule is by comparing the outputs of all theneurons with the input data and defining as "winning neuron" the one with the weight closest
to the input data. Once the winning neuron has been selected, the weights of that neuron andits neighbors are adjusted so that they are more similar to that type of data. The correction
function of the neuron weights and its neighbors is the way in which an adjustment factor is
distributed in each neuron, generally this value has a high correction in the winner anddecreases as it moves away from it. The most common adjustment functions can be constant,Gaussian, linear, etc.
Topology is defined as the way in which each neuron interacts with its neighbors, defining theamount of individual communications. This can be seen in Fig. 1 where different arrangements
are represented with different number of neuron neighbors are represented.

a.

b.

c.

Fig. 1. a. Orthogonal distribution, b. Honeycomb Distribution, c. Triangular Distribution.

The network geometry establishes the behavior of the neighborhood when it is within theboundaries. It can be modelled in plane, tubular or toroid form (Fig. 2 a, b, c) which is thenrepresented on a two-dimensional map as shown in the planar view of Figure 3 for a tubular
and a toroidal geometry.

a.

b.

c.

Fig. 2. a. Plane Geometry, b. Tubular Geometry,ac. Toroid geometry.

b.

Fig. 3. Planar view of a.Tubular, b. Toroid geometry

Finally, an epoch is defined when the entire batch of data has been entered into the network.
Usually many training epochs must be required to adjust the weights, taking care not toovertrain the network. In each epoch the error is measured as vectorial distances, whether
S2

Euclidean, Minkowski, Tanimoto, Mahalanobis, etc; which express the difference between the
input and output values of the net. Additionally, the mixing error can be calculated, which is
produced when a data from one class is classified by a neuron that previously recognized a datafrom another class. The latter can only be done if the data of the different classes werepreviously labelled.
The technique of Acoustic Emission (AE) is based on the monitoring of elastic waves produced
in a material and propagate through that physical medium. Generally, is produced by a material
excitation. This wave can be detected and acquired by a piezoelectric transducer that transforms
the material surface movement into a proportional voltage signal. In this process, the AE signal
is digitized, parameterized and finally analyzed. In order to save computational resources, themost relevant information is extracted from the waves, using characteristic parameters to
describe it. These, in general, are defined from the first and last crossing of a threshold definedby the operator to separate the interesting signal from the noise. This portion of the signal iscalled an AE hit. Some of the characteristic parameters of the AE are amplitude (expressed indB), number of counts, duration, RMS, rise time, absolute energy, and others. The RMS is ameasure of the energy contained in the signal and has the advantage of smoothing out smallchanges in the gain of the electronics or the coupling of the transducer. It is calculated by theequation:
1 $! %& #𝑉!" = # &𝑣 (𝑡)𝑑𝑡
𝑇 $!

(1)

The absolute energy is the time integral of the square of the AE signal, divided by the input
resistance of the preamplifier. This parameter calculates the electrical energy at the sensor
output which will be related to the elastic energy generated by the heard phenomenon.
1
𝐸! = &[𝑉(𝑡)# ]𝑑𝑡(2)
𝑅
The number of counts is the number of times the signal crosses the threshold during theduration of the hit. The rise time is the time from when the signal crosses the threshold until
the hit reaches its maximum amplitude. The amplitude is the maximum modulus value of the
AE signal voltage. This value is related to the magnitude of the source and material
properties. Generally, it is expressed in decibels (dBAE) since the variation in mV is of several
orders.
It is calculated by the equation:
𝑉)
𝐴(𝑑𝐵'( ) = 20 𝑙𝑜𝑔(3)𝑉*!"
These parameters or AE descriptors can be defined as information vectors that can eventually
be used as input information to train and simulate an ANN with the ability to recognize thedifferent AE signals during the monitoring of any process.
It has been demonstrated that some acoustic emission parameters are effective to evaluate tool
wear in metal drilling [Gómez M., 2012], rock fracture [Lockner D., 1993], rock drilling [FerrariG., 2015 and 2018] , behaving of reinforced concrete beams [Xargay H., 2018], and others .
There is literature relating the use of ANNs and AE signals in different fields of applicationsuch as tool condition classification, as well as the final roughness obtained in grinding
processes [Kwak, 2004], characterization of friction welding processes [Jiménez Macías,
2013], monitoring in additive manufacturing processes [Shevchik, 2018], monitoring of tool
wear [Sick, 2002][Ferrari G., 2014], prediction of mechanical characteristics in rock drilling
processes [Kumar, 2013], prediction of production stress of failure in composite material[Kumar, 2017], inference of rock abrasion index [Perez, 2015], monitoring in roughing
processes, among many others.
S3

In the present case, a Kohonen ANN was used to classify the characteristic patterns of AE
corresponding to a drilling process on rocky materials. After selecting AE parameters anddesigning the ANN architecture, classification maps were obtained allowing to recognize thedifferent geological materials layers.
2. EXPERIMENTAL DEVELOPMENT
2.1
Laboratory specimen’s construction
Laboratory specimens were made by depositing different rocks bonded with cement. All thetest pieces have the same external dimensions and the thickness and type of rock of eachintermediate layer can vary.
The rock structure is then designed to allow four holes to be drilled in a single test specimen(see Fig. 4).
The rocks used for this work are sandstone and lutite (limestone). The cementitious element ismade up of 3 parts of sand, 0.8 of cement and 0.2 of lime.

Fig. 4 Experimental set-up (specimen H2SL2 see Table 1).

Specimens with the compositions detailed in Table 1 were used to carry out this test.
Table 1 – Summary of specimen conformation.
TEST
NAME

H1L1

H2SL2

H2LP1

H3S1

Cem. Rock 1 Cem. Rock 2 Cem. Cem. Rock 1 Cem. Rock 2 Cem. Cem. Rock 1 Cem. Rock 2 Cem. Cem. Rock 1 Cem. Rock 2 Cem.
Sandstone
S
S
S
Limestone
L
L
L
L
Slate
P

*H1L1, H2SL2, H2LP1 and H3S1 are internal codifications that identify each hole number, composition andspecimen number. For example, H3S1 is hole number 3 (A3), has a double layer of Sandstone (S) and is the
specimen number 1 from this composite. H2SL2, is hole number 2 (H2), with the first layer of Sandstone (S),second layer of Limestone (L) and is the specimen number 2 of this composite.
**P is from slate rock, but the information from this layer is not used in the present paper.

2.2

Equipment

For the acquisition of AE data, a four-channel PAC model DISP-4 acquisition board and a WD
model broadband sensor of the same brand with a 2/4/6 preamplifier with 20 dB gain wereused. The equipment was operated with the PAC AEWIN Version E4.6 software.

S4

Push Hydraulic
SystemVariable speed
motor

Drilling toolHydraulic Pressure
GaugesExternarparameters

AE Sensor

AE

Pre
AmplifierMultilayer
specimen

Post processing of
information by RN
Kohonen

Kohonen map

Fig. 5. Block diagram of the experiment.

To perform the penetration of the specimens, a 62 mm rotary tool was used, mounted on a
vertical trolley, driven by a hydraulic cylinder, which is controlled by a system that allows toregulate the advance of the rotary tool (Fig. 5).
2.3 AE parameters used
In the acquisition, 19 AE parameters were taken: Rise Time (RISE), Count (COUNTS), Energy(ENER), Duration (DUR), Amplitude (AMP), Average Frequency (AFRQ), RMS, Average
Signal Level (ASL), PCNTS, Reverberation Frequency (RFRQ), Initial Frequency (IFRQ),
Signal Strength (SIG STRENGHT), Absolute Energy (ABS ENE), First Frequency Quadrant(FREQ PP1), Second Frequency Quadrant (FREQ PP2), Third Frequency Quadrant (FREQ
PP3), Fourth Frequency Quadrant (FREQ PP4), Frequency Centre (CFRQ), Peak Frequency(PFRQ).
It was decided to use RMS, ABS ENERGY, COUNTS, RISE and AMP as input parameters
for the neural network.
The selection of AE parameters is based on Pearson's correlation see Eq. (4), which is astatistical value that tells us the linear dependence between two variables, similar tocovariance, but with normalized value scales between 0 and 1
𝜎+𝜌+,- =(4)𝜎+ . 𝜎Where: 𝜎+- is the covariance of a and b
𝜎+ is the standard deviation of a.
𝜎- is the standard deviation of b.
The minor variables correlated to each other should be used
As an example, Table 2 shows the results obtained from Pearson's correlation for the H2L1
test.

S5

Table 2 – Pearson's correlation coefficients.

A
2
C
1

A
B
S
E
N
E
R
G
Y

A
F
R
Q

A
M
P

ABSENERGY
AFRQ
AMP
ASL
CFRQ
COUN
CYCLESDURATION
ENER
FREQPP1
FREQPP2
FREQPP3
FREQPP4
IFRQ
PARA1
PARA2
PCNTS
PFRQ
RFRQ
RISE
RMS
SIGSTRENGHT

1.00
-0.03
0.24
0.13
-0.01
0.69
NaN
0.36
0.80
-0.01
-0.01
0.04
0.05
-0.05
0.01
-0.01
0.29
-0.01
0.05
0.09
0.32
0.88

-0.03
1.00
-0.26
-0.15
0.10
-0.11
NaN
-0.20
-0.11
0.04
0.16
0.03
0.01
0.67
0.05
0.06
-0.16
0.06
-0.19
-0.22
-0.10
-0.10

0.24
-0.26
1.00
0.63
0.06
0.37
NaN
0.22
0.43
0.18
-0.21
0.03
0.09
-0.38
0.04
-0.14
0.43
0.14
0.26
0.19
0.51
0.38

A
S
L

C
F
R
Q

C
O
U
N

C
Y
C
L
E
S

D
U
R
A
T
I
O
N

0.13
-0.15
0.63
1.00
-0.15
0.21
NaN
0.14
0.27
-0.06
-0.37
-0.09
-0.03
-0.22
-0.09
0.01
0.25
-0.08
0.16
0.13
0.80
0.24

-0.01
0.10
0.06
-0.15
1.00
-0.06
NaN
-0.13
-0.04
0.92
0.56
0.22
0.13
0.10
0.12
0.01
-0.06
0.89
-0.02
-0.11
-0.12
-0.04

0.69
-0.11
0.37
0.21
-0.06
1.00
NaN
0.74
0.90
-0.06
-0.04
0.05
0.05
-0.16
-0.03
0.00
0.54
-0.05
0.10
0.28
0.33
0.91

NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN

0.36
-0.20
0.22
0.14
-0.13
0.74
NaN
1.00
0.67
-0.14
-0.08
0.01
0.02
-0.21
-0.07
0.03
0.40
-0.13
0.00
0.42
0.19
0.63

E
N
E
R

F
R
E
Q
P
P
1

F
R
E
Q
P
P
2

F
R
E
Q
P
P
3

F
R
E
Q
P
P
4

I
F
R
Q

P
A
R
A
1

P
A
R
A
2

P
C
N
T
S

P
F
R
Q

R
F
R
Q

R
I
S
E

R
M
S

0.80
-0.11
0.43
0.27
-0.04
0.90
NaN
0.67
1.00
-0.03
-0.06
0.02
0.04
-0.15
-0.02
-0.02
0.51
-0.03
0.08
0.27
0.41
0.97

-0.01
0.04
0.18
-0.06
0.92
-0.06
NaN
-0.14
-0.03
1.00
0.26
0.06
0.03
0.01
0.11
-0.09
-0.05
0.85
0.02
-0.10
-0.07
-0.03

-0.01
0.16
-0.21
-0.37
0.56
-0.04
NaN
-0.08
-0.06
0.26
1.00
0.27
0.14
0.20
0.12
0.14
-0.07
0.52
-0.07
-0.09
-0.23
-0.05

0.04
0.03
0.03
-0.09
0.22
0.05
NaN
0.01
0.02
0.06
0.27
1.00
0.68
0.04
0.00
-0.01
0.04
0.13
0.00
0.00
-0.03
0.02

0.05
0.01
0.09
-0.03
0.13
0.05
NaN
0.02
0.04
0.03
0.14
0.68
1.00
0.00
0.00
-0.02
0.05
0.08
0.01
0.00
0.00
0.03

-0.05
0.67
-0.38
-0.22
0.10
-0.16
NaN
-0.21
-0.15
0.01
0.20
0.04
0.00
1.00
0.05
0.08
-0.26
0.05
-0.30
-0.35
-0.16
-0.13

0.01
0.05
0.04
-0.09
0.12
-0.03
NaN
-0.07
-0.02
0.11
0.12
0.00
0.00
0.05
1.00
-0.03
-0.04
0.12
-0.01
-0.07
-0.06
-0.01

-0.01
0.06
-0.14
0.01
0.01
0.00
NaN
0.03
-0.02
-0.09
0.14
-0.01
-0.02
0.08
-0.03
1.00
-0.01
-0.01
-0.03
0.01
0.01
-0.02

0.29
-0.16
0.43
0.25
-0.06
0.54
NaN
0.40
0.51
-0.05
-0.07
0.04
0.05
-0.26
-0.04
-0.01
1.00
-0.04
0.16
0.63
0.30
0.45

-0.01
0.06
0.14
-0.08
0.89
-0.05
NaN
-0.13
-0.03
0.85
0.52
0.13
0.08
0.05
0.12
-0.01
-0.04
1.00
0.02
-0.10
-0.08
-0.03

0.05
-0.19
0.26
0.16
-0.02
0.10
NaN
0.00
0.08
0.02
-0.07
0.00
0.01
-0.30
-0.01
-0.03
0.16
0.02
1.00
0.12
0.12
0.07

0.09
-0.22
0.19
0.13
-0.11
0.28
NaN
0.42
0.27
-0.10
-0.09
0.00
0.00
-0.35
-0.07
0.01
0.63
-0.10
0.12
1.00
0.12
0.23

0.32
-0.10
0.51
0.80
-0.12
0.33
NaN
0.19
0.41
-0.07
-0.23
-0.03
0.00
-0.16
-0.06
0.01
0.30
-0.08
0.12
0.12
1.00
0.38

S
I
G
S
T
R
E
N
G
H
T
0.88
-0.10
0.38
0.24
-0.04
0.91
NaN
0.63
0.97
-0.03
-0.05
0.02
0.03
-0.13
-0.01
-0.02
0.45
-0.03
0.07
0.23
0.38
1.00

Once the parameters to be used were selected, the tests were fractioned to obtain all the signal
sections belonging to the cut condition of each kind of rock, then a reduction of the data wasmade to reduce the training time of the network.
Each section was reduced to 500 data per variable.
The start and end times for each rock layer, indicator (label) and amount of data from eachsection is shown in Table 3.
When the experiment was performed, the thicknesses of each layer and driller position wererelated to the test time, so it was possible to locate in time the physical position of each one(times from table 3 were selected by this way).
In Figures 6, 7, 8 and 9 the correlation between distance measurements and acoustic emission
signals can be seen using the RMS parameter where the difference between each layer of rockand cement can be appreciated.
Table 3 - Summary of times and data quantities
Summary Table - Time and Data
Test
H1L1
H2SL2
H2LP1
H3S1

Rock

Indicator

T. Start

Limestone
Limestone
Sandstone
Limestone
Limestone

L
L
S
L
L

161.31508.81
338.5
787.78378.01

Sandstone
SandstoneSST. End273.61623.21
734
977.1
430.372
Unused280.55437.44629.034
844.1

S6

4807
2554
18681
1548
675

Reduced
Data
480
510
518
516
675

5798
24075

579
501

Original data

Selected total
data

3779

Fig. 6. RMS vs. Time from test H1L1.

Fig. 7. RMS vs. Time from test H3S1.

Fig. 8. RMS vs. Time from test H2SL2.

S7

Fig. 9. RMS vs. Time from test H2LP1.

2.4 Final Kohonen neural network architecture
The parameters determined for the final network architecture after several iterations until
acceptable results are obtained are summarized in Table 4.
Table 4. Summary of Network Parameters Used.

Network size
Geometry
Correction function
Number of neighbours
Correction factor
Training epochs

25x25
ToroidGausian5
Max: 0.6 - Min: 0.01
500

Training and prediction process
The selected data is scaled between 0 and 1, then joined forming a 3779 x 5 matrix (where therows are the measurements and the columns are the 5 parameters previously selected). A
scaling is made so that all parameters are in the same order of magnitude, allowing to make aneffective error measure and an optimal weight adjustment.
The data are labeled to easily identify the resulting network. An "A" was used to identify thesandstone and a "C" to identify the limestone.
Data is randomly arranged so that the order of training is not initially preferential to a particularclass.
Finally, 70% of the data is separated for training and 30% for prediction, data introduced inthis prediction, never participated in the training process or adjustment of their weights; they
are located in the zone where their vector distance is similar to that kind of data.
3. RESULTS
The results obtained from the training and prediction are shown in Fig. 10 in parts a and b,
respectively.
Training shows that there is a zone where preferably the sandstone data is located (green "S")and another large zone where the limestone data is located (yellow "L"). considering that thisis a toroidal geometry so the information in the edges is similar. There is also a dispersion
where some of the data of "S" is located within the zone of "L".
In the prediction, a similar pattern can be observed, so the classification is made in an
acceptable way, remembering that these data did not participate in the adjustment of theweights of the neurons, they are located in the zones where the data are the most similar.

S8

The error control according to the number of epochs is found in Fig. 11, the results are 0.05 instandard error and 11.45% in the mixing error.
L L
L
S L

L S
L L L
S S L SL
S L L L L
L
L
L
L
L
L
L
L

L L L
L L L
L
L L
L
S
S
S S
S
S S S
S L S S

a.

L
S
S
S
S S

L L

S
S
L
S
S
S
S
S
S

S

S
S
L

S

S L
L
S S
S
S
S
S L
S L
L
S
S
S S
S S
S
S S
S S
S L
S
S S
S
S S

L
S

L L S
L
L
S L L LLL
L L L L L L
L
L L
L L L LL
L L L L L L L L L
S L
S S L L L L L L
L L L LL
L L
S
L L L LS
S S S S S L L
S S S S S S S L LS
S S S S S L SSS S S S S S LS S S S S S S S S
S S S S S S S S S
S S S S S S S L S
S S S S S S S L S
S S S S S S S S LS S S S S S S S LS S S S S S L L L
S S S S S S S L L
S SSL S L LS S S S S S
L L
L
L L L
L

L L L L

S L
L
L S
S

L L
L
L L

L L
L

L
S L
L
L
L
L L
L L L
L
L
L
L
L
L
S L L
S L
L L L
S L S
L L L
S S
S S L
S S
S S S
L S S
L S L
L L L
L L L
L L L
L
S
L L L
L

L
L
S S L
L

L
L
L
L
L
L
L
L
S
S
S
S
S
L
L
L
L

L S
L
L
S
L
L L
L
L L L
L L L L LL
S L L LL
L L S L L
L
L L
L L L
S L
L L S
L
L L
L L
L S L
L L L S
L
L
L S L
S
L S L
S S
S L S
S S S SS
S L S S S
S S
L S S
S
S
S S
S S L S S
L L
LLS
S L S SL S L L
L
L
L
L
L L
L
L L
S

S L S
S S L
L
L L L
L
L
L L
S L S
L L
L
L
L
S
L
S
L
S
L
S
L
L
L
L S
S L
S
S
S S S
L
S
S
S S
L S
S
L L S
S S
L
L
L L

S
L
L S
L S

S

S

L
L L

L
L S
L
L
L L
L L
S S
L
L L
S L
S

S L S L L
L L
L L L
L L L
L
S
L
S L
S
L
S
L S S
S L
S S S
S
S
S
S S
S S S SS S S S S
S L
S
S
S
S S
S
L
S
S
S
S
S
S
L
L L
S

S L
L S
S S
S
S
L

b.

10. a. Training Map.

L L

L L

L L L
L
L
L L L LL
L L L L L L
L
L L L
L L L LL
L L L L L L S L L L
S
L L
L L L L L
S L
L L L
L
S
L
S
S
S S S SSS S S S S
L
S S
L S
S S
S S S S SSS S S S S S S
S S S S S S
S
S S S
L L S
S S S S S S S L L S
S S S S S S L S L L
S S S S S S S L S LS S S S S L S
L L
S S
L L
S L L
S S S SSS L
S S S S S L L
S
S
L
S L

L
L L
L
L
L
L L
L

L

S L
L L

L
L

L L
L L

L L
S

L L S S

S

L L
L
L

L S L L

L
L L
L
L L
L
L L
L
L
L L
L
L
L L

L
L

L
L
S
S L
L L
L

L
S L
L

L
L
S
S
S
L
S
L
S
L
L
L
L
L

L S
L

L
S
L
S
S
L
L
L
L
L
L

L

L
L
S
S
L
L
L
L
L
L L
L

L
S

L
L
S

L
L S
L L
L
L
L L
L
L
L L S
L
S
S L
S
S
L L

L
L L L
L
S
L L
L
L S L L
L L
L L L
S L L
S
S
S
S
S
S S S
S
S S
S
S S L
S
L
S
L L S
S
L
L
S
S
S
L
S
L
L L
S
L
S
L L
L
S L
L

L L
L L
L
L
L
L
L
L L
L
L
S
L
L
L
L L L

L

S
L
S
S S
S

S

S S
L S S
L S
L
S
S
S
S S
L

S
S
S
S
S

Fig.

b. Prediction Map

Fig. 11.Training Error control.

4. CONCLUSIONS
It was possible to observe the capacity of Kohonen neural networks to classify information,from a large amount of data, through easily recognizable patterns expressed in a user-friendly
way. This could be improved using multilayer networks with a more complex architecture,
further optimizing network parameters and using a larger amount of data. This last point leads
to optimizing the programming of the network source code to take advantage of the resourcesof a modern computer.
We can also conclude that it is possible to use the AE parameters to describe the different typesof drilled rocks.
It should be clarified that the parameters selected are effective for this particular case, for theerror achieved expressed in the results, so that if similar networks were used for anotherpurpose, satisfactory results might not be produced.
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Jiménez Macías E., Emisión Acústica y Redes Neuronales para Modelado y Caracterización del Procesode Soldadura por Fricción Agitación - Revista Iberoamericana de Automática e Informática
industrial 10 (2013) 434–440.
Shevchik S.A., Acoustic emission for in situ quality monitoring in additive manufacturing using spectral
convolutional neural networks - Additive Manufacturing Volume 21, May 2018, Pages 598-604.
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of more than a decade of research - Mechanical Systems and Signal Processing (2002) 16(4), 487–
546.
Rajesh Kumar B., Regression analysis and ANN models to predict rock properties from sound levels
produced during drilling - International Journal of Rock Mechanics & Mining Sciences 58 (2013)
61–72
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emission parameters using artificial neural network - Applied Acoustics 115 (2017) 32–41.
Gómez M.P., Hey A.M., D’Attellis C.E. and Ruzzante J.E., “Assessment of cutting tool condition by
acoustic emission”, PROCEDIA MATERIALS SCIENCE, 1, 321–328, Elsevier (2012) ISSN:
2211-8128. DOI: 10.1016/j.mspro.2012.06.043
Perez S., A preliminary study on the role of acoustic emission on inferring Cerchar abrasivity index ofrocks using artificial neural network – Wear 344-345 (2015) 1–8.
Ferrari G., Magallanes J., Migliori J., Gómez M. P., “Clasificación de la condición de brocas en
taladrado a partir del análisis de la Emisión Acústica mediante Redes Neuronales”, Proceedings delCongreso Internacional de Metalurgia y Materiales, S19 4418, UNL, Santa Fe (2014) ISBN 978987-692-043-8 E-book.
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PROCEDIA MATERIALS SCIENCE 8. 693 – 701. Elsevier (2015) ISSN 2211-8128. DOI:
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Aires. ISBN: 978-987-1323

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Machine Learning Applied to Acoustic Emission tor Tool Wear
Classification during Milling of Composite Materials
Bonelli Toro, A. G.a, Gómez, M. P.b,ca

DMA, Gerencia de Materiales, Centro Atómico Constituyentes, CNEA, San Martín, Argentina.
GOE, GDTyPE, Centro Atómico Constituyentes, CNEA, San Martín, Argentina.
c
GEA, FRD, UTN, Campana, ARGENTINA. e-mail: abonellitoro@gmail.comb

ABSTRACTIn the last decades, the use of composite materials was increased in aerospace and aeronauticalindustries. The tolerances of manufactured components are decisive in its acceptance. In this way, tool
monitoring is an instrument to control the precision of the cutting processes. The drilling/milling
process involves a 60% of the rejections of manufactured composite components. Tool wear is one of
the main reasons for faulty milling as it achieves a poor termination and inaccurate size of the holes. In
the present work, milling tools used in machining (milling or drilling) of sandwich structure panels foraerospace applications were studied for different wear stages. Elastic waves generated during the cutting
action were measured by the method of acoustic emission. Three tools with different degree of wear
were selected and different machine learning algorithms were implemented PCA and t-SNE for
dimensionality reduction and random forest and Kohonen maps to identify the tool condition. The first
results are encouraging to carry on these applications to online tool wear classification.

Keywords: acoustic emission, machining, composite materials, machine learning
INTRODUCTION
Monitoring and control methods for machining processes in manufacturing are essential in
high-technology industries such as aerospace and aeronautics. With the purpose of improving
efficiency of the manufacturing, these methods are continously evolving. Drilling and milling
are the most used operation for structure assembly of composite components. About 60% of
these holes made in composites are rejected due to its bad termination [1], because it canseverely degrade the mechanical properties of the component made and one of the main reasons
is tool wear. Several ways to monitor tool condition are implemented, and can be classified as
direct or indirect methods. The direct methods are those that assess the condition employing
visual inspection of the tool geometry using techniques based on cameras and microscopes.
The indirect methods allow us to infer the status of the tool based on parameters related withthe process, for example, mechanical vibrations, current consumed by the machine, torque,forward force, or acoustic emission signals generated during the machining process. The
advantage of the indirect methods is the applicability on line in the cutting process. Typically,
direct methods are applicable while the process is stopped.
In the present work, the feasibility of acoustic emission applied on tool monitoring duringcomposite machining is studied. Several machine learning algorithms such as Principal
Component Analysis, t-Stochastic Neighbor Embedding, Kohonen maps and Random Forest
are implemented to explore and classify the data. The specimens used are sandwich structureswith reinforced carbon fiber polymer faces and an aluminum honeycomb core.
1. Acoustic Emission
Acoustic Emission (AE) is a Non-Destructive Testing (NDT) method that detects the
propagation of elastic waves on the surface of a given material, usually using piezoelectric
sensors in the range of 20 kHz to 1 MHz [2]. The most common AE sources are mechanical,
electro-magnetic and chemical stresses. This is a passive method because it doesn’t need an
external active emitter [3], as opposed to other NDT methods (Ultrasound, X-Ray, etc.).
AE allows the analysis of different machining operations such as drilling, milling and turning,
and was widespread used in metallic components [4, 5]. The increased usage of composite
materials in recent decades demands the study of each associated manufacturing process. On
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the one hand, the study of mechanical behavior in tensile, flexion and fatigue tests, seeking tounderstand the mechanical properties of composite materials. On the other hand, in thecharacterization and assessment of machining processes. The complexity of studying AE incomposite materials is based in the high degree of anisotropy of the material and the dispersive
nature of the propagated waves.
2. Sandwich structure
The sandwich structure is a structural composite material, commonly seen in aerospaceindustry. It has superior properties than other materials, such as high strength-to-weight andstiffness-to-weight ratios [6]. The hardness and strength are given by its faces (or face sheets)
and in the core there is a thick lightweight material which gives flexural strength combinedwith the two faces. Fig. 1 shows a diagram of sandwich structure. The manufacture is complex
and requires various stages. The faces are usually made of multi-laminate fiber-reinforced
composite material (carbon or glass) which already has several stages to be made and thenadded to the core [7].
Drilling, milling and sawing and are the most common machining operations on composite
sandwich structures, where the specimen must rigidly support the cut and the tool moves on
the part to be removed. The matrix is completely independent of the cutting parameters [8].
These parameters will depend on the kind of fibers being machined, regardless of the type ofmatrix used. Tools with physical vapor deposition (PVD) are commonly used because of its
high wear resistance.

Fig. 1 Sandwich structure diagram.
To achieve a good quality of the machined workpiece, the tool must prevent the edge fromattacking the multilaminate perpendicular to the plane. This is unavoidable, but the strategy is
to use tools with a diameter smaller than the desired hole and then perform a concentric milling
path, shown in Fig. 2.
1. Dimension reduction and classifying algorithms
Dimensionality reduction algorithms were developed in order to visualize the results of a highdimensionality dataset. This type of algorithms can also enhance the data processing time. Oneof the fundamental pillars is to preserve the characteristics of the original dataset in the newspace.
Classification algorithms categorize the data in different classes and can be divided in two
subclasses: supervised and unsupervised learning. Supervised algorithms need a target vector
to match each element of a vector. On the other hand, unsupervised algorithms need no target
to perform classification

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Fig. 2 Typical machining operation of sandwich panel structure.
i. Principal Component Analysis
Principal Component Analysis (PCA) is one of the simplest multivariate analyses based on
auto-vectors and auto-values. The main idea is to reveal the internal structure of the data by
displaying their variance. PCA provides the user a smaller dimensional dataset, a projection
which is a complementary perspective than the original dataset [9].
ii. t-Stochastic Neighbor Embedding
Another dimensionality reduction method is t-Stochastic Neighbor Embedding (t-SNE). Unlike
PCAs, t-SNE does a better analysis of non-linear behavior in a dataset by converting a highdimensional dataset into a low dimensional space, allowing data exploration [10]. The dataset
is called a map, and its low dimensional points are called map points. The objective of thisalgorithm is to be able to maintain the data structure of the multidimensional space and its
neighborhoods in the low dimensional space.
iii. Kohonen Map
The self-organizing map (SOM) or Kohonen map is an unsupervised single layer neuralnetwork. This implies that the dataset is given by the user in the input layer and there is no
ideal user-supplied output. The SOM learns to group or map its data into a specific number ofclasses [11]. This means that the network will not take into account the relationship between
each object and the true response, but that the network works completely independently [12].
Like PCA and t-SNE, the SOM also does a dimensional reduction which helps interpretation.
It classifies arranging the elements in a grid, putting similar data close to each other.
iv. Random Forest
Random Forest is a supervised ensemble classification algorithm that is essentially a collectionof Decision Trees (DT) [13]. DT are built on an entire dataset, using all the characteristics,
while Random Forest randomly selects specific variables to build multiple Decision Trees andthen averages the results. After building a large number of trees using this method, each tree
"votes" or chooses the class, and the class that receives the simplest majority votes is the"winning" or predicted class.

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EXPERIMENTAL
1. Tools selection
The Sandvik Coromill Plura (2P460-OA) with (Ti,Al)N2 were used. Its diameter is about 8mm
and the length is 63.5 mm. A set of tools was inspected with a scanning electron microscope toqualitative analyze each wear and three were selected. Fig. 3a shows the most worn tool (named
H1); Fig. 3b shows the one with intermediate wear (H2); and H3 is a new tool, shown in Fig.
3 c.

a.

b.

c.
Fig. 3 SEM inspection (left) and cutting-edge detail of the tools H1 (a), H2 (b) y H3 (c)S14Comparing the three tools, the most worn (H1) has rounded and soft cutting edges. The cause
may be the interaction between the tool and the chip.
2. Specimens and machining procedure
Two sandwich structure panels with aluminum honeycomb core and CFRP composite laminate
plates were the specimens for the machining process. Each laminate plate has 24 layers with alaminate orientation [0, 30, 60, 90, -30, -60]2S. The size of each specimen is 620 mm lengthand 180 mm width. Eight blind holes in the top face were made in each specimen as shown in
Fig. 4. In the first specimen, H1 milling tool was used in the left side of the specimen panel
and H3 in the right side. In the second specimen, H2 and H3 in the left and the right side of thepanel, respectively. The machining process was performed with an industrial CNC machinewith a feed rate of 250 mm / min and spindle speed of 8000 RPM. Two AE sensors werepositioned in the center of the width at 50 mm from the edge. The distance between the holes
is 50 mm. First, a drilling stage was performed to make 8 mm diameter and then, a spiral milling
was performed to increase the diameter to 10 mm.

Fig. 4 Hole (black) and sensor (gray) placement in the test panels.
3. AE Measurement
Two Physical Acoustic Corporation (PAC) wideband sensors and 2/4/6 preamplifiers wereused. The couplant was solid vaseline. Data was measured and acquired with an 18-bit
resolution PAC PCI-2 AE system with a sampling frequency of 5 MHz and a 1 MHz LPF. The
15 parameters given by the equipment were registered.
RESULTS AND DISCUSSION
1. Tool inspection
After the measurement, an inspection under a magnifying glass was performed. It can be seenthat as the tool wears, more chips are accumulated in the tool edges. Fig. 5a and 5b shows thedifference between H1 and H3, respectively.

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a.

b.

Fig. 5 (a) H1 and (b) H3 after panel machining.
2. Pre-processing
The AE measured during the machining operation was parametrized and analyzed. For eachmeasured feature, data was filtered based on MARSE energy parameter at 70 aJ to remove
background noise. Fig 6 shows the MARSE energy parameter to visualize the drilling andmilling stage and its transition stages (stationary and exit). The transition stages were discarded
because there is no machining involved.

Fig. 6 MARSE energy vs time to show machining stages involved in the experiment.
Also, a time filter was used to select only the milling stage as it determines the quality of thehole's termination. Then, the data was normalized and all the measurement were concatenatedin a single dataset. Finally, it was splitted into training (80%) and test (20%) dataset forclassification algorithms evaluation.
A correlation between the different parameters and the target was made. Centroid frequency,
RMS and average frequency were the most correlated variables.

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3. Dimensionality reduction algorithms
i. PCA
The PCA algorithm was implemented and it’s shown in Fig. 7. The non-linear nature of themechanic waves involved in AE impedes this type of algorithm from maximizing its ability todiscriminate between clusters. However, there are three zones with a clear predominant class.

Fig. 7 3D PCA plot for the milling stages.
ii. t-SNE
In the t-SNE method, there are three zones were clearly prevail one of the tools above the others(Fig. 8). There is a mix of H2 data points in the H1 and H3 clusters. The reason is that thealgorithm is nonlinear and works better for this kind of datasets supposing an advantage for alater classification.

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Fig. 8 3D t-SNE plot for the milling stage.
iii. Kohonen map
As mentioned above, SOM does a dimensionality reduction and a classification
simultaneously. An implementation in Python named MiniSOM was used [14]. In this caseFig. 9 shows 3 topological zones, corresponding to the three different tools wear. In this case,there is also a certain mix in the classes. H2 is mixed in the clusters of the other tools.

Fig. 9 Train self-organizing map for the milling stage.
The test set was used to perform the classification on unknown samples and the accuracy ofthis algorithm was 90%.
iv. Random Forest
The Random Forest algorithm was implemented to perform the wear classification on theoriginal, the PCA and the t-SNE dataset. Table 1 shows that the most efficient dataset is the
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original. However, the two dimensionality reduction algorithms work very well too. The
application of this algorithms can be thought in a really large-scale implementation as anintermediate stage.
Table 1. Comparison between Random Forest implementation in the three datasets
DatasetTraining accuracy

Test accuracy

Original

0.99

0.97

PCA

0.97

0.94

t-SNE

0.98

0.95

CONCLUSIONSIn this work, different dimensionality reduction and classification algorithms were applied forthe analysis of tool wear in composite materials machining. Three PVD tools and two
specimens were analyzed in this study.
To achieve the desired size hole in the laminate composite plate, drilling and milling operations
were performed. The focus of this work, the milling stage, was mainly studied because millingis the process that controls the hole quality termination. In this process it was possible todifferentiate the three different tool conditions in each algorithm carried out.
For dimensionality reduction methods of PCA and t-SNE, the algorithms differentiated areas
were one tool clearly predominates over the others, although with a certain mixing of H2 partover the others.
The tool wear analysis, performed using SOM, allowed the observation of areas where one tool
prevailed over the others with an accuracy of 90%.
The Random Forest algorithm was used on the original, PCA and t-SNE datasets. In all thesituations the results were above 95% of accuracy which is a very good result for this kind ofexperiments.

AKNOWLEDGEMENTS
Special thanks to Nicolás Belinco, Martín González Fuster, Gerardo Fernández, Federico
Antico, Pablo Vogt, Adriana Domínguez, Nicolás Núñez and Hernán Sánchez.
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Liu, D., Tang, Y., & Cong, W. L. (2012). A review of mechanical drilling for composite
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Gómez, M. P. (2015). Fundamentos de la Emisión Acústica. UTN-FRD.
Grosse, C. U. (2008). Acoustic Emission Testing. Acoustic Emission Testing. Springer.
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Gómez, M. P., Hey, A. M., D’Attelis, C. E., & Ruzzante, J. E. (2012). Assessment of
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Ferrari, G., & Gómez, M. P. (2015). Correlation Between Acoustic Emission, Thrust and
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Davies, J. M. (2001) Lightweight sandwich construction (MPG Books). Manchester:Blackwell Science.
Bitzer, T, (1997) Honeycomb Technology: Materials, Design, Manufacturing,
Applications and Testing. Springer Science.

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Zenkert, D. (1997). An Introduction To Sandwich Construction. Emas Publishing,London.
9. Einasto, M., Saar, E., Einasto, J., Tempel, E., & Tago, E. (2011). SDSS DR7 superclusters.
Astronomy & Astrophysics, 1–13. Hinton, G., & Roweis, S. (2002). Stochastic Neighbor
Embedding. Advances in Neural Information Processing Systems, 15, 833-840.
10. Heaton, J. (2012). Introduction to the math of neural networks. (WordsRu.co, Ed.).
Chesterfield: Heaton Research Inc.
11. Magallanes, J., et al. (2011). Kohonen classification applying ‘missing variables’ criterion
to evaluate the p -boronophenylalanine human-body- concentration decreasing profile ofboron neutron capture therapy patients. Journal of Chemometrics, 340–347.
12. Géron, A. (2017). Hands-on machine learning with Scikit-Learn and Tensor Flow:
concepts, tools, and techniques to build intelligent systems. Sebastopol, CA: O'Reilly
Media. ISBN: 978-1491962299
13. Vettigli G., MiniSom: minimalistic and NumPy-based implementation of the Self
Organizing Map. Available: https://github.com/JustGlowing/minisom/

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Acoustic Emission Numerical Analysis Applied to Structural Health
Monitoring of Laminar Structures (Guided Waves)aGroth, E. B.a, Shumacher, G.b, Idzi J.c , Rojo Tanzi, B. N.b, Clarke, T. G. R.a,
Lima, T. R. S.a and Jacques, R. C.a
Laboratório de Metalurgia Física LAMEF, Universidade Federal do Rio Grande do Sul, BRASILb
Grupo de Mecância Aplicada GMAP, Universidade Federal do Rio Grande do Sul, BRASIL
c
UIDET Ingenería Aplicada en Mecánica y Eletromecánica (IAME), Facultad de Ingenería,
Universidad Nacional de La Plata, La Plata, ARGENTINA e-mail:eduardo.groth@ufrgs.br

ABSTRACT
Structural health monitoring of critical components such as bridges, pipes, aircraft bodies isregarded with great interest by the industry and researchers nowadays. The development ofthese evaluation techniques is strictly connected to the development of new materials, with newproperties, behaviors, and other characteristics, allowing the design of new transducers. This
includes works in the propagation of mechanical waves, which is a phenomenon highly studied
in non-destructive testing (NDT). Acoustic emission testing is a technique that uses mechanicalwave propagation to monitor structural integrity by analyzing the sound waves produced bysmall increments in size of defects present in a sample. This type of test is challenging because
the acoustic emission event cannot be repeated, and techniques such as averaging of multiple
readings to increase signal to noise ratios cannot be thus employed. In this context, the present
work suggests a numerical model of the acoustic emission phenomenon for better
understanding of the generated signal, which allows for better designing of resonant sensors
with a high sensitivity.
Keywords: Monitoring, acoustic emission, DEM, natural frequency, guided waves.
1. INTRODUCTION
Crack propagation is a well-known and evaluated phenomenon mainly due to recurrencein civil structures of different scales and applications (Sengupta et. al). Whenever a crack
advances, microstructural ruptures release kinetic energy in the material medium. This energythat disperses and propagates from the crack is a typical acoustic emission and might take placein structures such as bridges, pressure vessels, etc. Monitoring of this type of event allows
control of structural integrity, remaining life estimations and safe operation of the component
at the current location. Acoustic emission technique has been widely applied to monitor
structures such as pipelines, (Jacques et. al; Clarke et. al), pressure vessels (Tirbonod andHanacek) and concrete structures (Paresh et. al; Chao et. al).
One of the great challenges in acoustic emission monitoring is the environmental noise
detected by the sensors. In several occasions one event might exhibit a displacement amplitude
below the background noise, depending on the distance between source and sensor, and theacoustic emission will be undetected. Also, the monitored component might be placed in anenvironment subject to diverse noise sources, such as rotating machines, fluid flow, impacts
and even rain, the latter being a considerable agent that might mask/hide acoustic emission,and disqualify the method (Eagle et. al).
In this context, this present work proposes to introduce a method that, through the use ofnumerical analysis, allows the development of sensors with a high sensitivity to acoustic
emission and low response to background noise, thus increasing acoustic emission performancein monitoring the integrity of structures and components.

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2. DEVELOPMENT
Fracture numerical modelling is a non-trivial theme which conventional finite element
method is not capable of reproducing. The adopted alternative in this work is a model based on
discrete elements (LDEM), initially proposed by Riera in 1984. This finite element method
discretizes the domain in cubic elements formed by beams that only support axial loads. Figure
1 presents a single element and the internal beam configuration (Groth 2016).

Figure 1: Unit finite element characteristic of the LDEM method
The mass is placed in the nodes, and each node has three degrees of freedom, corresponding
to node displacement in the three orthogonal directions. The equations that relate element
properties to the elastic constants of the isotropic medium are:!=9$,
4 − 8$)*! = )+" #(9 + 8!),2(9 + 12!)

)*$ =

2√3
*
3 !

where ! is the Young Modulus, " is the Poisson coefficient, #! and #" are the areas of thenormal and diagonal elements and $# is the length of the side in the basic cube. The resulting
movement equations obtained from this spatial discretization can be written in the followingform:34̈ (6) + 74̇ (6) + 9% (6) − :(6) = 0,

where x represents a node displacement vector, M is the diagonal mass matrix, C is the damping
matrix, 9% is the vector of internal forces acting on the node masses and P is the external forces
vector. The integration at the domain is carried out through addition of the central finite
differences which allow large deformations.

Figure 2: Bilinear constitutive model with material damage.
In this work, the LDEM implemented a relation between the axial force and axial
deformation in the internal beams of the element based on the bilinear law propposed byHillerborg in 1978 and presented in Figura 2. The specific fracture energy, Gf, is directly
proportional to the area below the triangle formed by the meeting of the two straight lines withthe x-axis in the geometric analysis of the law. An important characteristic of this model is therandom distribution of the Gf value of each beam within a pre-established range of values. In
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order to obtain randomness of the mechanical property, a Weibull distribution is applied, which
in practical terms is the effect of the metal grain size among other microstructuralcharacteristics which are found to vary within a domain. The deformation associated to the
maximum load supported by each beam (<& ) was also randomly varied.
2.1 Hybryd model DEM + FEM
A model solely built in LDEM to analyze mechanical wave propagation might lead to an
unfeasible computational cost to most ordinary computers. In this work, a fusion between a
LDEM and a FEM model was implemented. The aim was to adapt the LDEM characteristics
for crack propagation and a sufficient quantity of modelled material medium to allow theacoustic emission produced by the crack to exhibit a natural propagation manner and also
allowing frequency analysis of this perturbation.
The approach consists in transferring the displacements produced in LDEM to the modeled
geometry in the FEM model, as presented in Figure 4. Three simulations were used to validate
the method: a pure LDEM, a FEM and the mixed model. The subject chosen for this test wasa rectangular beam made of low carbon steel (SAE 1020) with E = 210 GPa, r = 7850 kg/m³,
n= 0,25, and sample dimensions of 15x5x1500 mm³. The loads are applied in the excitation
area (see Figure 3) in the longitudinal direction of the wave guide (z-direction). Readings weretaken from a control point located at a distance of 400 mm away from the frontal side of thesample.

Figure 3: Model used to evaluate the hybrid model technique.
As previously mentioned, three distinct models were studied: the first was built onlywith discrete elements (DEM), the second used only finite elements (FEM) and the third wasa hybrid model, where half of the sample (which contains the excitation region) was modeledwith DEM and the other half modeled with FEM (see Figure 4).

Figure 4: Three models designed to compare different model approaches.
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Because the meshes of the two models (DEM and FEM) were not exactly the same,
coupling between them was carried out in two different manners: (a) only in the nodes at theedge; (b) in the inner nodes of the interface (Figure 5). This resulted in two solutions for the
hybrid model.

Figure 5: Models of coupling between DEM and FEM domains of the body: a) in the edge
nodes and b) in the internal nodes of the common face.
2.1.1 Results of the mixed model validation
The results found for the displacement in the waveguide direction (z-axis) for the threemodels are presented in Figure 6. It is noticeable that the methods are perfectly applicable forthe elastic wave propagation study, since an excellent superposition of the curves was achieved.

Figure 6: Responses to the z-shift at the pickup point for the DEM, FEM models and the two
types of DEM + FEM models.
3. APPLICATION
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After validation of the mixed model (LDEM + FEM), a numerical study of the acoustic
emission produced by a crack opening in the three possible modes (Figure 7) is presented.

Figure 7: Crack breaking modes
The details of the model are presented in Figure 8: the DEM/FEM interface and the loads
to augment the crack for the three possible modes shown in Figure 7.

Figure 8: DEM/FEM hybrid model. (a) different load directions for each cracking mode; (b)DEM/FEM interface; (c) monitoring nodes; (d) boundary condition.
4. RESULTS
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Monitoring of a line of nodes equally spaced allows the aplication of a 2-way Fourier
Transform (FT). This method allows analysis of the existing frequencies and wavelengths in
the propagations produced by the increase in crack size. Initially the analysis considered only
the monitoring node, and later the 2-way FT was implemented.
The signal observed (three directions) in one of the monitoring nodes is presented in Figure
9. In this case, the crack is breaking in mode I and clearly the longitudinal movement is themost activated. The circle to the left indicates waves of the pressure (P) type originated fromthe crack growth. A more detailed analysis of the regions marked with circles will now follow.

(a)

(c)
(b)
Figure 9: Sign at one of the monitoring points, in the three directions, z (green), y (black)and x (blue).
The detail, Figure 9 (b), shows the arrival of a typical compression wave, the points are at a
distance of 190 [mm], and with the observed times, a velocity of propagation of 4500 m/s forthis pulse can be calculated. Figura 8 (c) shows a zoom in of the second circled region in thesignal. An energy packet (Figure 9, t= 0.25 milliseconds) is noticeable, which apparently
assumes the form of a L propagation mode. This information can be used in the design ofsensors customized for this type of signal.
The FFT (Fast Fourier Transform) of the acquired signal during crack growth in modeI, when compared to the dispersion curves of the waveguide (simulated beam) where theacoustic emission propagates, shows predominately generation of the longitudinal mode. This
proves that the most prominent displacements happen in the direction of the mechanical wave
propagation (Alleyne 1992).

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Figure 10 z direction displacements.
In Figure 10, the superposition of the dispersion curves found for the rectangular beam(Groth et al) is presented over the fft of the signal generated by the crack growth. The energy
is concentrated in the curve which represents the longitudinal mode (P wave excitation) when
the cracks grows in tensile mode. The signal exhibits a bit of noise in a large range of
frequencies.

Figure 11: Analysis of the emission by the crack advance in mode 2 in the monitoring of apoint. X, y and z shifts after signal processing (20-1500 kHz filter window).

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Figure 11 shows the acoustic emission analysis of the crack growth in mode II. The signal
exhibits movements preferably in the x and y directions, which indicates that the energy is
propagating in mostly shear displacement. However a longitudinal pulse may also be generated(shown by the circle in Figure 11), with a velocity of 5000 m/s.

Figure 12: (Left) FFT of the emission produced by the crack in mode 2 (sliding); (right) FFTof the emission produced by the crack in mode 3 (tearing).
Figure 12 (right hand side) shows the FFT of the signal obtained through the propagationof the crack in mode 3 (tearing). In this analysis, it is also possible to notice the energy mainlyin the torsional mode.
5. DISCUSSION
The idea of modeling the phenomenon is the understanding of the frequencies andpreferable movements activated by the acoustic emissions in order to use this data in the designof high sensitivity sensors. One hypothesis of this work is that the energy released by the crack
growth will assume some guided wave propagation mode in the beam where the crack ispresent. The geometry tested was a beam with a cross-section of 15 x 5 mm with a crack placedat the edge of the model. Loads applied to this edge caused the crack to advance in modes I, II
and III.
Through the analysis of the results, the method is thought to produce a result that
corroborates the hypothesis that the propagating modes are activated by the growth of cracks
in wave guides. Simulations produced a wide range of information which can be used in thedesign and project of acoustic sensors.
Experimental validation should follow in the future in order to verify the technique andensure it can be used to help advance the area of acoustic emission.
6. CONCLUSIONSIn this present work, a propposal of numeric modeling for crack propagation (which
encompasses the acoustic emission produced in the direction of the crack growth) waspresented
It was possible to use the hybrid approach to induce the crack growth and to analyze thepropagation of wave mechanics in the same model. These two ideas were possible due tomatching of two numerical techniques: traditional finite element method and discrete element
method, the later allowing the rupture of the domain. The technique of the hybrid model ispromising and produced a reasonable result with a wide range of information, which can beused in projects and in the understanding of the phenomenon, as along as the model is solidand reliable.
S28

REFERENCES
Alleyne D., Cawley P., (1990) A two-dimensional Fourier transform method for the measurement ofpropagating multimode signals. JASA 89:1159-1168
Clarke, T. G. R.; Jacques, R. C.; Bisognin, A.; Camerini, C.; Damasceno, S.; Strohaecker, T.R.
Monitoring the structural integrity of a flexible riser during a full-scale fatigue test, Engineering
Structures, Volume 33, Issue 4, 2011, Pages 1181-1186, ISSN 0141-0296,
https://doi.org/10.1016/j.engstruct.2010.12.039.
Chao Xie, Linjuan Yuan, Min Zhao, Yinghui Jia, Study on failure mechanism of porous concrete basedon acoustic emission and discrete element method, Construction and Building Materials,
235, 2020, 117409, ISSN 0950-0618, https://doi.org/10.1016/j.conbuildmat.2019.117409.
Eagle DM, (2005) Fundamentals of Acoustic Emission Testing: Wave Propagation. In: Moore PO, et.
al. NDT Handbook Vol. 6, 3rd ed.
Groth E.B., Iturrioz I., Kostesky L.E., Birck G., Silva G.S., and Clarke T.G.R., Acoustical Emission
propagation in a prismatic wave guided: Simulations using truss like discrete element method.
PROGRESS IN ACOUSTIC EMISSION XVIII. 2016
Groth E. B. The dispersion curve applied in guided wave propagation in prismatic rods. Latin American
Journal of Solids and Structures. 2018 doi: 10.1590/1679-782545274
Jacques, R.; C.; Clarke T. G. R.; Morikaw, S.; Strohaecke T.R. Monitoring the structural integrity of aflexible riser during dynamic loading with a combination of non-destructive testing methods, NDT &E International, 43, 2010, 501-506, https://doi.org/10.1016/j.ndteint.2010.05.005.
Jacques, R C; Flores, J V; Strohaecker, T R; Reguly, A. Acoustic emission testing in wires from thetensile armour of flexible risers under load. Insight - Non-Destructive Testing and Condition
Monitoring, 51, 2009, pp. 504-507, 511, https://doi.org/10.1784/insi.2009.51.9.504.
Paresh Mirgal, Joy Pal, Sauvik Banerjee, Online acoustic emission source localization in concrete
structures using iterative and evolutionary algorithms, Ultrasonics, 108, 2020, 106211, ISSN 0041624X, https://doi.org/10.1016/j.ultras.2020.106211.
Rose JL (2014) Ultrasonic guided waves in solid media. Cambridge University Press, New York.
Sengupta, Sanjay; Datta, Aloke Kumar And Topdar, Pijush. Structural damage localisation by acoustic
emission technique: A state of the art review. Lat. Am. J. Solids Struct. 2015, 12, 1565-1582.
https://doi.org/10.1590/1679-78251722.
Tirbonod, B.; Hanacek, L., Acoustic emission measurements during the local monitoring of a defect inthe ZB2 pressure vessel, NDT & E International, 24, 1991, 3-14, https://doi.org/10.1016/09638695(91)90676-T.

S29

Study of Fiber Bragg Gratings (FBG) as Acoustic Emission Sensor to Inspect
and Monitoring Fiber-Glass Reinforced Polymers (FGRP) Degradationda Silva, D.S., Groth, E.B., Walter, J.S., Lutckmeier, F., Zuglian, G. F., Jaques, R. C.,
de Lima, T.R.S. and Clarke, T.G.
Laboratório de Metalurgia Física, LAMEF, Universidade Federal do Rio Grande do Sul, BRASIL
e-mail: saraiva.silva@ufrgs.br

ABSTRACTFiber-glass reinforced polymers (FGRP) exhibit characteristics such as excellent chemical resistanceand low weight which make their use specially attractive for a series of indrustrial applications, which
include pressure vessels and pipes. However, a considerable inconvenience for the application in criticalcomponents is the lack of inspection and monitoring methods that allow structural integrity evaluation
in a reliable and efficient manner. The objective of this work is the development of a method capableof inspection and monitoring based on acoustic emission (EA) using Fiber Bragg Grating (FBG) to be
applied in the detection of failure and degradation of composite components made of FGRP. This new
method has advantages over conventional acoustic emission methods: there is no risk of explosion inclassified environments, immunity to magnetic field noise, low attenuation of the light pulses even forlong distances and multiplexing capabilities. The first stage of this work consisted in the development,
manufacture of the FBG sensors and characterization. After approval, the sensors were used to monitor
the damage progression in a pipe segment under fatigue loading and results were compared to the dataobtained with conventional EA sensors. The propposed method proved to be viable with great potential
although further studies are required for sensibility optimization.

Keywords: Composite material, FGRP, monitoring, acoustic emission, Fiber Bragg grating.
1. INTRODUCTION
Technological advances in new materials create a demand for new inspection and monitoring
techniques that can guarantee safe operation for critical structures and processes. In this context,
composite materials and specifically fiber-glass reinforced polymers (FGRP) stand out due toproperties such as high chemical resistance and low weight.
Among the techniques and inspection methods currently used to monitor damage anddegradation of this type of material, acoustic emission (AE) is a promising alternative (Maggi,2018). In this method, material degradation is associated to row of events of energy release
which leads to acoustic emissions in the material; those acoustic emission events can be
describe as transitory elastic waves generated by the rapid redistribution of strain inside thematerial during loading (Assarar, 2016).
Current standards and codes determine the use of piezoelectric transducers (PZT) as acoustic
emission sensors (ABNT, 2005). However, this type of sensor has disadvantages such as
coupling difficulties and large number of cables for signal acquisition, which imparts higher
loads to the structure if a large numer of sensors is used.
As an alternative, bragg gratings in optical fibers (FBG) can be used as EA sensors (Rajan etal, 2017). This type of sensor is largely used as a strain sensor due to its rapid response andgreat sensitivity to longitudinal stresses (Campanella et al, 2018). Studies have shown the EA
monitoring capability of FBG if interrogating systems with high sampling rates for signal
acquisition are employed. (Xia et al, 2015).
The use of optical fiber as a sensor has a number of advantages over conventional sensors, suchas: smaller size and weight, multiplexing capabilities (more than one sensor in a single fiber),immunity to electromagnetic noise, low signal attenuation (readings can be taken kilometers
away from the measuring point, and the possibility of instalation in classified environmentswith risk of explosion. (Rajan, 2015; Allil, 2010; Gomez et al, 2017).
The objective of this work is the development of an inspection and monitoring method basedon acoustic emission with FBG sensors to be applied in the detection of failures andS30

degradation of components produced by FGRP composites. For this purpose, different tests
were carried out for FBG sensor evaluation. The first stage consisted of monitoring the acoustic
events produced in a controlled manner in a FGRP pipe segment. Piezoelectric sensors wereused to allow comparison between data results. Later, the FBG sensor was used for damage
and degradation monitoring in a FGRP pipe in a fatigue loading test.
2. FIBER BRAGG GRATING (FBG) AS ACOUSTIC EMISSION SENSOR (AE)Fiber Bragg grating sensors are manufactured through techniques divided in two categories:
interferometer techniques and non-interferometer techniques, through periodic exposition of
the fiber to UV radiation. The most common non-interferometer technique uses phase masks,
a diffractive optical element which spatially modulates the UV beam emitted by a laser. This
technique was demonstrated for the first time in 1993 by K. O. Hill. (Hill et al, 1993).
Bragg grating in optical fibers can be described as a periodic modulation of the refraction index
in the core of the fiber. This periodic modulation is perpendicular to the longitudinal axis of
the fiber and exhibits constant spacing between each period. The functioning principle of thissensors involves the use of an optic interrogator, which is responsible for emitting a light pulse
through the fiber and is also responsible for acquiring the reflected signal. The light pulse
emitted by the interrogator interacts with the Bragg grating and is reflected in the opposite
direction. This reflection is centered in a specific wavelength, called Bragg wavelength (λb).
(Rajan, 2015). λb is a function of the effective refraction index of the optical fiber (neff) and thegrating period (Ʌb), according to equation 1:
λ! = 2 ∗ n"## ∗ Ʌ$
(1)
The signals obtained by the FBG sensors are associated to variations of the grating period in
the Bragg sensor due to longitudinal stresses or temperature variations. This correlation of the
Bragg wavelength with strain and temperature is described by equation 2:%&= (1 − p) ∗ -ε' + 𝛼() ∗ 𝛥𝑇3 + 𝛼* ∗ 𝛥𝑇
(2)&!

Where p is the photo-elastic coefficient (0.22), ε is the strain, 𝛼() is the thermal expansion
coefficient of the material, 𝛼* is the fiber refraction index variation and 𝛥𝑇 is the temperature
variation.
3. METHODOLOGY
The methodology used in this work is divided in three distinct stages: development andmanufacture of the Bragg gratings, evaluation of sensor response to acoustic events generated
in a controlled manner and evaluation of performance in dynamic testing (fatigue).

3.1. FBG Sensors Development and Production
In the stage of sensor development and production, the FBG sensors were produced by using
the technique with phase masks and were later encapsulated. The laser equipment used for
fabricated the Bragg gratings on to the fibers was a Braggstar M from Coherent company. Thislaser is operated with energy pulses that emit ultraviolet radiation with a wavelength of 248
nm, and a variable operating frequency up to 100 Hz.
The procedure of sensor manufacturing consisted in exposing the core of the fiber (10mm area),
which becomes the active length of the sensor. This area is positioned behind the phase mask,
which is a diffraction optical element with carefully controlled engravings on one of the faces.
This spacing is called Bragg grating period (Ʌpm), and each masks has a specific Ʌpm which
will originate a specific FBG sensor.
In this work, the phase mask had a Ʌpm = 1063 nm, which resulted in a FBG sensor with a λb
= 1550 nm. The phase mask was chosen to allow λb of the sensor to be inside the measuring
range of the optical interrogator, which is from 1530 nm to 1558 nm.
S31

In Figure 1 the production of the FBG is illustrated. The laser emits UV light pulses which hitson the reflective mirrors. These mirrors redirect the UV lightbeam to a cylindrical lenspositioned in front of the phase mask that collimates the beam and concentrates the energy. At
last, the beam goes through the phase mask and is diffracted, reaching the core of the fiber thatwas positioned behind the mask, which originates the FBG sensor.

Emissão
do UV6feixe
Emission
beam
laser
UV
EspelhosReflector
mirror’s,
refletores

Lente
Cylindricallens
cilíndrica

Phasedemask
MáscarafasesRegiãosensor FBG
FBG6sensor6region

Figure 1 – Manufacturing of FBG sensors with the phase mask technique.
Due to the frailty of the fiber, it was necessary to develop a proper encapsulating method toallow manipulation and installation of the FBG sensor. The sensor is initially glued to apolyimide substrate, which is responsible for transferring the deformation of the surface to thesensor.
3.2. Evaluation of The Response to Acoustic Events Generated in A Controlled MannerIn this stage, the response of the FBG sensors to static tests consisting of lead break acoustic
emissions was compared to signals obtained with piezoelectric sensors. The interrogator used
for the acquisition of the acoustic emission signals was a FI3200, from Fiberpro company, withan acquisition capacity of up to 100 kHz. The equipment used for the piezoelectric sensors wasan AMSY-5, from Vallen, and VS-30 sensors without any pre-amplification.
The static test for technique comparison (FBG vs piezo sensors) is presented in Figure 2, whichshows the sensors attached to a GFRP pipe segment. The FBG sensor was glued to the sample
with a rapid cure cyanoacrylate resin. The piezoelectric sensors were coupled with vaseline
grease and held in place with adhesive silver tape. The spacing between the sensors was 300
mm.

S32

FBG$sensor

PZT$sensor

PZT$acquisition
FBG$acquisition

Figure 2 – Setup of static test.
3.3. Performance Evaluation in Dynamic Tests
The performance of the FBG sensors in dynamic tests was evaluated during fatigue loading(5000 loading cycles) of a GFRP pipe segment. Figure 3 shows the test setup. A FBG sensor
was positioned at the center of the pipe, longitudinal direction, with cyanoacrylate resin. Signal
acquisition was carried out with a FI3200 equipment.

Figure 3 – Setup dynamic test.
4. RESULTS
4.1 FBG Sensor Development and Production
The methodology chosen for sensor production and encapsulation was proven to be efficient.
It was possible to obtain sturdy sensors with good reflectivity. In Figure 4, the optical responseof the FBG sensor with λb = 1050.60 nm is presented. The sensor after encapsulation ispresented in Figure 5.

S33

Figure 4 – Optical spectrum of the FBG.
FBG$sensor

Polyimide substrate

Figure 5 – Encapsulated FBG.
4.2 Evaluation of The Response to Acoustic Events Produced in A Controlled Manner
Results obtained in the lead break test for performance evaluation of the FBG sensors compared
to commercial piezoelectric sensors were satisfactory, since it was possible to detect the
acoustic events with both sensors. In Figure 6 (a) a waveform of the signal captured by thepiezoelectric is presented, and Figure 6 (b) shows the result of the FBG sensor to the same
event.
In Figure 6 (b) it is possible to see the signal to noise ratio for the FBG sensor is approximately
7.6x (maximum signal amplitude 0,01629 nm and background noise with an amplitude of
0,00214 nm).
A comparative analysis of the signal amplitudes (in dB) obtained for each sensor is presented
in Figure 7. Three events were generated with the lead breakings, always in at the same position.
The amplitude of the signals acquired with the PZT sensor is considerably higher than thesignals obtained with the FBG sensors. It was also more constant. An amplitude variation for
the FBG signal is noticeable on the third event (higher than 10dB). No significant amplitude
variations were detected with the PZT sensor.

S34

Time%(µs)

(a)

(b)

Figure 6 – Evento acústico adquirido com sensor (a) PZT and (b) FBG.

Figure 7 – Comparative amplitude of the signals acquired by the FBG and PZT sensors in dB.
4.3 Performance Evaluation in Dynamic Fatigue Test
The acoustic emission events were monitored during the fatigue load testing. In this work will
be presented the results of the beginning of the 5.000 cycle fatigue load. Figure 8 shows theacoustic emission events detected by the FBG sensor. Three events can be identified that haveamplitudes higher than the threshold.
Figures 9 (a) and 9 (c) show the data related to the events detected above 50 dB, at time instants
11:38:51 and 11:39:59, whereas Figure 9 (b) and 9 (d) show a zoom-in of the areas of therespective events.

S35

EventFigure 8 – Acoustic emission events detected during fatigue loading test.

(a)

(b)

(c)

(d)

Figure 9: (a) event detection; (b) stress signal (DEF) and acoustic emission (E.A.) in thedetected event; (c) increase in the region of the event.
The acoustic emission events detected with the FBG sensor presented an acceptable signal tonoise ratio, and the amplitude of the highest event was 0.035 nm.
Even though it is not possible to correlate these events to the GFRP composite damage ordegradation, it is evident that the FBG sensors are capable of detecting acoustic emission eventsof the material, and further investigations should be carried out in the future.
5. CONCLUSIONSIn this work, the development of an inspection method based on acoustic emissions captured
with fiber Bragg grating sensors for composite materials of the GFRP type was presented.
The methodology for sensor fabrication was proven to be efficient and allowed the productionof FBG sensors with a wavelength λb within the expected range. The procedure for the FBG
S36

sensor encapsulation was successfully applied and increased the resistance of the sensor during
handling.
The FBG sensors detected acoustic emission events generated in a controlled manner throughlead breakings with a signal amplitude of 0.0165 nm, 7.6x above the background noise. In
comparison, the average amplitude of the PZT sensor was 58 dB whereas the average signal
amplitude for the FBG sensor was 36 dB.
In the dynamic test the FBG sensors detected acoustic emission events with an amplitude of
0,035 nm. It was not possible, though, to correlate such events to any damage associated tomaterial degradation.
Results obtained in this work are promising and serve as an illustration of the evident capacityof the FBG sensors to detect acoustic emission and monitor composite materials of the GFRP
type, both for static and dynamic tests.
REFERENCES
ABNT - Associação Brasileira de Normas Técnicas. NBR 15269 – Ensaios não destrutivos –Monitoração continua por emissão acústica – Procedimento. 2005.
Allil R. C., “Sensores a fibra óptica com tecnologia FBG para medida de temperatura e alta tensão”,
Tese (Doutorado) – Universidade Federal do Rio de Janeira, CPPE, Programa de Engenharia Elétrica.
209 f, 2010, Rio de Janeiro, Brasil.
Assarar, M., Scida, D., Zouari, W., Saidane, E., Ayad, R. Acoustic emission characterization of damage
in shorthemp-fiber-reinforced polypropylene composites. Polymer composites 2016. 1101-1112.
Campanella, C., Cuccovillo, A., Campanella, C., Yurt, A., Passaro, V., Fibre Bragg grating based strain
sensors: review of technology and applications. Sensors 2018, 18, 3115;
Gomez, J., A., Ozdagali, A., I., Moreu, F., Reference-free dynamic displacements of railroad bridges
using low-cost sensors. Journal of intelligent material systems and structures 2017. v. I, n. 15.
Hill, K. O., Malo B., Bilodeau F., Johnson D. C., Albert J., Bragg gratings fabricated in monomodephotosensitive optical fiber by UV exposure through a phase mask, Appl. Phys. Lett. 1993. 62, 1035–
1037.
Maggi, A. Aplicação da técnica da emissão acústica em análise de dano em estruturas reforçadas com
fibras. Monografia - Universidade Federal do Rio Grande do Sul. 25 f. 2018.
Rajan G. “Optical Fiber Sensors Advanced Techniques and Applications”, Taylor & Francis Group,
LLC, 2015, pág. 207.
Rajan, G., Vinod, J., Moses, T., Prusty, B., Xi, J., Ballast breakage analysis using fbg acoustic emission
measurement system. Geotech Geol Eng 2017; 35:1239–1247.
Xia, M., Jiang, M., Sui, Q., Jia, L., Theoretical and experimental analysis of interaction fromacousticemission on fiber Bragg grating. Optik 2015. 126. 1150–1155.

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Autonomous System for Passive Acoustic Monitoring of CetaceansaVeca, A. C.a, Valese, N.b, Ruzzante, J.c,d, Albalat, A.b, Marino, A.b, Reyes Reyes, M. V.b

Instituto de Automática, Facultad de Ingeniería, Universidad Nacional de San Juan, ARGENTINA; e-mail:aveca@inaut.unsj.edu.arb
Grupo de Investigación en Acústica Subacuática, Universidad Nacional de Tres de Febrero – Fundación
Cethus, ARGENTINAcLaboratorio de ensayos no destructivos, Universidad Tecnológica Nacional, Facultad Regional Delta,
ARGENTINA

ABSTRACTThis paper describes the development of an autonomous system for passive acoustic monitoring of
cetaceans in the Argentine Sea. The system consists of an omnidirectional hydrophone with apiezoelectric transducer designed and built for this purpose, which record signals in a frequency range
up to 200 kHz. The signals are processed by a digital algorithm based on pattern identification to extract
the data of interest and store it in a temporary memory. Once the memory capacity is full, the data issent through WiFi to a land-based station located 6 km away, for its analysis. The system will beanchored at a depth of 40 m and submerged 5 m below the surface, with autonomy for 2 months. To
date, tests in laboratory using patterns of sounds produced by cetaceans in the wild, show that the system
is capable of fulfilling the proposed objective.

Keywords: cetaceans, emission pattern, underwater acoustics.
1. INTRODUCTION
In nature, many species of animals use sound as the main way of communication (fig. 1). In
particular, bats in air and odontocetes in water have developed a biosonar or echolocation
system to detect obstacles and to search and locate their prey. For this purpose, they use thepulse-echo technique with frequencies above 20 kHz (Veca et al, 2019).

Fig. 1. Acoustic spectrum in nature
As light does not penetrate too deep into the ocean, cetaceans rely on sounds as their primarysensorial sense. They produce a variety of sounds that can be recorded for long or short periods
S38

of time as well as in real time using passive acoustic monitoring tools, such as autonomoushydrophones that store the information inside. In general, the acoustic signals emitted bycetaceans are unique to each species, which allows getting relevant ecological information ofeach species through non-invasive studies using passive acoustics. One reason to this is the factthat propagation speed of acoustic waves in water is five times higher than in air. These
characteristics allow acoustic energy to propagate over long distances with reduced attenuation.
As disadvantages, sound transmission is affected by the frequency of the signal, the absorption
loss and the noise present in the medium.
This paper presents preliminary results of the development of an electronic prototype system
for the recognition of sounds emitted by cetaceans and transmission to a station located on the
coast for further analysis. The main objective is the development of an acoustic system forpassive acoustic monitoring of cetaceans in the Argentine sea.
A particular objective is to gain experience in the development of automatic systems forrecording signals in a hostile environment and transmitting data to an autonomous station
located on the coast. The general scheme of the system is shown in fig. 2. The data-acquisitionzone will be submerged 5 m below the surface and contains an omnidirectional hydrophone
and a built-in pre-amplifier.

Fig. 2. Scheme of the system
The buoy contains the electronic system for the recognition of the acoustic signals captured by
the hydrophone, an analog to digital converter ADC with its corresponding memory, thebatteries and a modem with an antenna for the transmission of the signals to the land-based
station.

S39

2. CHARACTERISTICS OF THE WORK ENVIRONMENT
Sound Speed
As already mentioned, the sound propagation of the marine environment is characterized by
the speed, absorption loss and noise presence. The sound velocity profile shown in fig. 3(Kinsler et al., 1980) is divided into four zones, being the upper layer (surface layer) the oneof interest in this case as the hydrophone will be submerged 5 m deep. This layer is influenced
by local environmental variables. The speed of sound decreases with a soft negative gradient,taking an approximate value of 1,495 m/s. The surface layer presents characteristics ofisothermal water mixed by the action of the wind at the surface of the ocean. Sound tends to be
trapped by reflections on the surface and refractions upwards from the bottom due to the changein acoustic impedance (fig. 4).

Fig. 3. Sound speed profile in the ocean.

Fig. 4. Sound reflections and refractions within the surface layer
Transmission Loss
If the wave front is considered spherical, then absorption loss can be modelled by (Agost
González, 2015):

S40

𝑇𝐿(𝑑𝐵) = 10 ∗ log(𝑟) + 𝛼𝑟 ∗ 10!"(1)
where 𝑟 is the distance from the source to the receiver in km and 𝛼 is the absorption coefficientin dB/km. 𝛼 is a function of frequency (𝑓):#!

#!

𝛼 = 0,11 $%#! + 44 ('$((%#!) + 0,000275𝑓 * + 0,0003(2)

Given that the signals of interest, further described below, have all their energy above 100 kHz,the absorption attenuation, 𝜶, is about 30 dB/km. Assuming a source-receiver distance of 10km, the transmission loss would be about 10 dB, which shows the reduced attenuation of theacoustic wave front in the water.
NoiseSoundscapes, or acoustic environment in the ocean are composed of three different acoustic
sources: 1) the geophony comprised sources from the physical ambient such as water currents,
bubbles due to non-linear processes, the movement of waves, earthquakes, etc; 2) the biophonycomposed by sounds produced by marine life, such as schools of fish, cetaceans, among others;
and 3) anthropophony, which includes noise produced by human activities at sea, such as
shipping, naval exercises with sonars, etc. Fig. 4 shows the time series (bottom left),spectrogram (top left), and the noise density spectrum (right) of a fragment of a recording. The
spectrogram and the noise levels are referred to 1 µPa. As observed, the low frequencies present
a higher relative level in the spectrum, 110 dB in the vicinity of 1 Hz due to the low attenuation
at low frequencies, while the average is in the order of 80 dB.

Fig. 4. Ocean noise levels
From this analysis, it is concluded that the average noise level belongs to frequency bands
much lower than the frequency range emitted by the dolphins, presenting a salvable influence.
Furthermore, the profile of the seabed at the distance where the hydrophone will be located issimilar to that indicated in fig. 5, presenting low-pass filter characteristics (Medwin, 1998).

Fig. 5. Seabed profile where the hydrophone will be located.
3. EMITTED ACOUSTIC SIGNAL

S41

A specific type of signals that could be present in the designated recording area is the Narrow
Band High Frequency (NBHF) click. These clicks have been previously described and assigned
to several species of porpoises and small dolphins (Reyes et al. 2018, Melcón et al. 2012, Kyhn
et al. 2009) as well as the pygmy sperm whale (Madsen et al. 2005). These acoustic signals areoften stereotyped, presenting a peak frequency around 120-130 kHz, no energy below 100 kHz,
3 dB bandwidth around 15 kHz (Reyes et al. 2018, Madsen et al., 2005) and durations of around
300 µs. Due to the high frequency content of NBHF clicks, these result to be highly directional(Martin et al. 2018, Kyhn et al. 2010), making them difficult to record when being emitted
away from the axis between the hydrophone and the animals head.
Clicks are normally produced within click trains, in which a regular inter click interval (ICI)
between 5-50 ms (Reyes et al. 2018) separates one click from another. Figure 6 shows afragment of a recording including a series of NBHF clicks recorded at the location where thecurrently described autonomous system will be deployed.

Fig. 6. Acoustic recordings from Peale’s dolphins (Lagenorhynchus australis) in Argentinean
Patagonia. a) Series of click trains; b) zoomed fragment of single click train; c) zoomed single
click within a click train; d) 512 FFT points normalized power spectrum of click in (c) (Marino
2017)
If the system transmits to the land-based receiving station, all the sounds recorded by thehydrophone, the transmitting modem should always be active. Taking into account thetechnical specifications of the modem, the consumption of 1 Amp/h would make it impossible
S42to achieve a reasonable autonomy of the system. Added to that, the consumption of the rest of
the electronic systems should be considered. To overcome this limitation, it was decided todevelop a simple criterion for the recognition of the signal of interest. The criterion used inacoustic emission (Ruzzante & Pumarega 2017) and in the determination of threshold transit
time for ultrasound signals (Veca, 2019) was chosen. That is to use a decision threshold above
the noise level present in the signal and a time window that takes into account the average ICI,
as shown in fig. 7.

Fig. 7. Threshold and temporal window
4. SYSTEM IMPLEMENTATIONThis project comprises three fundamental components: the reception of acoustic signals emitted
by cetaceans, their discrimination with respect to other signals present in the marine
environment and the transmission of recorded data to a receiving station located on the coast.
Below the development of the hydrophone is presented, then the recognition and recording
system of the received signals, and finally the data transmission is explained.
4.1 Hydrophone
The design of the hydrophone considered different parameters related to its future
implementation for research studies:• The temperature of the aquatic environment does not affect the functioning of thepiezoelectric ceramic.
• The equipment is resistant to the environment in which it will work.
• The sensitivity and frequency response of the hydrophone are adequate for the study
signals.
• The cable length is adequate for the depth in which the hydrophone will be placed during
measurement.
Design and manufacturing are separated into three main parts. In the first instance, thetransducers were characterized and measured under different circumstances in order to
optimize the following stages. Secondly, the preamplifier circuit was designed contemplating
the values obtained in the previous stage, and finally, the housing where all the electronics aremounted together with the transducer was designed and built.
4.1.1 Transducer
In order to choose the type of transducer for this application, it is necessary to take into accountthat water impedance (1.54 x 106 Pa*s/m3) is much higher than that of air (412 Pa*s/m3),
therefore the transducer must be robust and capable of generating a great force (Rossing et al.,2004) In this work it was decided to implement two piezoelectric ceramics as a transducer
element since its firmness makes it capable of supporting or producing great efforts.
S43

The working frequency range that is intended to be covered goes from 10 Hz up to 200 kHz.
Taking this into account, it was decided to use piezoelectric ceramics as they are theelectroacoustic transducers that best fit the needs of this study. Two different types of ceramics
were implemented in order to cover the entire frequency range of interest. The first one presentsa hemispherical geometry (fig. 8) in order to obtain a broader directionality in its receiving
pattern.

Fig. 8 – Hemispherical piezoelectric of Sonox P53 material, CeramTec
The ceramic belongs to the CeramTec brand and it is made of a material that is a registered
design called Sonox P53 (CeramTec, Germany). The characteristics of this hemisphere PZT
ceramic is shown together with its resonance frequency in table 1.
Table 1 – Characteristics of the hemispherical crystal.
Parameter
Measurement
Internal radius
5.25 mm
External radius
6.25 mmWidth
1.00 mmResonance frequency
172 kzSecondly, a cylindrical piezoelectric ceramic was implemented to omnidirectionally cover the
low frequency bandwidth of interest. Fig. 9 shows this PZT ceramic and its dimensions andresonance frequency are shown in table 2.

Fig. 9 – Cylindrical piezoelectric, Steminc SMC32D30H10410.
Table 2 – Characteristics of the cylindrical crystal.
Parameter
Measurement
Internal radius
30 mm
S44

External radius
HeightResonance frequency

32 mm
10 mm
29.5 kHz

These piezoelectric elements were analyzed under different circumstances in order to estimate
the impedance curve in each of them (fig. 10). This measurement is important since, based on
the maximum impedance value obtained, the input impedance value of the signal preamplifier
circuit is determined. To ensure maximum voltage transfer across the spectrum of interest,
correct impedance matching is required between the transducer and the preamplifier input.
Furthermore, the resonance frequency of the ceramics can be deduced from the impedance
curve, which shows which part of the spectrum presents the highest sensitivity.

Fig. 10 – Impedance variation with frequency for the hemispherical piezoelectric ceramics.
The ceramics set was assembled using a piece designed in 3D printing software where eachhemispherical ceramic points to the opposite side in order to cover a wide reception angle (fig.
11). The cylindrical ceramic is located below the hemispheres at a distance of approximately 1cm.

Fig. 11 – Piezoelectric ceramics assembled in the 3D structure.
4.1.2 Preamplifier circuit
The signal preamplifier is placed inside the housing, as close as possible to the structure that
holds the ceramics. This decision was made in order to shorten the path of the signal without
S45

amplification to reduce noise induction and improve signal to noise ratio (fig. 12). This
implementation has advantages from other kind of electronic topology as a charge amplifier.

Fig. 12 – Preamplifier circuit at the assemblage stage with the piezoelectric crystals.
At the first stage of the circuit the signal is amplified with an approximated gain of 32 dB. This
gain was determined according to the typical gain values used in amplifiers for hydrophonesof similar characteristics. Also, this type of circuit lowers the high impedance of the sensors,
improving the SNR of all the system. At the input of the circuit two diodes were placed todischarge any voltage peak caused by the sensors. The most important feature in this first stage
is the implementation of a DC voltage servo to decouple the DC voltage at the circuit output.
This circuit was used both for the signal coming from hemispherical ceramic and for the signal
coming from the cylindrical ceramic, in parallel. Then the outputs of both parallel stages areadded (fig. 13).
At the circuit output, an asymmetric balancing system was implemented in order to protect thesignal from any type of unwanted interference that may be induced along the path from thehydrophone output to the monitoring station.
This balancing system ensures the same impedance value in each output, which implies
equivalent noise induction in each of them. Thus, by unbalancing the signal, a greatercancellation of the noise induced in common mode is obtained. Also in this implementation it
doubles the signal level. Figure 14 shows the frequency response of the preamplifier circuit.

Fig. 13 – Block diagram of the preamplifier circuit.

S46

Fig. 14 – Frequency response of the preamplifier
4.1.3. Hydrophone housing
The design of the hydrophone housing is a fundamental part of the design process. This mustmeet certain requirements that ensure the durability and correct operation of the hydrophone
under the environmental work conditions. Ideally, it should have a minimal impedance change
regarding the aquatic medium and behave as an attenuator for unwanted signals such as noise.
In addition, the materials used for its construction must have negative buoyancy.
Fig. 15 shows the components assembled inside the hydrophone housing. It is made up of twoturned solid PVC tips and a PVC tube-shaped cylinder.

Fig. 15 – Assembled hydrophone within its housing.
The hydrophone will be subject to noise sources with varying relative intensities depending on
the signal frequency, the speed of the current, the location of the hydrophone, the effectivenessof both external and internal decoupling materials and the size of the hydrophone. The current
and movement generate a fluctuation in the pressure outside the housing that contains thehydrophone, exciting vibrations in its surface and in the other components of the hydrophone.
As only one hydrophone is used, for this study it was not possible to implement the gain control
method of an array to achieve the reduction of ambient noise by combining several transducers.
However, sea current noise can be reduced by certain design features, as the use of a dielectricmineral oil that has a density as close as possible to water density (1000g/m3) filling thehydrophone housing in order to ensure a minimum impedance change for the acoustic signalof interest.

S47

4.2. Recognition and recording system
Given that the hydrophone will be submerged at 5 m depth, 6 to 10 km far from the coast,reasonable operation autonomy of at least 1 month must be ensured. The autonomy depends
exclusively on the available energy source, the batteries. Based on this limitation, it was
decided to:
1. Consider only acoustic signals exceeding the decision threshold between a certain
temporal window.
2. The decision threshold will be set above the average background noise level recordedin the area.
3. The temporal window will be determined by the mean duration of the acoustic signalsof interest.
The signal recorded by the hydrophone will follow two paths (fig. 16). On one hand, it willenter to a stage called "decision-making" and on the other hand, it will be digitized with a 16bit analog-to-digital converter (ADC) with a sampling rate of 500 kHz (ADS8322)(Analog
Devices, MA, USA), which allows an amplitude resolution of 76 µV. The digitized signal willenter a circular buffer (FIFO stack). If the decision-making stage recognizes that it is a signalof interest, the information contained in the buffer will be stored on a solid state hard disk.
When its capacity is complete, all stored signals will be transmitted to the station located on
the coast through the modem for further analysis.

Fig. 16. Block diagram of the recognition, recording and data transmission system.
During the decision-making stage the signal recorded by the hydrophone is rectified and its
envelope is calculated using of a low pass filter. Then it is compared with the threshold andtemporal window. If the signal meets both conditions (logical condition Y), it is stored in thehard disk (fig. 17) (Veca, 2019).

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Fig. 17. Decision-making stages.
The rectifier was implemented with a LM318 operational amplifier and two 1N4148 diodes.
The LM318 integrated circuit IC has high gain per bandwidth (GB = 15 MHz) and high slewrate (SR = 70V/µs), which are necessary to perform with the diodes, the complete rectificationof the input signal with the minimum deformation. Once the signal is rectified, it goes througha second order low pass filter based on the Sallen Key topology with a cutoff frequency of 10
kHz, using a TL081 IC. The threshold comparator is based on a LM311 and the temporalwindow on a LM555 in monostable configuration. The "AND" function is performed with theoutput of the LM555 and the output of the LM311 by means of NAND gates using a CD4011
IC. The diagram is shown in figure 18. The CPU is based on an Arduino DUE system (Arduino,2018).

Fig.18. ”And” function.
4.3. Data transmission
For the purpose of this paper, a FS1000A module that transmits at a frequency of 433 MHz,and its respective XDRF5V receiver, were used to transmit and receive the data (fig. 19). These
devices work at that frequency because it is within the free use bands, and are compatible with
the Arduino UNO system. For the final system, it is planned to implement transmissionreception with a "rocket M5 (Ubiquity networks, NY, USA) system with TCP/IP protocol that
supports a transfer speed of the order of 150 Mbps with a bandwidth between 2 to 40 MHz. It

S49

can operate in environments with temperatures between - 70 °C and + 75 °C and it hasprotection against static discharges in the order of +- 24 kV. The transmitting antenna is
omnidirectional due to the movements of the buoy.

Fig. 19. Transmitter (located in the buoy) and receiver (located at the coast)
5. LABORATORY SIMULATION TESTSA series of laboratory tests were carried out to test the developed system. They consisted ofemulating the surroundings of the hydrophone and the signals emitted by the species ofcetaceans in the area. A fish tank was used to simulate the marine environment (fig. 20)(Gimenez et al., 2018, Julian et al., 2018, Bustos et al., 2018). Inside, a piezoelectric disk isconnected to the emulator of the sounds emitted by cetaceans. The sounds were generated bymeans of an electronic synthesizer, programmable by software (fig. 21). On the opposite side,
there is a piezoelectric receiver that simulates the hydrophone.

ProjectorReceiver

Fig. 20. Emulation of the marine environment.

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Fig. 21. Sound generator system.
The acoustic signal detector consists of the envelope extractor, the decision threshold and thetemporal window (fig. 22). The transmitter that emulates the modem and the receiver thatsimulates the land-based station is shown in fig. 19.

Fig. 22. Receiving system.
Finally, figure 23 shows a top view of the fish tank and the modules mentioned above.

Fig. 23. Whole system
6. RESULTS
Two tests were carried out. The first one used commercial buzzer-type disks with working
frequency of 5 kHz excited with a square wave as transducers. The process of modulation,
reception, rectification and the comparator output can be seen in fig. 24. In this case, thewindow and the "AND" function were not implemented. A higher background noise level than
expected was recorded due to the dimensions of the tank and the working frequency, since the
S51

acoustic wave front travels through the water and through its walls, causing interference andbeating. A FLUKE 281 programmable generator was used to generate the signals, and a
Tektronix TDS2002C dual channel oscilloscope with a 70 MHz sampling frequency was used
to record the signals.

Fig. 24. Test with 5 kHz ceramics. a) Modulator. b) Modulated sinusoid. c) Received signal.
d) Rectified signal. e ) Filtered signal (red) and comparator output (yellow).
The second test used MASA E-180/220 ultrasound transducers (Massa, 2000) operating at 220
kHz. In this case, the detection and decision system was tested in air, from a reflective surface
40 cm from the transducer. The reason for using these transducers is that the working frequency
is similar to that used by signals of interest emitted by dolphins, being able to test all theelectronics in conditions close to reality. Fig. 25 shows the echo signal, the rectifier signal, thecomparator response, the window signal and the "AND" function signal. To record the signals,
a PC-based OWON VDS3102 oscilloscope with a 100 MHz sampling rate was used. In thiscase, the signal-to-noise ratio was in the order of 30 dB since there was no noise caused byreflections and interference.

S52

Fig. 25. Test with a 220 kHz ultrasound transducer. a-Echo signal. b-Rectified signal. cComparator output, d-Temporal window and e- “AND” function.
7. CONCLUSIONSThis paper presents preliminary studies of the development of a system for reception of signals
generated by cetaceans in order to study their activity. First, the physical characteristics of themarine environment that influence the recording of acoustic signals were analyzed. Then, atypical emission sequence was analyzed from the point of view of the power density spectrum,
verifying the expected bandwidth.
Taking into account the energy limitations at the buoy, it was decided to use the threshold andtemporal window technique to recognize the signals of interest and to proceed to record it for
its subsequent transmission to the receiving station on the coast.
Using a fish tank, a simulation was performed to test the designed detection system thatconsisted of generating a low frequency sequence, detecting it and transmitting it to a receiverthat emulated the receiving station.
Finally, the detection and decision system was tested with a generated high-frequency
ultrasound signal, verifying that it meets the expected conditions.
The system can support other low consumption sensors in order to transmit complementary
information, such as water temperature, relative humidity, salinity, UV radiation, andgeostationary data (GPS), among others.
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Acústica Inalámbrica entre Robots Submarinos y Boyas de Superficie Parte I”. Course:
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L. A. Kyhn, J. Tougaard , F. Jensen, M. Wahlberg, G. Stone, A. Yoshinaga, K. Beedholm,
P.T. Madsen (2009), “Feeding at a high pitch: Source parameters of narrow band, highfrequency clicks from echolocating off-shore hourglass dolphins and coastal Hector’sdolphins,” J. Acoust. Soc. Am. 125(3): 1783-1791. doi: 10.1121/1.3075600
L.A. Kyhn, F.H Jensen, K. Beedholm, J. Tougaard, M. Hansen, P.T. Madsen (2010),“Echolocation in sympatric Peale’s dolphins (Lagenorhynchus australis) and Commerson’s
dolphins (Cephalorhynchus commersonii) producing narrow-band high-frequency clicks,” J.
Exp. Biol 213: 1940-1949. doi: 10.1242/jeb.042440
P. T. Madsen, D.A. Carder, K. Bedholm. S.H. Ridgway (2005), “Porpoise clicks from asperm whale nose - convergent evolution of 130 kHz pulses in toothed whale sonars?,”
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Energy and Shannon Entropy in Acoustic Emission Signals for Damage
Evaluation in Andesite RockZittoa, M.E., Sassanoa,b, M.P., Muszkatsa,c, J.P., Filipussid,e, D.A., and Piotrkowskia,e, RaGrupo de análisis de series temporales no estacionarias y no lineales, Facultad de Ingeniería,
Universidad de Buenos Aires, ARGENTINA. series.nsnl@fi.uba.arbIngeniería de sonido, Universidad Nacional de Tres de Febrero, ARGENTINAc
Universidad Nacional del Noroeste de la Provincia de Buenos Aires, ARGENTINAdGerencia Desarrollos Tecnológicos y Proyectos Especiales, CNEA, ARGENTINAe
Escuela de Ciencia y Tecnología, Universidad Nacional de San Martín, ARGENTINA

ABSTRACT
Andesite rock samples were subjected to uniaxial compression tests monitored by Acoustic Emission(AE) for the assessment of the brittle fracture process. The identification and evolution of different
fracture regimes were based on the application of methods suited for non-stationary and nonlinear timeseries: Wavelet Transform (WT); Empirical Mode Decomposition (EMD); Hilbert-Huang Transform(HHT) and Shannon Entropy (SE). The jumps of accumulated AE energy, which indicate where thegreatest damage due to macro fractures occurs, were preceded by minimum entropy values of the EMD
components, which would indicate that they act as precursors to catastrophic damage.
Keywords: Wavelet Transform, Hilbert Huang Transform, Shannon Entropy, Andesite Rock,Fracture.

1. INTRODUCTION
The Acoustic Emission (AE) technique allows obtaining information about different processes
that occur inside a material subjected to stress by analyzing the non-stationary elastic waves
generated, dispersed, and finally detected. At a microscopic level, natural flaws act as stressconcentrators in rock, favoring brittle fracture. This nucleation process continues with aninterplay of microcrack growth and coalescence into macrocracks, leading to the final rupture
when the accumulation of energy dissipated in fractures reaches a critical level (Lockner,
1993). Thus, crack propagation is characterized by wideband AE (Rodriguez, 2019). The
generation and propagation of the elastic waves imply different spatial and temporal scales
ranging from the breakdown of atomic bonds to seismic failures, which makes thecharacterization with AE of fractures be an important complement in seismology (Filipussi,
2015).
The generated AE signals are of very low amplitude (10 μ𝑉) and high frequency (50 𝑘𝐻𝑧 to 1
𝑀𝐻𝑧) and are characterized by discrete oscillating pulse events (hits) that reach a maximum
amplitude in a rise time of the signal and are then damped (Grosse, 2008). It is possible toassess damage by computing the characteristic parameters of hits: energy, root mean square(RMS), amplitude, rise time, duration. Due to the essential connection between AE andseismology, some authors (Zhang, 2018; Carpinteri, 2009; Rao, 2005; Shiotani, 2001) evaluate
damage through the b-value, which means the accumulated frequency distribution of AE
amplitudes. The linear relationship in log-log scale rules in AE resembling the seismic
Gutenberg-Richter law.
Under the hypothesis that it is possible to associate characteristic frequencies to the different
damage processes, the authors of this work have performed previous studies, analyzing andapplying the Wavelet Transform (WT) to AE signals in specimens of andesite rock subjected
to uniaxial compression. A frequency band associated with the propagation and advance of
fractures in the material was selected through the determination of the maximum coefficientsS55

of the WT and the minimum of Shannon Wavelet Entropy (SWE) (Muszkats, 2019; Sassano,2017).
Following previous work, and taking account of the non-linearity of AE signals, an alternative
analysis was here performed: the Empirical Mode Decomposition (EMD). The method
consisted in searching characteristic modes, associated with the advance of dangerous cracks
with the application of the Shannon Mode Entropy (SME).
2. EXPERIMENT AND ANALYSIS METHODS
2.1 Experiment
Four cylindrical specimens were tested, 75 mm diameter and 150 mm length, coming from anandesite core extracted in the rock location of Cerro Blanco, Province of San Juan, Argentina.
These specimens were subjected to simple uniaxial compression tests, up to breakage. A
hydraulic servo type and closed-loop CGTS machine was used, with 100 tons capacity and0.12 mm/min actuator travel speed. Fig. 1 shows the experimental setup. The andesite specimen
is instrumented with AE sensors.
AE was monitored with three piezoelectric sensors. Two of them were attached to the surfaceof the specimen, tuned respectively to 60 𝑘𝐻𝑧 and 150 𝑘𝐻𝑧. The third one, which wasbroadband, was attached to the lower loading plate taking into account the ability of the metal
as a wide waveguide. The AE equipment, also shown in Fig. 1, was completed with a twochannel board PCI-2 PAC, (Physical Acoustic Corporation). The commercial software 𝐴𝐸𝑊𝐼𝑁
was used for the analysis and determination of the characteristic parameters of signals.
We present results obtained with specimen 3, whose signals corresponded to the broadband
sensor channel, 100 kHz - 1 MHz. This selection was to avoid the pre-filtering provided by theresonant sensors. The test lasted approximately 15 minutes, the sampling time was 𝑡𝑠 = 1 𝜇s,
and 75385 hits were obtained.

Fig. 1 Experimental setup and AE equipment.
2.2 Analysis methods
S56a) Wavelet Transform
WT is a widely used method for non-stationary time series analysis. This method allows
analyzing the data in a time-frequency plane from its decomposition taking a fixed basis, which
is obtained by scaling and displacing a mother wavelet, with different scales and temporal
displacements. The time-frequency resolution obtained with this method exceeds that obtainedwith the windowed Fourier Transform. (Bogges, 2009; Torrence, 1998).
In (Muszkats, 2019), we adopt the Continuous Wavelet Transform (CWT) that allows us
working with frequency bands of variable width that we formally define in Eq. 1%
𝑊𝑓(𝑢, 𝑠) = ∫&% 𝑓(𝑡)𝜓 ∗ ",$ (𝑡)𝑑𝑡(1)
Where 𝜓u,s(𝑡), displayed in Eq. 2, is the wavelet corresponding to scale s and time displacementu.
')&"𝜓",$ (𝑡) = 𝜓 8 $ 9 , 𝑠 > 0(2)√$
Taking discrete values for u and s (associated with a frequency), the squared coefficients of𝑊𝑓(𝑢, 𝑠) give us the power content of the signal at time 𝑢* = 𝑛𝑡$ and scale s.
The complex Morlet wavelet was used as the mother wavelet, which provided an improved
capture of the oscillatory character of the signal.
b) Empirical Mode Decomposition (EMD)
WT uses an a priori basis, which in general is unsuitable to analyze non-linear signals, and the
difficulty is usually saved with mathematical tricks, adding non-meaningful terms to theanalysis to fit the data.
At the beginning of the century, Norden Huang developed the EMD method for time series
analysis based on locally decomposing the series with a basis obtained from data. This allows
splitting the series into modes (Intrinsic Mode Functions, IMFs). An IMF must meet two
conditions: i) the number of zeros has to be equal to the number of local extremes, or at mostdiffer in one; and ii) the mean value of the upper and lower envelopes must be null at all points.
The first condition means that modes are oscillatory functions. The second condition meansthat the local mean value is zero. Data fitting improves. Additionally, the Hilbert transform
applied to IMFs leads to obtain instantaneous frequencies associated with each of them. The
entire process of calculating IMFs and then applying the Transform of Hilbert constitutes theHilbert-Huang Transform (HHT). (Huang, 1998; Huang, 2014).
The procedure for obtaining IMFs consists of first determining the upper and lower envelopesof the series. Secondly, the local mean value of both envelopes is calculated and then subtracted
from the original series points. If the resulting function meets the conditions i) and ii)
previously stated, the result constitutes the first IMF. If not, the procedure, called sifting, isreinitiated. The first IMF corresponds to the most tiny detail, i.e. the largest frequency. The
first IMF is substracted from the original data and the first residue is obtained. The procedure
is repeated on the first residue and the second IMF is obtained. The iteration permits to obtainthe successive IMFs. It finishes when it is not possible to extract more oscillations, which iswhen the corresponding residue is a monotonous function or it has at most one local extreme.
Thus, the decomposition of 𝑥 (𝑡) results in Eq. 3𝑥(𝑡) = ∑,(3)+-' 𝑓+ (𝑡) + 𝑟(𝑡)where 𝑟(𝑡) is the series trend, N is the number of modes and 𝑓𝑖 is the i-mode. By definition, theHilbert transformations of the 𝑓𝑖s are well behaved, so it is possible to calculate the
S57

instantaneous frequencies corresponding to each mode as a function of time (Huang, 2008;
Huang, 2014).
c) Shannon entropy
The Shannon Entropy (SE) is a measure of the intrinsic order of the signal, and the presence ofminima suggests a more organized behavior, for example, an impulsive response, due to abrupt
advancement of fractures, different from phenomena such as friction, fracture closure and noisein general that can be associated with high entropy values. (Shannon, 1948). SE was appliedby the authors in previous AE and WT work (Piotrkowski, 2009; Sassano, 2017; Muszkats,
2019b).
If we have the discrete values of a hit, 𝑥 (𝑡), measured with a certain sampling time 𝑡𝑠 and wecall them x𝑗, 𝑗 = 1,…, 𝑀, we can define the hit energy in arbitrary units (a.u.), (Eq. 4).
/
𝐸 = ∑0(4).-'C𝑥. D
We can define the fraction of energy corresponding to time 𝑗𝑡$ as in Eq. 5
12 3"

𝑝. = 4!
(5)
hence we can define the SE of 𝑥 (𝑡) as in Eq. 6
𝑆𝐸 = − ∑0.-' 𝑝. logC𝑝. D
3(6)

RESULTSa) Continuous Wavelet Transform (CWT)
In this section, we first recap some of the main results presented in (Muszkats, 2019), tocompare them with the EMD decomposition. Following the results of some of the authors of
this work (Zitto, 2015; Sagasta, 2018) it is possible to assign certain frequency bands to thedifferent processes that lead to a final break in brittle materials. To determine the band of
frequencies associated with incipient damage due to fractures we use the CWT, which allows
us to filter the hits around a certain frequency. At the beginning of tests, crack nucleation
occurs; the damage mainly consists of a large number of small cracks. The coalescence of thesemicro-cracks results in larger cracks of dangerous rapid advance, new micro-cracks emerge
from the macro-cracks, and the whole process repeats until the final collapse occurs.
Since the selected 𝑡𝑠 in the AE tests was 1 𝜇𝑠 the highest frequency we could detect was 500
kHz. Our study focused on frequencies ranging from 100 to 500 kHz. The CWT of all the hitscorresponding to the test was calculated. The frequency (scale) band corresponding to thehighest absolute CWT values was identified around 240 kHz. A few of these maxima occurredat the beginning of the test, and the occurrence increased after 4 minutes when fractures began
to appear in the sample. For other frequency bands, either a few maximum coefficients
appeared, or they appeared with equal density throughout the test. Therefore, the 240 kHz
frequency band could be assigned to damage connected with macro fractures in the specimen(Muszkats, 2019).
The evolution of AE accumulated energy was compared with those of each frequency band. It
can be observed in Fig. 2 that energy evolution curves are similar in shape, but do not explain
the whole energy of hits. Besides, the band with the highest energy content corresponds to afrequency of 121 kHz.

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Fig 2 Evolution of the accumulated AE energy and the accumulated CWT energy of thedifferent bands.
b) Empirical Mode Decomposition (EMD)
The EMD and the instantaneous frequency evolution (HHT) were calculated for each hit, toassociate the different modes in the decomposition with different mechanisms that generate
AE. Five to six modes were obtained, depending on each particular hit. Fig. 3 shows the first
three IMFs that correspond to characteristic hits. The amplitude of the first mode is similar to
the amplitude of the full hit; for the second mode, the amplitude is 25% of the amplitude of thefirst mode; and overall, the amplitude decreased in the successive modes, becoming negligible.
Fig. 4 shows HHT results for the same hits graphed in Fig. 3. The instantaneous frequencies ofmodes 1 and 2 are within the sensor bandwidth. Frequencies of mode 1 correspond to the 200300 kHz interval, and the intensity is appreciable only during a minor interval of the hit,
especially at the beginning. The frequencies for mode 2 are in general lower than 170 kHz andtheir intensities extend throughout the entire hit. These results would suggest that we could
associate the impulsive mode 1 to the rapid advance of macro fractures. This agrees with theresults obtained with CWT.
Then we calculated the accumulated energy for each mode comparing it with the AE energy.
Results are in Fig. 5. We can observe that mode 1, with instantaneous frequencies ranging
between 200 and 300 kHz, contains approximately 90% of the AE energy, which is otherwise
negligible for modes 3 and onwards. Contrarily, with the filtering performed with CWT, theenergy in the 240 kHz band is much lower than the energy of hits. This constitutes a goal of
the EMD decomposition, which takes into account the nonlinearity of the signal. Due to theadaptive basis employed, more precision and conciseness are achieved, and the severe fracture
process could be more confidently assigned to mode 1.
c) Shannon Mode Entropy (SME)
Finally, we calculated the SME for all the hits, studying their evolution throughout the entire
test. Since entropy is very variable from hit to hit, we performed a moving average smoothing
involving 700 hits. Fig. 6 compares the SME of modes 1 and 2 with the accumulated AE energy
for the second half of the test. The graph shows that the SME for modes 1 and 2 exhibits a very
similar shape. The jumps of accumulated energy indicate where the greatest damage due tomacro fractures occurs. Noticeably, these energy jumps are preceded by minimum entropy
values, which would indicate that they are precursors to catastrophic damage.
S59

Fig. 3 Three characteristic hits with their corresponding first three modes of the EMD
decomposition.

S60

Fig. 4 Instantaneous frequency for the hits in figure 3. Red: mode1, blue: mode 2 and green:mode 3.

Fig. 5 Accumulated energies of the first three modes in the EMD decomposition comparedwith the accumulated AE of hits during the whole test (left) and in the last half part of the test
(right).
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Fig. 6 Smoothed SME modes 1 and 2 compare with the AE accumulated energy AE energy
scale is different from that of Fig. 5 for clarity reasons.
4

CONCLUSIONS

AE signals from uniaxial compression tests on Andesite rock previously analyzed with WT
were analyzed in the present paper with EMD and SE. With both WT and EMD analysis
methods, the frequency of around 240 kHz could be assigned to the mechanisms of formation
and rapid advancement of macro fractures. However, the energy dissipated in that band could
not be quantitatively determined with the WT. In this sense, the EMD showed its potential for
analyzing signals from non-stationary and non-linear processes. Comparison of the Shannon
entropy of modes (SME) and AE energy throughout the test showed that the minimum entropy
values are precursors to abrupt jumps of energy that identify severe fracture damage.
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fracture regimes in andesite rock via the energy evolution of acoustic emission signals in
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LA EMISIÓN ACÚSTICA, SU HISTORIA EN LA
ARGENTINA
López Pumarega M. I.a, b, Ruzzante J. E.b, c, d, Gómez M. P.b, c, eaProyecto Centro Internacional para Estudios de la Tierra (ICES), Comisión Nacional deEnergía Atómica y Universidad Nacional de Cuyo, Mendoza, Argentina.
b
Grupo Latinoamericano de Emisión Acústica GLEA.
cFac. Reg. Delta, Universidad Tecnológica Nacional, Campana, Buenos Aires, Argentina.
dUniversidad Tres de Febrero, Caseros, Buenos Aires, Argentina.
eDpto. ICES y Ondas Elásticas, Gerencia de Área Investigaciones y Aplicaciones No Nucleares,
Comisión Nacional de Energía Atómica, Centro Atómico Constituyentes, San Martín, Buenos
Aires, Argentina.
e-mail: ilopezpumarega@gmail.com
La Emisión Acústica (EA) es un método de Ensayo No Destructivo (END) que es ampliamente
utilizado para estudios e investigaciones en ciencias de los materiales. Los inicios de esta técnica
en el mundo se remontan al siglo XX con Kishinoue en la década del 30 en Japón y a Obert en
Estados Unidos de América en la década del 40, pero como referencia científica fundacional deltema, se puede mencionar la Tesis Doctoral de Joseph Kaiser en Alemania en 1950. Kaiser abordó
el problema concreto de las ondas elásticas producidas por los metales durante su deformación ylo estudió a partir de registrarlas mediante instrumentación electrónica. Con el avance tecnológico
de las últimas décadas, los equipos y sistemas de EA han alcanzado altos desempeños y bajos costos
en relación a sus antecesores, permitiendo ampliar y diversificar sus posibilidades de aplicación.
Actualmente la EA es utilizada en ensayos industriales para evaluar la integridad de grandes
estructuras y componentes, y también en una amplia variedad de líneas de investigación enmateriales, como metales, plásticos, concreto, rocas, maderas, compuestos, etc. En este trabajo seresumen las actividades de la EA en la Argentina desde sus orígenes en la década del 60, hasta
nuestros días, incorporando una breve reseña de los avances en su Normalización a nivel nacional(IRAM), regional (MERCOSUR) e internacional (ISO). Se presentan también la creación del Grupo
Latinoamericano de Emisión Acústica (GLEA) en 1997 y una breve reseña histórica desde 1999 delos Encuentros científico-tecnológicos llamados E-GLEA.

Palabras Clave: Emisión Acústica, GLEA, Normalización, Argentina.
1. INTRODUCCIÓN
La detección temprana de daño en los materiales de componentes y estructuras
industriales, es un tema de enorme importancia económica y de seguridad ambiental, ya
que permite disminuir los costos de fabricación y mantenimiento, y los riesgos para lostrabajadores y para el público en general. En ese sentido, la Emisión Acústica (EA), queforma parte de los Ensayos No Destructivos (END), es una herramienta fundamental. Lastécnicas de END, y en particular la EA, son valiosas para la caracterización de materiales
y la identificación y cuantificación de los distintos procesos físico-químicos que ocurren en
los mismos. La particularidad de la EA es su capacidad para detectar “on line” lasmodificaciones en el estado de tensiones de los materiales y por ende detectar la aparición
de procesos de falla incipientes, posibles precursores de cambios catastróficos aún en

S65

regiones alejadas de una estructura. Incluso la técnica se utiliza desde hace unos 30 años enminas y en el estudio de la corteza terrestre, apuntando a la predicción de sismos yerupciones volcánicas.
La EA, que es el nombre tanto del proceso como de la técnica, está constituida por
ondas elásticas generadas por la redistribución repentina del campo de tensión-deformación
en materiales o estructuras sometidos a carga externa. La EA es un fenómeno dinámico.
Ocurre en el inicio o crecimiento de fisuras, en apertura y cierre de fracturas, deformación,
movimiento de dislocaciones, formación de cavidades, corrosión, rotura de ligaduras enmateriales compuestos, etc. Estas ondas se propagan a través del material y eventualmente
llegan a la superficie donde producen pequeños desplazamientos temporarios. Son ondas
de baja amplitud y alta frecuencia, normalmente ultrasónicas. Por esta razón, se requierepara su detección transductores piezoeléctricos muy sensibles. Las fuentes de EA estángeneralmente relacionadas con “daño”. Por ello, la detección y el análisis puede ser usado
para evaluar el comportamiento dinámico del material bajo condiciones de carga y de estemodo predecir su falla.
La diferencia principal con otros métodos de END tales como Rayos X, Ultrasonido,
etc., es su capacidad de detectar cambios en tiempo real. De este modo, usando un sistema
de EA, podemos monitorear “in situ” el comienzo y el desarrollo de daño en el material ola estructura, aun cuando ello no sea posible por medio de la inspección visual.
2.

ORÍGENES DEL MÉTODO DE EAHistóricamente los antecedentes del uso de la EA se remontan a tiempos muy
remotos, ya que seguramente pudo ser escuchada en forma directa en diversos fenómenos
naturales como, por ejemplo, en los movimientos sísmicos, el congelamiento del agua alformarse hielo, la fractura de rocas y árboles, etc. Es posible, según los antropólogos, quelos primeros alfareros que horneaban cerámicas, también percibieran los sonidos emitidos
durante el enfriamiento de las mismas, para evaluar el proceso. También, al alcanzar elgrado de pureza necesario del estaño, se podrían haber producido las primeras experienciasdel “grito del estaño”, sonido audible que se genera cuando se provoca deformación plástica
por maclado, ya que las piezas más antiguas conocidas de ese metal puro, datan del año
2650 a.C. En la edad media ya se había documentado la existencia de ruidos originados
cuando un acero es templado, detectándose con esos sonidos la transformación
martensítica. (Drouillard, 1996). Históricamente la EA ya figura en textos escritos durante
el siglo IIX en el actual Irak, tal como lo cita Ruzzante J. et al (2016), relacionada con elsonido producido al deformar el estaño, y en Latinoamérica en el siglo XVII, referido al
enfriamiento del hierro.
En las ciencias médicas, también los sonidos que produce el cuerpo humano hancontribuido ampliamente para el estudio de la salud y la enfermedad, primero en forma
directa con el oído del médico y luego mediante el invento del estetoscopio (Palacios T.,
2001). También hay antecedentes históricos del uso de dispositivos similares por Leonardo
da Vinci para detectar barcos cercanos a la costa. Hasta acá se está considerando la EA
como un fenómeno ondulatorio en la parte audible del espectro de frecuencias.
En la primera mitad del siglo XX, hay documentos que muestran primeros estudios
sistemáticos basados en la medición de la producción de ondas elásticas, como por ejemplo
en Japón, adonde Kishinoue estudió la fractura de una varilla de madera sometida a una
tensión flexural, utilizando como transductor (sensor) una cápsula de fonógrafo.
En los Estados Unidos de América, Robert J. Anderson en 1925, informo en su Tesis
que, durante los ensayos de tracción de una aleación de aluminio, entre el punto de fluencia
y cerca de la carga de rotura, los incrementos de carga producían una serie deS66

deslizamientos que daban lugar a una serie de líneas aserradas o bandas de Lüders, que eranacompañados por una serie de "clics o sonidos audibles" que dependían del espesor de lachapa, siendo más agudos para una chapa delgada. Se observa así, cómo a lo largo deltiempo se amplió el conjunto de materiales donde se detectaba el fenómeno de la EA.
(Drouillard, 1996).
La etapa científico-tecnológica de la EA se inicia en Alemania durante 1950 con laprimera Tesis Doctoral de Joseph Kaiser (1950): “Estudio del Fenómeno Acústico en losEnsayos de Tracción”, en la Universidad Técnica de Múnich, relacionada con elcomportamiento de los metales ante la tracción.
Luego el método y las técnicas de aplicación de la EA han evolucionado rápidamente,y hasta la actualidad se han publicado miles de trabajos.
3. LA EA EN LA ARGENTINA
En el Centro Atómico Bariloche de la Comisión Nacional de Energía Atómica
(CNEA), el Dr. Roberto Pascual durante el año 1971 inició las actividades de EA en
Argentina. Entre ese año y 1980 se realizaron trabajos de investigación en diversas
aleaciones de cobre y de aluminio que produjeron varias publicaciones y una Tesis deDoctorado. Luego se formó otro Grupo en la Universidad de Córdoba, que también realizó
publicaciones y una Tesis de Doctorado (Palacios T., 2001).
En el Centro Atómico Constituyentes de la CNEA, en 1979 el Dr. José. Ruzzante
formalizó las actividades de un Grupo de Trabajo dedicado íntegramente a la investigación
científico-tecnológica y la promoción de desarrollos en EA. Dicho grupo continúa en la
actualidad dirigido por el Dr. Martín Gómez, habiéndose publicado numerosos trabajos
científico-tecnológicos y Tesis de Grado, Maestría y Doctorado. Sus áreas de trabajo
relacionadas con la EA y las ondas elásticas, se aplican en varios temas diferentes: metales,
plásticos, cementos, materiales compuestos, concreto, materiales odontológicos, rocas,
ciencias de la tierra, emisión magneto acústica, adherencia de recubrimientos (“scratchtest”) y aplicaciones en geofísica. Se realizan también desarrollos tecnológicos de equipos
especiales.
Asimismo, se formó otro Grupo relacionado con las aplicaciones de la EA en elInstituto de Matemática, Astronomía y Física, I.M.A.F. de la Universidad Nacional de
Córdoba y otro en la Facultad de Ingeniería de la Universidad de Buenos Aires. En ambos
casos hubo varias publicaciones internacionales y Tesis de Doctorado (Palacios T., 2001).
En 1997 comenzaron los trabajos en el Instituto de Materiales y Suelos de la Facultad
de Ingeniería de la Universidad Nacional de San Juan de la mano del Ing. M. A. Sabio
Montero. En ese mismo año, el Dr. Horacio Lescano incluye la EA entre las tareas del grupo
de Vibraciones mecánicas (GVM) de la Facultad Regional Delta de la Universidad
Tecnológica Nacional, a partir del cual se nuclea el Grupo de Emisión Acústica (GEA) que
actualmente dirige el Dr. Martín Gómez.
En el ámbito científico académico existen otros Grupos que trabajan o han trabajado
en los últimos 15 años en investigación y desarrollo en EA, o que la aplican como método
de evaluación en diversos procesos: Entre estos se pueden mencionar los Grupos de Daño
por Hidrogeno y de Corrosión, ambos de la Gerencia de Materiales de la CNEA; el Grupode Materiales Compuestos del ITPN (CONICET-FI UBA), la Facultad de Ingeniería de la
Universidad Nacional de Río Cuarto, provincia de Córdoba; la Facultad de Ingeniería de la
Universidad Nacional de Misiones UNaM en Oberá, Misiones; en la Carrera de Ingeniería
en Sonido, Universidad Nacional de Tres de Febrero UNTREF, en Caseros, Buenos Aires;y el DME-FAIN de la Facultad de Ingeniería de la Universidad Nacional del Comahue en
Neuquén, donde se realiza este E-GLEA 10.

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4. LA COMUNIDAD DE EA EN EL MUNDO
Pocos años después del inicio de los estudios e investigaciones de la nueva técnica
de EA, sus especialistas, técnicos y usuarios, comenzaron a organizarse en varias partes delmundo. Así en EEUU se creó en 1967, el “Acoustic Emission Working Group, AEWG”,
para centralizar la información, compatibilizar la terminología y las técnicas
experimentales, y compilar e intercambiar experiencias. Su primer Encuentro fue en 1968.
En Japón comenzó a reunirse en 1969 el “Japanese Commitee on Acoustic Emission(JCAE)”. Por otro lado, Europa comenzó la organización de la comunidad usuaria de la EA
en 1972, con la “Physics Conference on Acoustic Emission” en Londres, allí se creó el“European Working Group on Acoustic Emission, EWGAE” (Ruzzante J. et al, 2016).
En Asia el primer grupo de trabajo nacional en EA fue el de la India, creado en 1985.
El grupo nacional de Sud África denominado "South African Working Group for Acoustic
Emission" se creó en 1989. En Corea se creó en 1990 el "Korean Working Group on
Acoustic Emission, KWGAE” (Palacios T., 2001).
Todos estos Grupos periódicamente organizan Congresos Internacionales donde sepresentan los últimos trabajos de investigación, desarrollo y aplicaciones de la EA.
5. LA COMUNIDAD DE EA EN LATINOAMÉRICA
En Argentina, con la necesidad de vincular a los especialistas, técnicos y usuarios
relacionados con la EA, el 4 de abril de 1997, se creó en la CNEA el Grupo
Latinoamericano de Emisión Acústica, GLEA Los miembros fundadores por Argentina
son: Dr. J. E. Ruzzante (CNEA), Dra. M. I. López Pumarega (CNEA), Téc. M. Armeite(CNEA), Dr. C. E. D'Attellis (CNEA), Ing. T. A. Palacios (CNEA); Dra. R. Piotrkowski(Universidad Nacional de San Martín); Ing. M. A. Sabio Montero (Universidad Nacionalde San Juan), y el Ing. P. Feres Filho de Brasil (López Pumarega M. I. et al., 2002; Ruzzante
J. et al, 2004).

Figura 1. Miembros Fundadores del GLEA: R. Piotrkowski, T. Palacios, J. E. Ruzzante, M. I. López
Pumarega, M. Armeite, P. Feres Filho, M. A. Sabio Montero.

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Los objetivos del GLEA son puramente científicos y tecnológicos: promover,desarrollar, investigar, realizar reuniones, en temas de EA, y fomentar la incorporación denuevos adherentes interesados en estos temas. La participación en el GLEA es
absolutamente gratuita.
El GLEA dispone de una página web y un correo electrónico
(https://www.cnea.gob.ar/cac/glea/). En la Fig. 1, se puede ver una fotografía de losmiembros fundadores del GLEA. Con posterioridad se fueron incorporando más de 200
especialistas, técnicos y usuarios de Sudamérica y de España.
5.1.

ENCUENTROS E-GLEA

Para promover las actividades y generar un espacio de discusión de temas, desde
1999, y cada dos años, el GLEA organiza Encuentros Internacionales, denominado EGLEA, ya habiéndose realizado 9 ediciones del mismo. El primero de estos Encuentros, el
E-GLEA 1, se realizó en el Centro Atómico Constituyentes de la CNEA, en Buenos Aires,del 10 al 14 de septiembre de 1999. En esa ocasión se invitó al especialista Prof. Dr. Kanji
Ono, de la Universidad de California, EEUU, uno de los más reconocidos investigadores
en EA, creador y editor de la revista especializada “Journal of Acoustic Emission”. A travésde esa publicación se difundieron las actividades realizadas durante el E-GLEA 1 a lacomunidad internacional (ver Fig.2).
Los E-GLEA son un ámbito de encuentro, discusión y difusión de los nuevos trabajos
de investigación y aplicaciones de la EA. Año por medio, se fueron realizando los distintos
Encuentros internacionales del GLEA de acuerdo con el siguiente resumen:
•
•
•
•
•
•
•
•
•
•

E-GLEA 1: Buenos Aires, Argentina, 6-10 de septiembre de 1999.
E-GLEA 2: Buenos Aires, Argentina, 10-14 de septiembre de 2001.
E-GLEA 3: Río de Janeiro, Brasil, 2-6 de junio de 2003.
E-GLEA 4: Taormina, Italia, 16 al 19 de octubre de 2005.
E-GLEA 5: Universidad Nacional de Misiones, Oberá, Misiones, Argentina,
durante este Encuentro se celebraron los 10 años del GLEA, 29 de agosto al
1 de septiembre de 2007.
E-GLEA 6: Universidade de Rio Grande Do Sul, Porto Alegre, Brasil, 16 deseptiembre de 2009.
E-GLEA 7: Universidad Tecnológica Nacional, Regional Mendoza,
Mendoza, Argentina, 25-27 de agosto de 2011.
E-GLEA 8: Universidad Nacional de Tres de Febrero, UNTREF, Caseros,
Buenos Aires, Argentina, 10-12 de septiembre de 2013.
E-GLEA 9: Facultad de Ingeniería, Universidad Nacional de Misiones, Oberá,
Misiones, Argentina, celebrándose los 20 años del GLEA, del 29 al 31 deagosto de 2017.
E-GLEA 10: Facultad de Ingeniería, Universidad Nacional del Comahue,
Neuquén, Provincia de Neuquén, Argentina 14 al 16 de agosto de 2019, donde
se presenta este trabajo.

Como se puede ver, en 6 ocasiones se pudieron privilegiar como lugares derealización de los Encuentros, los ámbitos universitarios afines a la EA, pudiéndose así
fomentar la participación de los estudiantes. En todos los E-GLEA participaron enpromedio más de 60 especialistas, investigadores, técnicos y usuarios de la EA.
Desde sus inicios, los Encuentros contaron con la colaboración de varias instituciones
extranjeras y argentinas, incluida la Agencia Nacional de Promoción Científica yS69

Tecnológica a través del Fondo para la Investigación Científica y Tecnológica (FONCYT)
y sus subsidios para Reuniones Científicas.
En las Fig. 2 a 4 y 7 a 12, se muestran fotografías de los participantes en losencuentros E-GLEA. Las fotos de la Figuras 5 y 6 se insertan en orden cronológico a lasantes mencionadas y corresponden a unas Jornadas de Emisión Acústica y Estructuras
Geofísicas, auspiciadas por el GLEA, la primera en San Juan, Argentina en 2003 y lasiguiente en Malargüe, Mendoza, Argentina, en 2004. En esas reuniones comenzaron asentarse las bases de lo que luego sería el “International Center of Earth Sciences” (ICES)
fundado entre la CNEA y la Universidad Nacional de Cuyo, en conjunto con investigadores
del Consiglio Nazionale delle Ricerche (CNR) e importantes instituciones científicas deItalia.

Figura 2. E-GLEA 1, Portada de la revista “Journal of Acoustic Emission, Vol. 17, No. 1-2/JanuaryJune 1999”, gentileza Prof. Dr. Kanji. Ono, Centro Atómico Constituyentes, Buenos Aires, Argentina, 6-10
de septiembre de 1999.

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Figura 6. 1as. Jornadas de Emisión Acústica y
Estructuras Geofísicas, Malargüe, Mendoza,
Argentina, 20-21 de diciembre de 2004.

Figura 3. E-GLEA 2, Centro Atómico
Constituyentes, Buenos Aires, Argentina, 10-14
de septiembre de 2001.

Figura 7. E-GLEA 4, Taormina, Italia, 16-19 deoctubre de 2005.

Figura 4. E-GLEA 3, en el marco del ”3rd. PanAmerican Conference for Nondestructive
Testing (3 PANNDT)”, Río de Janeiro, Brasil, 26 de junio de 2003.
Figura 8. E-GLEA 5 y Celebración de los 10
años del GLEA. Universidad Nacional de
Misiones, Oberá, Misiones, Argentina, 29 deagosto al 1 de septiembre de 2007.

Figura 5. Jornada sobre Emisión Acústica en
Estructuras (Geofísicas y de Ingeniería Civil),
Instituto Nacional de Prevención Sísmica
INPRES, San Juan, Argentina, 5 de diciembre de
2003.

S71

Figura 9. E-GLEA 6, Universidad Federal de
Rio Grande do Sul (UFRGS), Porto Alegre,
Brasil, 16 de septiembre de 2009.

Figura 10. E-GLEA 7, Facultad Regional
Mendoza, Universidad Tecnológica Nacional,
Mendoza, Argentina, 25-27 de agosto, 2011.

Figura 11. E-GLEA 8, Universidad Nacional de Tras de Febrero UNTREF, Caseros, Buenos Aires,
Argentina, 10-12 de septiembre de 2013.

Figura 12. E-GLEA 9 y Celebración de los 20 años del GLEA, Facultad de Ingeniería, Universidad
Nacional de Misiones, Oberá, Misiones, 29 al 31 de agosto de 2017.

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6.

LA NORMALIZACIÓN EN EA

Desde hace más de 30 años, en la Argentina existen varias empresas, subsidiarias deotras extranjeras y también algunas nacionales, que realizan ensayos de EA en el ámbito
industrial e inclusive algunas que diseñan y fabrican equipos especiales.
A medida que avanzaban el uso y las aplicaciones de la EA en Argentina y con el
fin de obtener un nivel de ordenamiento tecnológico mínimo, fue necesario poder crear
disposiciones, protocolos o documentos específicos. Así surgió la necesidad de comenzar
a trabajar en la “Normalización” de las actividades de EA.
Una Norma es un documento, que, aprobado por consenso en un organismo
reconocido, establece reglas y criterios para usos comunes y repetidos. Las Normas están
en todas partes y nos protegen a todos. Los productos elaborados conforme a Normas son
más aptos, más seguros y de mejor calidad. Aseguran la compatibilidad de los productos
y la disponibilidad de repuestos que prolongan la vida útil del objeto, facilitando elcomercio y colaborando con la regulación del mercado, permitiendo la transferencia detecnología.
La actividad de “Normalización” es llevada a cabo, en cada país, por un Organismo
de Normalización: entidad reconocida a escala nacional, regional o internacional. Cada
nación del mundo tiene su propio Organismo Nacional de Normalización. En Argentina
es el Instituto Argentino de Normalización y Certificación IRAM. Este Instituto lleva
adelante las actividades de Normalización desde el año 1935, y fue reconocido en 1994
por el Poder Ejecutivo Nacional, como el Organismo de Normalización de la República
Argentina. Es un ente privado, no estatal, en el que participan representantes de consumo,
producción, usuarios, gobierno, comunidad científico-técnica, fabricantes de productos,
etc. Desarrolla, estudia y publica Normas argentinas en todos los campos de actividad. Se
debe destacar que el IRAM fue el primer Organismo de Normalización creado en América
Latina, llevando elaboradas más de 13000 Normas aplicables en muy diversos ámbitos.
El proceso de elaboración de Normas se hace a través de los Organismos de Estudio(Comisiones), con la participación de representantes de distintas organizaciones que
pertenecen a los tres sectores involucrados en su creación: los productores, losconsumidores y los responsables de velar por el interés general y el bien común. Todos,
excepto el personal del IRAM, tienen carácter honorario
En Argentina, debido a los altos estándares de calidad y seguridad que se deben
aplicar en la industria nuclear, a iniciativas de la CNEA, en 1969 se creó en el IRAM el
Subcomité de Ensayos No Destructivos, con competencia específica en todas las áreas deeste tipo de ensayos. Recién en junio de 2000, también a pedido de la CNEA, se creó en
el IRAM la Comisión de EA. Esta Comisión funciona desde entonces en forma autónoma
y continua, reuniendo en promedio 5 personas en cada convocatoria mensual.
En la década de los 90, debido a la creciente regionalización latinoamericana, se creóla Asociación MERCOSUR de Normalización (AMN). Es el único organismo responsable
por la gestión de normalización voluntaria en el ámbito del MERCOSUR. En junio de2004, a propuesta de la Comisión de EA del IRAM, junto con su equivalente de Brasil(Associação Brasileira de Normas Técnicas, ABNT), se creó en ese ámbito el Comité
Sectorial MERCOSUR 24 (CSM 24) con competencia específica en el tema de los END,
estableciéndose también el Subcomité de Emisión Acústica. En la actualidad existen más
de 12 Normas de EA publicadas en el IRAM y/o en el MERCOSUR (Catálogo de Normas
IRAM y Asociación Mercosur de Normalización).
A nivel internacional también las actividades en la creación y adopción de Normas
de EA, ha ido progresando rápidamente. En la “International Organization forStandardization ISO”, existía el “Technical Committee TC135” para todos los END, pero

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en el año 2007 el CSM 24, propuso la creación de un grupo específico para el estudio dela EA, el “Sub Committee SC9”. Su Secretaría en la actualidad pasó de Brasil a China. En
este Subcomité participan activamente 14 países y otros 5 más son “Observadores”. A su
vez, cada Subcomité crea Grupos de Trabajo (“Working Groups, WG”) para tratar temas
específicos, convocando a reconocidos especialistas internacionales. En esos Grupos deTrabajo, participan por Argentina, varios reconocidos especialistas de CNEA.
En Europa además existe el “European Committee for Standardization” CEN, que
reúne a los Institutos de Normalización nacionales de 33 países europeos, desarrollando ypublicando Normas europeas. Desde octubre de 1991, se aprobó formalmente un Acuerdo
de Cooperación Técnica entre la ISO y el CEN, tratando así de no duplicar las tareas.
De acuerdo a una revisión internacional realizada por una empresa alemana
fabricante de equipos de EA, existen a nivel internacional, más de 100 Códigos y Normas
relacionadas con este método de END (Vallen–Syteme GmbH, Release September 2013).
En la cita de la referencia, las Normas, Códigos y Prácticas están ordenadas por institución
productora (ISO, ASNT, CEN, API, EPRI, ASME, IEEE, etc.), actualizadas al año 2013,
con su correspondiente codificación, título, fecha de edición y comentarios. Las Normas
desarrolladas en Latinoamérica no han sido incorporadas en ese documento aún, pero
pueden ser consultadas en la página web de la AMN, CS24.
La Agencia Internacional de Energía Atómica (OIEA por sus siglas en inglés),dependiente de las Naciones Unidas, también desarrolla el tema de la EA en su Documento
Técnico: IAEA-TECDOC 628/Rev.1. La Sección V del Código ASME, en su Artículo 12
describe el proceso de examinación por EA, de recipientes metálicos durante la prueba depresurización (ASME Code, Section V, ARTICLE 12). El Artículo 13, describe losrequerimientos a usarse durante la monitorización continua por EA, en contornos derecipientes a presión metálicos y no metálicos usados en servicio, en la industria nuclear yno nuclear, (ASME Code, Section V, ARTICLE 13). En la Publicación No. 322 del“American Petroleum Institute, API”, se hace una evaluación ingenieril del método de EA
para la detección de pérdidas en tanques sobre tierra, para almacenamiento.
Gran parte de la actividad de personas en el área de los END está reglamentada aescala nacional e internacional, a través de las Normas específicas para cada Método de
END, y en la Norma de Calificación y Certificación de Personal, que se dedica a laaplicación de los END en la industria. Es ésta la Norma NM-ISO 9712 para la Calificacióny Certificación de Personal de END. Allí están comprendidas todas las actividades
industriales de EA, con los requisitos, competencias y obligaciones para los Operadores e
Inspectores de EA, en sus tres posibilidades: Nivel 1, Nivel 2 y Nivel 3.
El IRAM está acreditado por el Organismo Argentino de Acreditación (OAA) parala Certificación de Personas y éste a su vez, delega la facultad de la Calificación de
Personas en los Organismos de Calificación Acreditados (OCA), certificando sus acciones.
En la Argentina existen pocos OCAs, entre ellos se pueden citar: la CNEA, la Universidad
Tecnológica Nacional (UTN) Facultad Regional Delta y la UTN Facultad Regional
Mendoza. Todos ellos están habilitados para la Calificación de Personas en los métodos
de END en los que cada uno es competente, desempeñándose conforme a las indicaciones
de la NM-ISO 9712.
Teniendo como marco Norma NM-ISO 9712, en la Argentina, aún no se ha podido
establecer definitivamente el Sistema de Calificación de Personas que operan con latécnica de EA. En el año 2007, desde la CNEA y a través del IRAM, se pudo completar
en forma provisoria el esquema de Certificación para el Nivel 1 de EA, llegando a dictarse
también el primer Curso de Nivel 2, pero luego por diversas circunstancias fortuitas este
proceso fue interrumpido. Por las particularidades del método de EA, la tarea de laNormalización de las actividades que profesionales y técnicos desarrollan dentro delS74ámbito de los END, es muy importante y compleja de realizar, siendo ésta una tarea todavía
no concretada que debe encararse seriamente entre todos los involucrados con este método.
A nivel nacional existen al menos 8 documentos gubernamentales nacionales yprovinciales que nombran específicamente a la EA (Ruzzante J. et al, 2016). La
documentación en el ámbito nacional y provincial donde se menciona la EA pueden
encontrarse en el portal de “Información Legislativa y Documental Oficial”, especificando
el tipo de documento y el año de su publicación.
De todo lo escrito anteriormente, se desprende la enorme importancia del uso de la
EA en el mundo y en particular en la Argentina, estando presente inclusive en la
documentación legal nacional y provincial.
7. CONCLUSIONES
Se han mostrado el avance y los logros más importantes de todos los colegas que
han dedicado su trabajo y su esfuerzo a profundizar la comprensión de la EA, difundir su
uso, ampliar sus posibilidades y potencialidades de aplicación, y obtener un nivel deordenamiento tecnológico mínimo a través de la tarea de la Normalización.
Una prueba fehaciente de esos frutos es la realización de este Décimo Encuentro delGrupo Latinoamericano de Emisión Acústica E-GLEA 10, en el ámbito de la Universidad
Nacional del Comahue en la ciudad de Neuquén, con la presentación Oral de más de 30
Trabajos de diversos campos de aplicación de la EA.
La presentación de este Trabajo, se ha realizado como tributo de homenaje a toda lacomunidad de los investigadores, profesionales, técnicos y usuarios de la EA en América
del Sur y especialmente en Argentina. Todos ellos, desde hace muchos años han sabido
unirse para profundizar el conocimiento en esta área de la ciencia, compilar e intercambiar
sus experiencias en fraterna comunidad que potencia y aumenta la sinergia de sus trabajos
individuales. Parte de esa tarea ha sido posible gracias a la creación y funcionamiento delGrupo Latinoamericano de Emisión Acústica GLEA y de todos sus integrantes.
REFERENCIAS
Acoustic Emission Examination of Metallic Vessels During Pressure Testing, ASME
Code, Section V, ARTICLE 12.
Acoustic Emission Working Group, AEGW, http://www.aewg.org/
An Engineering Evaluation of Acoustic Methods of Leak Detection in Aboveground
Storage Tanks, API Publication Number 322.
Asociación Mercosur de Normalización, AMN, https://www.amn.org.br/
Asociación Mercosur de Normalización:
https://www.amn.org.br/Content/Documentos/ptCartilha_AMN_FINAL2.pdf
Catálogo de Normas IRAM, http://www.iram.org.ar/index.php?id=Catalogo-de-normas
Code, Standards, Practices, and Guidelines related to Acoustic Emission (AE), Vallen–
Syteme GmbH, Release September 2013.
Continuous Acoustic Emission Monitoring, ASME Code, Section V, ARTICLE 13.
Drouillard, T.F., A History of Acoustic Emission, Journal of Acoustic Emission, vol. 14,
N°1, pp. 1-34, 1996.
European Committee for Standardization, https://www.cen.eu
European Working Group on Acoustic Emission (EWGAE), http://www.ewgae.eu/
Grupo Latinoamericano de Emisión Acústica (GLEA),
https://www.cnea.gob.ar/cac/glea/
Información Legislativa y Documental Oficial INFOLEG, www.infoleg.gov.ar

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Instituto Argentino de Normalización y Certificación IRAM,
http://www.iram.org.ar/index.php
International Standard Organization ISO, https://www.iso.org/standards.html
Kaiser J. Untersuchung über das Auftreten von Geräuschen beim Zugversuch, Dr.-Ing.
Dissertation, Fakultät für Maschinenwesen und Elektrotechnik der Technischen
Universität München (TUM); 1950.
López Pumarega M. I., Piotrkowski R., Ruzzante J. E., The history of the Latin American
Acoustic Emission Group (GLEA), NDT.net, Vol. 7 No.09, September 2002.
Normas MERCOSUR de END, https://www.amn.org.br/Paginas/NormasGeral
Palacios T., Antecedentes históricos de la Emisión Acústica en Latinoamérica, Primer
Encuentro del Grupo Latinoamericano de Emisión Acústica, E-GLEA 1,
http://www.cnea.gov.ar/cac/glea/trabajos/palacios.pdf, 2001.
Ruzzante J. E., López Pumarega M. I., Piotrkowski R., The status of Acoustic Emissionin Argentina, Insight, ISSN: 1354-2575, Vol. 46, No. 12, pp.733-735, 2004.
Ruzzante J., López Pumarega M. I., Emisión Acústica, un diálogo con los Materiales,
https://www.ciencias.org.ar/user/Ruzzante.pdf, 2016.
Training guidelines in non-destructive testing techniques, IAEA-TECDOC 628/Rev.1.
Vallen–Syteme GmbH, Code, Standards, Practices, and Guidelines related to Acoustic
Emission (AE), Release September 2013,
http://www.vallen.de/zdownload/pdf/AE_Standards_1309.pdf

S76Índice
RESULTADOS PREMILINARES DE UNA CONSTRUCCIÓN DE SENSORES ULTRASÓNICOS
Albalat A.M., Ruzzante J.E., Carballal C.A., Cavalchini J., Elorriaga M. y Antunez M.E.

1

INFLUENCIA DO SISTEMA DE CONTROLE DA CARGA EM ENSAIOS DE EMISSAO ACUSTICA: ESTUDOTEORICO EXPERIMENTAL
Almeida W.R., Rojo Tanzi B.N., Lacidogna G., De León D.M. y Iturrioz I.

7

REDES NEURONALES APLICADAS AL MONITOREO DE PROCESOS DE PERFORACIÓN EN PROBETAS DE
MATERIAL GEOLÓGICO
Basgall A., Ferrari, G., Gómez, M.

18

ESTUDIO DE PROPIEDADES MECÁNICAS EN TUBOS DE CFRP
Bonelli Toro A., Podetti G., Arcone D., Daverio N., Hazarabedian A. y Gómez M.P.

29

INTERFAZ PARA VISUALIZAR Y PROCESAR SEÑALES DE EMISIÓN ACÚSTICA
Bonelli Toro A.G., Iofrida M.J., y Gómez M.P.

37

ESTUDIO DE LA CONDICIÓN DE HERRAMIENTAS DE MECANIZADO DE MATERIALES COMPUESTOSUTILIZANDO EMISIÓN ACÚSTICA
Bonelli Toro A.G., González Fuster M., Fernández G., Antico F., Vogt P. y Gómez M.P.

46

PROPAGACIÓN DE ONDAS EN SÓLIDOS ANÍSOTRÓPICOS: PREDICCIÓN DE VELOCIDAD ENPOLICRISTALES
Cowes D.A., Mieza J.I.y Gómez M.P.

56

METODOLOGÍA PARA ESTIMAR LA INFLUENCIA DE EVENTOS ANÓMALOS EN LOS NIVELES DE RUIDO
DEL TRÁNSITO RODADO DE LA CIUDAD AUTÓNOMA DE BUENOS AIRES
Crapa M.N., Masson F., Mansilla J., Zanardi E. y Lombera E.

61

MONITORAMENTO DA DEGRADAÇÃO DE COMPÓSITO PRFV APLICANDO A TÉCNICA DE EMISSÃO
ACÚSTICA (EA) COMBINADA COM SENSORES DE REDE DE BRAGG EM FIBRA ÓTICA (FBG)
Da Silva D.S., Groth E.B., Lutckmeier F., Zuglian G., Jaques R., de Lima T.S. y Clarke T.G.

71

EMISIÓN ACÚSTICA APLICADA AL MONITOREO DE PROCESOS DE PERFORACIÓN EN PROBETAS DE
MATERIAL GEOLÓGICO
Ferrari G., Basgall A., Gómez M.

81ANÁLISIS DE INCERTIDUMBRE PRODUCIDA POR EL CAUDAL VEHICULAR MEDIANTE EL USO DELMÉTODO DE CÁLCULO NMPB-ROUTES-96
Galimberti J., Ruffa F. y Stasi A.

88ANÁLISIS MODAL BASADO EN MEDICIONES NO INVASIVAS CON UN ARREGLO DE MICRÓFONOSCOMERCIAL
Garcia J. y Boelke O.

101ANÁLISE NUMÉRICA DA EMISSÃO ACÚSTICA APLICADA AO MONITORAMENTO DE INTEGRIDADE
DE ESTRUTURAS
Groth E.B., Shumacher G., Rojo Tanzi B.N., Clarke T.G.R., Jacques R.C. y Iturrioz I.

109

ACOUSTIC EMISSION MONITORING OF 16” PIPE SEGMENTS DURING HYDROSTATIC TESTING
Jacques R.C., Lima T. S., Soares S.D., Groth E.B. y Clarke T.G.R.

120

ANALISIS DE VIBRACIONES EN UNA CAJA REDUCTORA DE UNA TURBINA
Klempnow. A., Piñeyro J.J., Bello A. y Elfi F.

132

MEDICIÓN DE RUIDO EN PEQUEÑAS TURBINAS EÓLICASLabriola C.L., Colón D.L., Lassig J. y Palesse C.

141

ANALISIS PARA DETERMINAR UNA CLASIFICACIÓN CUALITATIVA DEL ESTADO DE FONDOS DETANQUES A PARTIR DE ENSAYOS DE EMISION ACUSTICA Y SU RELACIÓN CON EL FENOMENO DE
LA CORROSIÓN ACTIVA
Montenegro A.N.

151

ESTUDIO DE LA TRANSFORMACIÓN MARTENSITICA EN UN ACERO AISI 321 MEDIANTE TÉCNICASMAGNÉTICAS
Neyra Astudillo M.R., Gómez M.P. y Pardal J.

158

PREDICCIÓN DE LA RESISTENCIA AL DESGASTE POR ABRASIÓN DE RECARGUES POR SOLDADURA A
TRAVÉS DE EMISIÓN ACÚSTICA
Peix D.A., Ávila E., Molina G., Ferrari G., Folgueiras J. y Gómez M.P.

175

ANÁLISIS, TRANSFORMACIÓN Y RESÍNTESIS DE SONIDO: UNA IMPLEMENTACIÓN DE LA TÉCNICA
ATS COMO BASE PARA POSIBLES INTERRELACIONES ENTRE SEÑALES SONORAS Y AUDIO DIGITAL
Perna G.L., Paupy P.E. y Huggenberger D.

185

ESTUDIO DE LA NORMA DE ENSAYO DE EMISIÓN ACÚSTICA PARA LA DETECCIÓN DE CORROSIÓN
EN TANQUES LLENOS DE LÍQUIDO EN ARGENTINA
Semorile M., Montenegro A.N. y Tellería G.

197

NORMAS TÉCNICAS DE EMISIÓN ACÚSTICA EN ARGENTINA Y MERCOSUR
Semorile M.

205

A REDUCED ORDER MODEL OF WAVE PROPAGATION AND VIBRATIONS IN RODS AND TUBES OF
UNIFORM CROSS SECTIONS
Suárez Antola R.

221

ESTUDIO E IMPLEMENTACIÓN DE CUADRO DE CLASIFICACIÓN CUANTITATIVO PARA ENSAYOS DEEMISIÓN ACÚSTICA EN FONDOS DE TANQUES EN ARGENTINA
Tellería Gonzalo, Tellería Juan Manuel y Castillo Canache Argenis

233

ESTUDIO DE LA PROPAGACION DE FISURAS MEDIANTE EMISION ACÚSTICA
Vatcoff L. y Santarelli E.

241

SISTEMA AUTÓNOMO PARA MONITOREO ACÚSTICO DE CETÁCEOS
Veca A.C., Valese N., Ruzzante J.E., Albalat A.M. y Marino A.

253

ENERGÍA Y ENTROPÍA WAVELET DE SHANNON EN SEÑALES DE EMISIÓN ACÚSTICA PARAEVALUACIÓN DE DAÑO EN ROCA ANDESITA
Zitto M.E., Sassano M.P., Muszkats J.P., Filipussi D.A. y Piotrkowski R.

262

LA EMISIÓN ACÚSTICA, SU HISTORIA EN LA ARGENTINA
López Pumarega M.I., Ruzzante J.E y Gómez M.P.

273