Journal of Acoustic Emission
Volume 31, 2013
Contents of Issue No. 1
31-001
Acoustic Emission Signal Propagation in Damaged Composite Structures,Markus G. R. Sause
31-019 In-Flight Fatigue Crack Growth Monitoring in a Cessna T-303 Crusader Vertical
Tail, Eric v. K. Hill and Christopher L. Rovik
31-036 Acoustic Emission Monitoring of Composite Blade of NM48/750 NEG-MICONWind Turbine, Dimitrios Papasalouros, Nikolaos Tsopelas, Athanasios Anastasopoulos,
Dimitrios Kourousis, Denja J. Lekou and Fragiskos Mouzakis

Announcement
We start a new mode of distribution for Journal of Acoustic Emission with Volume 31(2013). With this issue, this Journal becomes an open access publication. We can reduce
the time needed from submission to publication and make the articles reach readers
quicker.
A new international editorial board is being organized in cooperation with theInternational Institute of Innovative Acoustic Emission and when it is set, it will bereported in a later issue. Review processes will continue as before.
Kanji Ono, Editor-in-ChiefAcoustic Emission Signal Propagation in Damaged Composite Structures
Markus G. R. Sause
University of Augsburg, Institute for Physics, Experimental Physics II, D-86135 Augsburg
Abstract
Numerical studies were performed to investigate acoustic emission (AE) signal propagationin carbon fiber-reinforced plastic plates with internal damage. For the case of thin plates, Lambwaves are the dominating mode of propagation for ultrasonic waves. The interpretation of Lambwaves is one of the main challenges for valid source localization or identification procedures.
Naturally, the lateral size and the thickness of the plate have a significant impact on the propagation behavior of guided waves. While these geometric dimensions can be understood as boundary conditions, distortions of wave patterns caused by internal discontinuities are more difficult tounderstand. During loading of a composite specimen, a variety of damage types can occur, thatalter the properties of the composite. Although AE signals originate from the rapid microscopic
displacement within the material, the interaction of the excited acoustic waves is not limited tomicroscopic dimensions. Due to the wavelength of the Lamb waves in the millimeters range, thepropagating Lamb-wave modes can interact with discontinuities in these dimensions as well.
Typical macroscopic damage modes found in the millimeters range are inter-ply delamination,inter-fiber cracks or fiber breakage crossing one or multiple plies. Such damage is often encountered in carbon fiber-reinforced plastics as a consequence of impact damage, proof testing ofvessel structures or as manufacturing error. The current study presents results of finite element
calculations to investigate the influence of those discontinuities on the propagation behavior of
distinct Lamb-wave modes.
1. Introduction
Among the class of engineering materials, fiber-reinforced plastics show extraordinary highstrength-to-weight and stiffness-to-weight ratios. This makes them ideal materials for construction of aircraft, high-performance cars or sporting goods. The design of fiber-reinforced structures relies on the anisotropy of the elastic properties caused by the alignment of carbon fibers.
While this anisotropy is advantageous for constructional aspects, it causes major challenges fornon-destructive evaluation (NDE) of these structures. Such NDE monitoring of the structural
integrity is often done by acoustic emission (AE) analysis or active guided wave monitoring.
Both methods rely on the propagation of acoustic waves in flat, plate-like structures. Plate waves
are the dominant mode of wave propagation in those structures, and these waves are also known
as Lamb waves [Lamb, 1917]. The infinite number of Lamb wave modes, which can exist withina plate is of two types. One type shows symmetric and the other shows anti-symmetric motion
with respect to the mid-plane of the plate. However, the types of modes found most often in thinwalled fiber-reinforced structures are the fundamental symmetric mode (S0) and anti-symmetricmode (A0), often referred to as extensional and flexural mode. Lamb-wave propagation in undamaged carbon fiber-reinforced polymers (CFRP) has been investigated extensively before[Lowe, 1995, Castaings, 2004, Wang, 2007, Sause, 2010d]. Scattering of Lamb waves at internal
damage, like cracks or delamination, is the key principle for structural health monitoring (SHM)
of CFRP by guided wave testing [Raghavan, 2007]. The impact of such discontinuities on AE
analysis has been investigated less. Since Lamb-wave propagation is the carrier of information
on AE source activity in the material, distortion of information due to interaction of Lamb waves
with internal discontinuities is closely related to the question of reliability of the information.
J. Acoustic Emission, 31 (2013)1© 2013 Acoustic Emission GroupChanges of modal intensity or occurrence of alternative propagation paths due to scattering canreadily affect source localization accuracy and/or complicate source identification procedures.
This was recently demonstrated for the case of metallic obstacles, as encountered as fasteners incomposite structures [Sause, 2012d]. The purpose of the current study is the extension of theprevious investigation to internal discontinuities as typically faced for the case of damaged composite structures. Using finite element modeling (FEM), the interaction of Lamb waves with discontinuities is easy to visualize. It was recently demonstrated that this approach yields results
consistent with experiments [Sause, 2010d, Sause, 2012b, Sause, 2012c, Sause, 2012d].
2. Finite Element ModelingIn the following, a finite element modeling approach is applied using the software program“ComsolMultiphysics”. All descriptions only refer to the way of implementation within themodule “Structural Mechanics” of this software.
2a. Simulation methodology
The calculation of stress-strain relationships is based on the structural mechanics constitutive
equation. Based on the principle of virtual work the program solves the partial differential equations for equilibrium conditions, expressed in global or local stress and strain components for an
external stimulation.
For linear elastic media with elastic coefficients D , Hooks law is chosen as the constitutive
equation.
  (1)
σ = D⋅ε

In the general case for anisotropic media the elasticity tensor D is a 6 x 6 matrix with 12 independent components. Using the Voigt notation convention, the stress tensor can be written asvector σ with six independent components composed of normal stresses σ and shear stresses τ.
The strain tensor is written similarly as vector ε , which also has six independent components
consisting of normal components ε and shear strain components γ. In the case of isotropic media
the elasticity tensor is completely described by Young’s modulus E and the Poisson ratio ν.
The principle of virtual work states that the variation of W induced by forces Fi and virtual
displacements dui in an equilibrium state equals zero:δW = ∑ Fi ⋅ δui = 0(2)

iGenerally, the external applied virtual work equals the internal virtual work and in the case of adeformable body with volume V and surface S, results in a deformation state with new internal
stress and strain components.
t 
t 
t 
(3)
δ
u
⋅
F
dS
+
δ
u
S
∫
∫ ⋅ FV dV − ∫ δε ⋅ σ dV = 0
S

V
V

The external applied forces FS and FV act on the surface and volume of the body, respectively.
The constraint forces within the material are expressed by consistent internal stress σ and strainε components, with the superscript t indicating the transposed vectors.

2

To extend the principle of virtual work for dynamic systems, mass accelerations are introduced. This yields the formulation of the d’Alembert principle, which states that a state of dynamic equilibrium exists if the virtual work for arbitrary virtual displacements vanishes. Taking
this into account and introducing the material density ρ, equation (3) becomes:


t 
t 
t 
∂ 2u 
∫S δu ⋅ FS dS + V∫ δu ⋅ FV dV − V∫ δε ⋅ σ dV − V∫ ρ ∂t 2 ⋅ δu dV = 0(4)This defines the basic differential equation solved for every finite element. In order to model
structural mechanics problems, a suitable geometry and respective boundary conditions have tobe defined.
2b. Model description
The model setup used in this study is shown in Fig. 1. A rectangular plate of 400 mm × 400
mm size and 1 mm thickness is used as propagation medium. Elastic properties of unidirectional
CFRP plate are used as given in Table 1. Fiber direction of the unidirectional lamina is oriented
along the 0° axis as noted in Fig. 1. Two symmetry planes were chosen to reduce the volume
modeled to one quarter of the overall volume. The symmetry planes are the yz- and xz-planewith respect to the origin of the coordinate system.
CFRP (unidirectional 0°)
m
200

m

20
0m

90°

yz
-s y
mm

etr
y

0°

xz-

pla
ne

sym

points for signal detection
0°: (x,y,z) = (100,0,1) mm
45°: (x,y,z) = (71,71,1) mm
90°: (x,y,z) = (0,100,1) mm

m

try
me

e
plan

model source
1.0 mmdamaged area (e.g. crack)zyx

origin (x,y,z) = (0,0,0) mm

Fig. 1. 3-dimensional model setup used for simulation of Lamb-wave propagation.
As AE source model, a point dipole was chosen following [Gary, 1994]. As demonstrated in[Hamstad, 2002] such point dipoles can be used to excite particular fundamental Lamb-wave
modes in isotropic plates. In contrast to complex source models based on micromechanical considerations [Sause, 2010a, Sause, 2010b], point dipoles are computationally more efficient toinvestigate Lamb-wave propagation in larger structures. The position of the point dipole waschosen at (x, y, z) = (0.00, 0.00, 0.53) mm, slightly asymmetric with respect to the mid-plane ofthe plate, to excite a reasonable amount of S0 and A0 Lamb wave modes at the same time. The
length of the dipole (oriented in x-direction) was chosen to be 200 µm. A linear ramp function
with excitation time te = 1 µs and maximum force Fmax = 3 N was used.

3⎧ F ⋅ (t te ) t ≤ te
F (t ) = ⎨ max
t > te⎩ Fmax(5)

All signals are evaluated at 100-mm distance to the AE source in 0°, 45° and 90° propagation
direction as z-displacement on the top surface of the plate.
The region of internal damage starts at a distance of 50 mm from the source in the 0°direction. This configuration was chosen, since complementary studies with obstacles placed in
90° orientation to the fibers confirmed that the influence of the obstacles is largest in the 0° case.
Details of the various configurations are shown in Figs. 2-4. Each of the modeled geometries
refers to one prototype of internal damage.
Inter-fiber cracks (also called matrix cracks) weaken the link between neighboring fibers andcan thus affect the transmission of acoustic waves. In the chosen configuration, the growth direction of such cracks is along the fiber axis direction (0°). From the perspective of wave propagation, the worst case is a growth of such cracks through the complete thickness. This configuration is studied here as cut through the plate with width of 25 µm and various lengths as shown in
Fig. 2.

width: 25 µmzywidth: 25 µm

width: 25 µm

m
7.5

x

m
20

m
m
10

m

5m

m

m

50 mm distance to AE source

Fig. 2. 3-dimensional model geometry used for simulation of inter-fiber cracks (cut along fiber
axis direction).
As the second prototype, rupture of fibers can accumulate locally and can become a macroscopic crack. In the current configuration this is modeled as a 100-µm thick cut perpendicular to
the fiber axis direction as shown in Fig. 3. Since such crack growth typically initiates ultimate
failure of the load carrying structure, we investigated three depths of the cut to investigate theinfluence on wave propagation. The depth of 0.5 mm already refers to a significant amount ofdamage, reflecting a rupture of more than 800,000 fiber filaments. Also, for bending loads, theinitiation of these cracks is typically found on the surface of the structure. Therefore, the current
model, with damage starting on the surface, exhibits an asymmetric location of the crack withrespect to the mid-plane.
The third prototype is inter-ply delamination. This is an in-plane discontinuity that weakens
the local bending stiffness of the plate and is often encountered in fiber-reinforced structures as aconsequence of impact damage, excessive loading or even as residue of the manufacturing process. Since this can occur in various extents, we investigate three different sizes to reflect reasonable variability of the size of the delaminated area. For all configurations a thickness of thedelamination of 50 µm is chosen, centered within the plate.
4

20

m

20

m

m

20

m

m

m

z
y

x

0.250 mm0.500 mm
50 mm distance to AE source

0.125 mmFig. 3. 3-dimensional model geometry used for simulation of broken fibers (cut perpendicular tofiber axis direction).
z
y

20

m

x

m

5

mm

20

20
m

height:
50 µm

m

m

m

mm
10

height:
50 µm

m
20

m

height:50 µm

50 mm distance to AE source

Fig. 4. 3-dimensional model geometry used for simulation of inter-ply delamination (cut parallel
to fiber axis direction).
Table 1. List of elastic properties used for finite element modeling.
T800/913 (unidirectional)

Density [kg/m³]
1550

Elasticity Constants [GPa]C11=154.0
C12= C13=3.7
C22= C33=9.5
C23=5.2
C44=2.5
C66= C55=4.2

For accurate resolution of calculated signals, a maximum element size of 1.0 mm edge length
was chosen for tetrahedral mesh elements with quadratic order of the geometry shape functions.
To resolve geometric details in narrow regions the mesh was locally refined down to a minimumedge length of 5 µm. The temporal resolution was chosen to be 100 ns and results were calculated for the first 100 µs after signal excitation. Convergence of simulation results with respect tomesh resolution and temporal resolution was previously checked by subsequent refinement. To
this end mesh resolution was increased to 0.5 mm edge length and temporal resolution was increased to 5 ns. Results of these calculations were found to have a coherence level ≥ 0.998 to thecurrent settings within the frequency range between 1 kHz and 2 MHz.
4. Results
In the following, the influence of the modeled damage on the signal propagation is investigated. Figure 5 shows the Choi-Williams distribution (CWD) of the calculation result for signal
5

propagation along the 0° axis of the unidirectional CFRP plate [Choi, 1989]. The CWD in Fig. 5a uses a different color, frequency and time range than Fig. 5-b, which is used to emphasize theS0-mode at the initial part of the signal. To identify particular Lamb wave modes, the dispersion
curves of the fundamental modes were calculated by the software package provided by [Zeyede,2010]. As indicated by the superimposed dispersion curves for the S0-mode at 100 mm and
300 mm source distance, the S0-mode is reflected at the edge of the plate in x-direction and is
detected more than once. The CWD result of the A0-mode shown in Fig. 3-b agrees well with thecalculated result of the A0 dispersion curve for 100 mm distance. The small dips in the curve
seen at 52 µs and 62 µs are caused by multiple reflections of the S0-mode, which has been reflected at all edges of the plate at this time. The calculation results for the pure CFRP plate in
Fig. 5 will act as a reference case to evaluate the influence of any damage modeled within the
propagation path. A comparison of the calculated wave-fields will be made in section 5.
(a)

Displacement [fm]Frequency [kHz]2000
1800 CFRP Reference
1600
1400
1200
1000
800
600
400
200
0

S0-mode (100mm)

0.000

S0-mode (300mm)

0.004000
0.008000
0.01200
0.01600
0.02000

edge reflection

10
5
0
-5
-10
0.00

0.01

0.02

0.03

0.04

0.05

Time [ms](b)Displacement [nm]Frequency [kHz]1000

CFRP Reference

800

0.000
4.000

A0-mode (100mm)
S0-mode (100mm)

600

8.000
12.00

400

16.00

200

20.00

0
0.10
0.05
0.00

-0.05
-0.10
0.000

0.025

0.050

0.075

0.100

Time [ms]Fig. 5. Simulation results of signal propagation at 100 mm distance along 0° direction in unidirectional CFRP. Truncated time scale shows S0-mode (a) and full time scale shows A0-mode (b).
6

4a. Inter-fiber cracks
Inter-fiber cracks are one of the basic damage types found in fiber-reinforced composites.
These are cracks with propagation direction parallel to the fiber axis direction. Since the strengthof the direction perpendicular to the fiber axis is the weak link between the fibers, this type ofdamage is observed frequently in composite structures. In the current configuration, the interfiber crack is modeled as crack-through process, i.e. the crack reaches from the top to the bottomof the plate. The length of the inter-fiber crack is varied between 5 mm and 20 mm to cover abroad range of macroscopic crack sizes.
As seen in Fig. 6 for the S0-mode detected in damaged structures, there are only negligible
differences compared to the reference case. In contrast, there is a detectable influence of thepresence of the inter-fiber cracks on the initial part of the A0-mode. For the various lengths of
5 mm, 10 mm and 20 mm of the inter-fiber crack no significant differences are observed relative
to each other besides a small signature at 63 µs.
0.35

Reference
5mm
10mm
20mm

0.30

Displacement [nm]

0.25
0.20
0.15
0.10
0.05
0.00
-0.05
0

20

40

60

80

100

Time [µs]Fig. 6. Simulation results of signal after propagation of 100 mm along 0° direction in unidirectional CFRP with inter-fiber cracks of various lengths.
4b. Broken fibers
Among the damage types found in fiber-reinforced composites, a rupture of the load bearing
constituents is the most severe damage. In the current model, the presence of such discontinuous
fiber filaments is taken into account by an air gap with extension perpendicular to the fiber axis
direction (see Fig. 3). To take into account the various degrees of damage for this type of failure,
the depth of the air gap in the plate thickness direction is varied between 0.125 mm and
0.500 mm.
As seen in Fig. 7, the influence of the discontinuity on the first arrival of the S0-mode(t < 20 µs) is negligible for all cases investigated. Dependent on the through-thickness dimensionof the modeled discontinuity, a secondary peak arises around 35 µs. This is caused by modal
conversion of the incident primary S0-mode at the discontinuity into a secondary A0-mode and7into a secondary S0-mode (see Fig. 9 and Fig. 10, also [Sause, 2012d]). The later parts of thesignal (t > 40 µs) are also affected by this interaction, which causes a superposition of the incident A0-mode with the multiple reflections of the secondary modes. It is worth noting that themodel is energy conservative. Still, the calculated amplitude of the signals is distinctly different.
This is solely caused by the presence of the broken fibers, which cause a preferential scatteringof the displacement field, which transfers energy from the in-plane displacement to the out-ofplane displacement.
0.50

Reference
0.125mm
0.250mm0.500mm0.45

Displacement [nm]

0.40
0.35
0.30
0.25
0.20
0.15
0.10
0.05
0.00
-0.05
0

20

40

60

80

100

Time [µs]Fig. 7. Simulation results of signal after propagation of 100 mm along 0° direction in unidirectional CFRP with broken fibers modeled with various dimensions.
4c Inter-ply delamination
Inter-ply delamination is one of the most common types of damage found in fiber-reinforced
composites, since it is often caused by local impact or as residue of a manufacturing error. During mechanical testing of fiber-reinforced structures, delamination can evolve step by step andcan affect the elastic properties along the signal propagation path. Thus, the influence of delamination on the detected AE signals is of considerable interest. To resemble the variety of dimensions of inter-ply delamination, the size of the delaminated area in the in-plane direction wasvaried between 5 mm and 20 mm (see Fig. 4).
Figure 8 shows the calculated signals for all three inter-ply delamination sizes investigated
and the reference signal for comparison. For the S0-mode there are only negligible differences
observed to the reference case. Since propagation of the S0-mode is dominated by the in-plane
stiffness, this is explained by considering a multi-layer specimen composed of 950 µm CFRP
and 50 µm air. The in-plane stiffness of such a plate is as close as 95 % of the stiffness of a pure
1000-µm CFRP plate. A larger influence is observed for the propagation behavior of the A0mode. The shape and intensity of the A0-modes differ from the reference signal due to thechange of local bending stiffness as introduced by the inter-ply delamination. With increasing
size of the inter-ply delamination, the deviation compared to the reference case increases as well.

8

0.40

Reference
5mm
10mm
20mm

0.35

Displacement [nm]

0.30
0.25
0.20
0.15
0.10
0.05
0.00
-0.05
0

20

40

60

80

100

Time [µs]Fig. 8. Simulation results of signal after propagation of 100 mm along 0° direction in unidirectional CFRP with inter-ply delamination of various dimensions.
5. Discussion
In the present study, the interaction of Lamb-wave modes with different types of internal
damage in unidirectional CFRP plates was investigated by means of finite element modeling. For
evaluation of the signals a unidirectional CFRP plate was chosen as propagation medium, since
this exhibits a maximal elastic anisotropy. The wave fields obtained for the reference case without obstacles, a modeled 20-mm long inter-fiber crack, a 0.5-mm deep broken fibers and a 10mm long inter-ply delamination are shown in Figs. 9 and 10 for two distinct times t = 20 µs andt = 40 µs after signal excitation.
The interaction of the different types of damage with the incident S0-mode is visualized bestin the wave-fields in Fig. 9. For the inter-fiber cracks and inter-ply delamination, only weak interaction with the S0-mode is found. In contrast, for the broken fibers, the incident S0-mode causes excitation of a strong secondary A0-mode with a radiation pattern around the crack, which acts
as an epicenter. This is caused by a modal conversion of the primary S0-mode into a secondary
A0-mode, since the primary wave front of the A0-mode did not arrive at the position of the modeled crack for t < 20 µs. Subsequent to the emission of the secondary A0-mode, an excitation of asecondary S0-mode is observed as well.
In Fig. 10, the wave field for t = 40 µs shows the interaction of the A0-mode with the different types of modeled damage. Again only weak interaction is found between the A0-mode andthe modeled inter-fiber cracks or the modeled inter-ply delamination as shown in Figs. 10-b and10-c. For the broken fibers, similar as for the incident S0-mode, the A0-mode is significantly affected by the presence of the discontinuity.

9a)Reference (pure CFRP)b) Inter-Fiber cracks

t = 20µs, z-displacement [nm]A0-modec)t = 20µs, z-displacement [nm]primary S0-mode

S0-modenegligible interaction

Delaminationd) Broken Fibers

t = 20µs, z-displacement [nm]t = 20µs, z-displacement [nm]primary S0-mode

transmitted S0-mode

weak interaction

50mm

secondary A0-modes

Fig. 9. Comparison of simulated wave fields of z-displacement for reference (a), 10-mm interfiber crack (b), 10-mm delamination (c) and 0.5-mm broken fibers (d) at t = 20 µs after signal
excitation.

a)Reference (pure CFRP)b) Inter-Fiber cracks

t = 40µs, z-displacement [nm]t = 40µs, z-displacement [nm]S0-modeA0-modesmall deviation
to reference

reflected S0-modec)Delaminationd) Broken Fibers

t = 40µs, z-displacement [nm]t = 40µs, z-displacement [nm]weak interaction with A0-mode

50mm

disturbance of wave field

Fig. 10. Comparison of simulated wave fields z-displacement for reference (a), 10 mm inter-fiber
crack (b), 10 mm delamination (c) and 0.5 mm broken fibers (d) at t = 40 µs after signal excitation.
10In order to investigate the influence of the simulated damage on AE analysis, the calculated
signals are processed in a similar way as typical for experimental signals. Thus, AE features are
extracted from the signals and arrival times of the S0-mode and the A0-mode are calculated for a
distance of 100 mm in all propagation directions of 0°, 45° and 90°. Table 2 lists all AE parameters and arrival times of the cases studied. For calculation of signal energies and signal amplitude
an artificial sensor sensitivity of 4.5x109 V/m was assumed.
Table 2. Definition of AE features.
AE feature
Amplitude [mV]Definition
𝑈!"#

Absolute energy [J]𝐸=

Peak frequency [kHz]𝑓!"#$Frequency centroid [kHz]𝑓!"#$%&'( =Weighted Peak frequency [kHz]𝑓!"#$% =Partial Power 1 [%]!! !
!! ! ! !"!"##$%& !
! ! !"!"#$

Partial Power 2 [%]Partial Power 3 [%]Partial Power 4 [%]Partial Power 5 [%]Partial Power 6 [%]Partial Power =!!"𝑈 𝑡 ! 10𝑘 𝑑𝑡!

𝑓 ∙ 𝑈(𝑓)𝑑𝑓
𝑈 𝑓 𝑑𝑓𝑓!"#$ ∙ 𝑓!"#$%&'(

PP1: f1 = 0 kHz; f2 = 150 kHz
PP2: f1 = 150 kHz; f2 = 300 kHz
PP3: f1 = 300 kHz; f2 = 450 kHz
PP4: f1 = 450 kHz; f2 = 600 kHz
PP5: f1 = 600 kHz; f2 = 900 kHz
PP6: f1 = 900 kHz; f2 = 1200 kHz!! !
! ! !"!!
!"##$%& !
! ! !"!"#$The definition of features is based on the basic properties derived from the signal in time
domain 𝑈 𝑡 and in frequency domain 𝑈 𝑓 as shown in Figs. 11-a and 11-b, respectively. 𝑈!"#is the maximum signal amplitude, as seen in Fig. 11-a, while the absolute energy is derived fromthe integral of U t ! within the investigated time of 100 µs. f!"#$ defines the frequency positionof maximum FFT-magnitude as seen in Fig. 11-b, while f!"#$%&'( is defined in Table 2. Definition of the different Partial Power features is given in Table 2 as well.
Arrival times of S0-mode and A0-mode are picked from the CWDs following the method introduced by Hamstad et al. [Hamstad, 2008]. Thus, arrival of the mode is picked as the time of
maximum coefficient intensity of the respective mode at a constant frequency (500 kHz in thecurrent case).
In the following, the influence of the internal damage types on the extracted signal features isdiscussed using the initial arrival time (S0-mode arrival), the signal amplitude and the weighted
peak frequency representative for the other features of Table 3.

11

(a)
t0
Umax

0.010

Amplitude [mV]

0.005

threshold level
0.000-0.005-0.010

tAE
0.00

0.02

0.04

0.06

0.08

0.10

Time [ms]Fig. 11. Extraction of features from acoustic emission signals in time (a) and frequency domain(b).
12Table 3. Values of AE features extracted from the calculated signals in various propagation directions.
Arrival time S0 [ µs]Arrival time A0 [µs]Amplitude [mV]

Absolute energy [nJ]Peak frequency[kHz]

Weighted Peak frequency [kHz]Frequency centroid[kHz]

Partial Power 1[%]

Partial Power 2 [%]Partial Power 3 [%]Partial Power 4 [%]Partial Power 5 [%]Partial Power 6 [%]Configuration

0°

1

9.2

81.6 342.5

23.4

39.1

124.5 396.5

59.5

13.3

13.7

7.0

10.9

1.6

45°

11 23.8
21 33.5

90.2

30.5

23.6 127.0

232.4 425.3

38.6

21.2

17.8

9.2

23.4

2.3

44.6

24.9

23.6 293.0

504.9 870.0

16.0

18.4

17.9

8.9

2.2

15.5

2

91.7 388.2

55.6

29.3

72.3 178.4

77.6

12.8

4.3

1.7

11.8

1.5

90.2

32.3

55.8

87.9

229.9 601.4

30.6

15.3

23.1 17.0

15.6

2.2

5mm-90°

9.3
12 24.8
22 35.6

37.0

26.5

55.8 117.2

234.2 468.2

20.9

23.6

24.1 14.3

2.3

1.5

10mm-0°3

91.8 498.3

58.0

29.3

73.7 185.5

68.7

15.4

8.7

3.8

8.0

1.1

10mm-45°

90.2

31.4

58.2

87.9

223.7 569.3

40.6

21.6

18.0

9.4

13.6

2.4

10mm-90°

13 24.7
23 31.9

40.8

24.2

58.3 263.7

353.6 474.2

22.0

24.4

25.1 13.7

2.1

1.2

20mm-0°4

91.5 556.4

64.1

29.3

71.7 175.7

66.3

17.0

9.6

4.2

8.5

0.9

20mm-45°

14 23.1
24 29.3

90.2

32.0

64.4

87.9

225.6 579.2

41.5

20.8

17.6

9.2

18.5

2.5

40.6

30.4

64.4 263.7

338.7 435.0

19.9

23.0

24.4 13.3

3.6

0.9

5

90.0 369.7

44.1

29.3

70.2 168.1

77.9

7.7

5.9

3.6

11.9

1.3

90.2

29.8

44.3

87.9

229.1 597.3

42.2

16.9

18.1

8.4

19.0

2.5

0.125mm-90°

9.4
15 25.6
25 31.8

37.0

30.0

44.3 439.5

438.8 438.2

16.7

20.6

27.5 15.4

4.9

0.8

0.25mm-0°

690.0 533.7

54.0

19.5

64.8 214.9

63.0

13.3

11.1

6.8

16.5

0.9

0.25mm-45°

92.5

37.5

54.2

87.9

227.2 587.6

36.6

15.0

18.1 11.7

19.0

2.2

0.25mm-90°

16 25.3
26 31.7

36.9

29.7

54.3 439.5

423.6 408.3

17.0

20.0

25.0 17.0

6.0

1.0

0.5mm-0°7

90.8 993.1 122.0

9.8

60.3 372.9

50.0

19.0

15.0

9.0

10.6

1.0

0.5mm-45°

17 23.7
27 30.9

92.5

45.3 122.0

87.9

220.5 553.1

37.3

15.1

22.9 12.3

21.0

1.9

41.0

32.8 122.0 439.5

451.6 464.0

15.0

18.0

24.0 21.0

3.0

1.0

8

92.3 358.0

39.5

29.3

85.7 250.8

81.0

9.0

3.0

2.0

10.0

2.0

92.5

34.1

39.7

97.7

249.3 636.7

40.9

19.0

17.1 10.2

27.0

2.9

36.8

31.4

39.8 293.0

368.1 462.6

14.0

18.0

22.0 17.0

4.0

1.0

35.4

39.1

128.3 421.6

76.0

11.0

4.0

3.0

19.9

3.0

Reference

90°
Inter-fiber cracks
5mm-0°
5mm-45°20mm-90°

9.2

8.9

Fiber breaks
0.125mm-0°0.125mm-45°

0.5mm-90°

9.4

9.4

Delamination
5mm-0°
5mm-90°9.4
18 24.6
28 29.710mm-0°910mm-45°

39.2

35.1

35.7

97.7

247.3 626.4

36.0

20.0

12.7

9.0

29.0

2.3

10mm-90°

19 24.8
29 32.0

68.6

24.6

35.8 293.0

384.0 503.3

16.0

19.0

20.0 15.0

4.0

1.4

20mm-0°1042.0 523.6

59.0

39.1

146.8 552.0

62.0

18.0

9.0

4.0

7.5

3.0

20mm-45°

20 24.2
30 29.3

90.2

40.7

59.4

87.9

226.2 582.2

42.6

22.5

18.0

6.8

19.0

2.6

71.0

31.1

59.5 175.8

281.1 449.5

21.0

23.0

21.0 14.0

10.9

2.0

5mm-45°20mm-90°

9.4 102.0 322.1

9.4

13

5a. Influence on arrival time
In order to obtain valid source locations, a proper determination of the initial arrival time is a
key requirement for most of the currently used localization algorithms. The significant influenceof the various internal damage types on the propagation behavior of the signals was alreadydemonstrated in section 4. In Fig. 12, the extracted initial arrival times are shown for all damage
configurations and the reference case in 0°, 45° and 90° propagation direction. The largest influence of the internal damage types is found for the 90° propagation direction, which demonstrates, that the complete wave-field is affected by the presence of the damage and not solely thepart of the wave propagating through the obstacle (0° direction).
Based on the maximum deviation of 4.2 µs to the reference case of the calculated arrival timeof the S0-mode in 90° propagation direction, a corresponding error of localization in the range ofseveral centimeters can be expected. The estimation is based on the calculated group velocity of
the S0-mode in 90° propagation direction.
A significantly larger influence was found for the arrival time of the A0 mode. Due to modal
conversion occurring at the internal damage, the arrival of the first detectable A0-mode is 48.6 µs
ahead compared to the reference case in one configuration. However, this is not the arrival of the
primary A0 mode as discussed before. For the majority of the cases studied, the arrival times of
the A0 mode are within 10 µs difference to the reference case. Consequently, for localization
methods using arrival times of both fundamental Lamb wave modes, the observed difference inarrival time of the A0 mode might have a large impact as well.
40

90°

35

S0 arrival time [µs]

30

45°

25
20
15
10
5

Reference caseInter-Fiber cracks
Broken Fibers
Inter-ply Delamination

0°

0

5

10

15

configuration

20

25

30

Fig. 12. Calculated arrival time of S0-mode for the various configurations of internal damage incomparison to the reference case for 0°, 45° and 90° propagation direction.

14

5b. Influence on signal features
For source identification procedures one approach is the use of parameter-based patternrecognition [Sause, 2010c, Sause, 2010a, Sause, 2012a]. Since cracks and delamination are likely
to evolve between AE source positions and the position of detection during specimen loading,their influence on the transmitted Lamb waves has to be understood thoroughly.

amplitude [V]From the list of calculated features given in Table 2, some were found to deviate more than
100 % from the reference case. One example is the signal amplitude values shown in Fig. 13 foreach configuration, evaluated in 0°, 45° and 90° propagation directions. Compared to the reference case (red), the signals passing through the damaged region experience large changes of theirsignal amplitude. This is due to the modal conversion at the damage position, and the scattering
introduced by some of the damage types. These effects are not taken into account by the conventional definitions for calculation of signal amplitude or AE signal energies as given in Table 2.
Except for configuration 7, the overall deviation of amplitude values was found to be within± 0.25 V (i.e. ± 5 dBAE) to the reference case. Compared to the ± 3 dBAE recommendation of
ASTM E 2191 for sensor replacement this amplitude range is well within typically encountereduncertainties of measurement setups used.
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0.00

Reference caseInter-Fiber cracks
Broken Fibers
Inter-ply Delamination

0

5

10

15

configuration

20

25

30

Fig. 13. Calculated signal amplitude of signals for the various configurations of internal damage
in comparison to the reference case for 0°, 45° and 90° propagation direction.
The calculated weighted Peak-Frequency for the signals of different damage configurations
and the reference case are shown in Fig. 14 for 0°, 45° and 90° propagation directions. Dependent on the propagation angle, different values for the weighted peak frequency are observed. Thisis caused by the asymmetric propagation of Lamb-wave modes in the unidirectional plate modeled herein. Similar to the signal amplitude, deviations compared to the reference case are up to
100 %. Again, features evaluated in all propagation directions are affected by the presence of themodeled damage types. The range of feature values seems unacceptably high in order to perform
15

valid source identification based on frequency features, since the modeled source type is identical for all cases studied.
Reference caseInter-Fiber cracks
Broken Fibers
Inter-ply Delamination

Weigted Peak Frequency [kHz]

500

90°

400

300

45°
200

0°
100

0

0

5

10

15

configuration

20

25

30

Fig. 14. Calculated weighted peak frequency of signals for the various configurations of internal
damage in comparison to the reference case for 0°, 45° and 90° propagation direction.
However, for pattern recognition methods, there is a typical experimentally encountered distribution range for an individual feature. This is demonstrated in Fig. 15. Here, the experimentally detected signal features of a double cantilever beam experiment including their classification
into matrix cracking, interfacial failure and fiber breakage are shown. Details of the experimental
setup are found in [Sause, 2010d, Sause, 2012b], while references [Sause, 2010c, Sause, 2012a]
focus on the underlying methodology. The black data points are the resulting feature values of
the current simulation work with black circles marking the signals propagation direction. For thecurrent evaluation along one propagation direction, the feature range observed is quite comparable to the typical extent of one cluster observed for a particular failure mechanism (i.e. matrix
cracking, interfacial failure or fiber breakage). Thus, the presence of a failure type, as modeledherein, is expected to increase the dimensions of the cluster belonging to a particular failure type.
6. Conclusions
Within this study the influence of internal damage in fiber-reinforced composites on signal
propagation of Lamb waves was investigated. For the present study a unidirectional fiberreinforced plate was studied, including embedded models for inter-fiber cracks, broken fibers
and inter-ply delamination located in the 0° direction of the plate.
Overall, a significant influence on the signals from all damage types was observed that iswell detectable with piezoelectric sensors, and thus a feasible approach for SHM utilizing guided
wave monitoring.

16

100

Cluster 0 (Matrix cracking)
Cluster 1 (Interface failure)
Cluster 2 (Fiber breakage)

Partial Power 3 [%]

80

60

40

90°
45°

20

0

0°

0

100

200

300

400

500

600

700

800

Weighted Peak-Frequency [kHz]

900

1000

Fig. 15. Diagram of weighted peak frequency vs. partial power 3 of experimental measurementof double cantilever beam specimen [Sause, 2010d] compared to positions of features extracted
from simulated signals for 0°, 45° and 90° propagation direction (all configurations).
For AE analysis, the largest impact is expected for source localization routines, since the internal damage types can act as virtual AE source caused by modal conversion effects. This significantly alters the emitted wave field of the original source and may even affect the initial arrival time of the different wave modes, causing substantial errors during source localization.
Also, energetic features and frequency features extracted from the Lamb-wave signals are influenced by the presence of internal damage. In some configurations, the influence on a particular feature value was found to be very significant. However, the overall range of values of theextracted features is comparable to ranges where pattern recognition approaches still can lead tovalid classifications of source mechanisms. In contrast, the evaluation of signals with different
observation angles relative to the source positions might cause a larger impact with respect to theaccuracy of pattern recognition methods.
Acknowledgment
I would like to thank Marvin A. Hamstad for the possibility to discuss AE sensing technologies and signal propagation in fiber-reinforced polymers.
Literature[Castaings, 2004][Choi, 1989]

M. Castaings, C. Bacon, B. Hosten and M. V. Predoi (2004), J.
Acoust. Soc. Am., 115:3, 1125-1133.
H. Choi, W. Williams, IEEE Transactions on Acoustics, Speech andSignal Processing, 37:6, 862-872.
17

[Gary, 1994][Hamstad, 2002][Hamstad, 2008][Lamb, 1917][Lowe, 1995][Raghavan, 2007][Sause, 2010a][Sause, 2010b][Sause, 2010c][Sause, 2010d][Sause, 2012a][Sause, 2012b][Sause, 2012c][Sause, 2012d][Wang, 2007][Zeyede, 2010]

J. Gary and M. A. Hamstad (1994), J. Acoustic Emission, 12:3-4,
157-170.
M.A. Hamstad, A. O’Gallagher, J. Gary, J. Acoustic Emission, 12:34, 157-170.
M.A. Hamstad, J. Acoustic Emission, 26, 40-59.
H Lamb, ‘On Waves in an Elastic Plate’, Proceedings of the Royal
Society of London Series A Containing Papers of a Mathematicaland Physical Character, Vol 93, pp 114-128, 1917.
M. Lowe, IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control, 42:4, 525-542.
A. Raghavan, C. Cesnik,The Shock and Vibration Digest, 39, 91114.
M. G. R. Sause and S. Horn (2010), J. Nondest. Eval., 29:2, 123-142.
M. G. R. Sause and S. Horn (2010), J. Acoustic Emission, 28, 142154.
M. G. R. Sause (2010), Identification of failure mechanisms in hybrid materials utilizing pattern recognition techniques applied toacoustic emission analysis, PhD-thesis, mbv-Verlag, Berlin.
M. G. R. Sause and S. Horn, Influence of Specimen Geometry on
Acoustic Emission Signals in Fiber Reinforced Composites: FEMSimulations and Experiments, 29th European Conference on Acoustic
Emission Testing, Vienna 09/2010
M.G.R. Sause, A. Gribov, A.R. Unwin, S. Horn, Pat. Rec. Letters,
33:1, 17-23.
M.G.R. Sause, T. Müller, A. Horoschenkoff, S. Horn, Comp. Sci.
Technol., 72, 167-174.
M.G.R. Sause, M.A. Hamstad, S. Horn, Sens. Act. A., 184, 64-71.
M. G. R. Sause, S. Horn, Influence of Internal Discontinuities on
Ultrasonic Signal Propagation in Carbon Fiber Reinforced Plastics,
30thEuropean Conference on Acoustic emission Testing, Granada
09/2012
L. Wang, F. Yuan, Comp. Sci. Technol., 67, 1370-1384.
R. Zeyede,Notes on orthotropic Lamb waves, Technion - Israel Institute of Technology, http://mercurial.intuxication.org/hg/elasticsim/

18

In-Flight Fatigue Crack Growth Monitoring in a Cessna T-303 Crusader
Vertical Tail
1

Eric v. K. Hill1 and Christopher L. Rovik2
Aura Vector Consulting, 3041 Turnbull Bay Road, New Smyrna Beach, FL 32168
2
Toyota Technical Center, 8777 Platt Road, Saline, MI 48176

Abstract
This research involved the in-flight monitoring of fatigue crack growth in the vertical tail of a
Cessna T-303 Crusader twin-engine aircraft. A notched 7075-T6 aluminum aircraft channel
beam support structure was cyclically tested in the laboratory. Acoustic emission (AE) data
were taken during these fatigue tests, which were subsequently sorted into three failure mechanisms: fatigue cracking, plastic deformation, and rubbing noise. These data were then used totrain a Kohonen self-organizing map (SOM) neural network. At this point, a similar channel
beam support structure was installed as a redundant structural member between the ribs in thevertical tail of the T-303 aircraft. AE data were subsequently gathered from initial taxiing andtakeoff to the final approach and landing. The AE data recorded during the in-flight tests werethen classified using the laboratory trained SOM neural network into the three above mentioned
mechanisms. From this it was determined that plastic deformation occurred throughout all regions of flight but was most prevalent during taxi operations, fatigue crack growth activity occurred mostly during flight operations -- particularly during roll and Dutch roll maneuvers -while the mechanical rubbing noise occurred mainly during flight with very little occurring during taxi. The success of the SOM classification of failure mechanisms indicated that the prototype in-flight structural health monitoring system for aging aircraft was highly successful at capturing fatigue crack growth data. It is envisioned that the application of such structural healthmonitoring systems in aging aircraft could warn of impending failure and allow for replacementof parts when needed rather than at conservatively calculated intervals. As such, continuation ofthis research should eventually help to minimize maintenance costs and extend the service livesof aging aircraft.
Keywords: Aging aircraft, in-flight fatigue crack monitoring, Kohonen self-organizing map,neural network, structural health monitoring
Introduction
Fatigue Cracking in Aircraft
Aircraft today typically are expected to last longer than automobiles. This is due to manyfactors, including the cost of the aircraft, government regulations, and the dramatic consequencesof failure. There are many problems that arise from the fact that aircraft are expected to last solong. Perhaps the major source of problems, which is the subject of this research, is the presence
and growth of fatigue cracks. The ability to repair damage from fatigue cracks has not been aproblem, but the detection and monitoring of fatigue crack growth has proven to be a real challenge.
Fatigue cracking is the brittle fracture that results from cyclic loading below the yieldstrength of a normally ductile metal. The highly concentrated stresses at the crack tip result in
the formation of a heart-shaped plastically deformed zone ahead of the crack. This plastic zone
strain hardens with cyclic loading and fractures when the ductility of the metal has been exhaustJ. Acoustic Emission, 31 (2013)19© 2013 Acoustic Emission Grouped, thereby extending the crack. This fatigue crack growth process repeats itself over and overagain until final failure of the part.
Aircraft experience different types of fatigue loadings. Takeoffs and landings are very fundamental types of cyclic loadings on aircraft. Cabin pressurization is also a type of cyclic loading, since the fuselage of an aircraft is a large pressure vessel that undergoes a breathing process
as the plane pressurizes and depressurizes to accommodate passengers at varying altitudes. Vibration due to atmospheric turbulence and engines is also a major cause of fatigue cracking inaircraft. Thus, all aircraft develop fatigue cracks over time.
Detecting fatigue cracks in aircraft structures is important because, if left unchecked, thecracks will eventually reach critical length, at which point they progress to catastrophic failure
within a relatively short period of time. As a structure with a crack is cycled, the crack will growuntil it is stopped -- for instance by grain boundaries -- but it will then typically change directions within a few cycles and continue to progress to failure. The affected parts must be replaced
before this happens. Currently this is accomplished at conservatively calculated intervals basedon linear elastic fracture mechanics. However, it would be beneficial to develop a system tomonitor the growth of fatigue cracks so that replacements are installed only when needed, rather
than at calculated intervals that are of necessity, highly conservative.
Detecting Fatigue Cracks
There are several nondestructive testing methods commonly used to detect fatigue cracks inaircraft. Two of the most common are eddy current testing and radiographic testing. The main
disadvantages to eddy current and radiographic testing are that they are both time consuming andtherefore very expensive. But more than that, these two techniques oftentimes require disassembly of the structure in question to obtain access for the inspection, and as such, crack growthmonitoring is at best intermittent rather than continuous. The ability to detect a growing fatigue
crack and identify its location is fundamental to reducing the maintenance costs associated withaircraft ownership and at the same time improving aircraft safety.
Acoustic emission (AE) nondestructive testing has been employed previously for continuous
in-flight monitoring of aircraft fatigue crack signals [1-5]. In order to detect fatigue crack
growth in an in-flight environment, it is necessary to determine the characteristics of the cracking
signal. The problem arises from the fact that amongst the crack signals, there also exist signals
due to plastic deformation and mechanical noise. Neural networks have been employed herein to
separate these signals and classify them as to the appropriate mechanism.
The test bed used for the in-flight fatigue crack growth monitoring was the vertical tail of a
Cessna T-303 Crusader aircraft shown in Fig. 1. It is interesting to note here that the crack
growth data acquisition for this aircraft was prematurely terminated because of the unexpecteddiscovery of fatigue cracks in its wing ribs. This resulted in the decision to sell the aircraft rather
than repair it; consequently, the planned data acquisition from the controlled fatigue cracking in
the vertical tail was cut short. Ironically, such an acoustic crack detection system as utilized inthis research, when fully developed, could have monitored those wing rib fatigue cracks in flightto ensure safety while the airplane was being returned for repairs.

20Fig. 1. Cessna T-303 Crusader test bed aircraft.
The experimental research associated with this study consisted of two segments. The first
segment dealt with the testing of a fatigue crack growth specimen in a controlled laboratory environment. The second segment of the research involved the testing of a similar specimen installedin the vertical tail of the Cessna T-303 Crusader test bed aircraft. The in-flight specimen was aredundant structure, as it was not installed to replace any structure of the aircraft but simply toprovide a structure that could undergo fatigue cracking without compromising the structural integrity of the aircraft. Data were taken during various flight regimes, including taxi, takeoff,
steady level flight, rolls, and Dutch rolls. The purpose of the two roll maneuvers was to impose
significant bending loads on the vertical tail which would hopefully induce fatigue crack growthin the notched portion of the redundant specimen.
Acoustic Emission
Acoustic emission nondestructive testing was the tool used in this research to monitor fatigue
cracking. Acoustic emissions are typically referred to as sound waves, but more appropriately,
they are the stress waves that propagate throughout a medium as a result of a sudden release ofenergy [6]. The purpose of this study was to identify and monitor the growth of fatigue cracks;
therefore, the waveforms associated with a fatigue cracking were of particular interest here.
The research presented herein was based on identifying and monitoring fatigue crack growthin the redundant internal structure mounted between the ribs in the vertical tail. A concurrent
research project involved AE monitoring of fatigue cracking in the engine cowling of a Piper
PA-28 Cadet general aviation aircraft [7]. The results of these two research projects will provide
the basis for the development of an operational, in-flight acoustic emission fatigue crack growthmonitoring system for aging aircraft.
Experimental Procedure
Data Acquisition and Digital Signal Processing
Digital signal processing is the process by which an analog waveform is converted into a discrete approximation of the analog signal. An understanding of the data acquisition system isnecessary in order to understand the process of converting an analog signal into a digital representation.
21

Fig. 2. AE data acquisition system.
As shown in the block diagram of Fig. 2, included in the AE data acquisition system is a filter for each transducer used. It is a critical first step to determine all possible sources of background noise so that these sources, where possible, can be filtered out a priori from the rest of
the signals. Many sources of background noise were identified for both the laboratory and thein-flight environment and are summarized in Table 1.
The laboratory test involved testing a fatigue specimen attached to a cyclic MTS tensile test
machine as well as a VTS shaker table. The many possible sources of background noise in thelab were identified and filtered out. For example, high frequency electromagnetic interference(EMI) signals can be filtered out using a low pass filter.
The presence of mechanical noise was very obvious in the laboratory during the tests associated with the MTS machine. The MTS machine operates using a hydraulic system to deliver aforce to the collar to which the specimen was attached. Some of these concerns were alleviatedby the fact that the hydraulic servo was mechanically isolated from the actual test platform by theuse of hoses rather than mechanical connections. Nevertheless, there was still a significant
amount of hydraulic noise that had to be filtered out. Also the fluorescent lighting in the lab
emitted a 60 Hz wave, which can impact the data acquisition system through radiation and simple electrical induction. Both of these noise sources were eliminated through the use of highpass filters.
There was also a significant amount of mechanical noise emanating from the structure itself.
These sources of noise were mechanisms such as rivet fretting and bearing failure in the bolt
holes. The goal of our test was not to eliminate these signals, as their presence in the airplane isunavoidable; hence, they were not filtered out. However, there were other sources of mechanicalnoise present, which were not necessarily directly associated with the test structure. Due to thefact that the MTS tensile test machine secures a specimen by using a hydraulically operated grip,
there was noise associated with the plastic deformation between the specimen and the grip. A
high-pass filter was used to eliminate as much of this mechanical noise as possible, while notremoving noise associated with rivet fretting and bearing failure [8]. Similar mechanical noises
were present in the VTS shaker table tests.
Moreover, there was also a substantial amount of noise present during the in-flight test.
There was EMI noise from the strobe light tail beacon. There was also mechanical noise fromthe control cables that run through the empennage to operate the control surfaces. In addition,
the noise associated with turbulent airflow over the empennage was a source of continuous noisethat was very difficult to eliminate.
22

Table 1. Sources of background noise.
Laboratory Test
Noise Source
Noise Type
Fluorescent Lights
EMI
MTS Hydraulics
Hydraulic Noise
Grip Rubbing
Mechanical Noise
Rivet Fretting
Mechanical Noise
Bearing Failure
Mechanical Noise

In-Flight Test
Noise Source
Noise Type
Tail Beacon
EMI
Control Surfaces
Mechanical NoiseInterface Rubbing
Mechanical Noise
Rivet Fretting
Mechanical Noise
Bearing Failure
Mechanical Noise

A graphical representation of AE activity is quite useful in qualitatively examining the data
gathered during the tests. There are several combinations of the six parameters recorded, in addition to the variants derived from these parameters, which are commonly used to determine thepresence of various mechanisms in a data set (Fig. 3). Analysis of these plots provided evidence
that there were at least three distinctly discernable mechanisms.
The data acquisition software provided real-time analysis of the AE data. Thus, it was possible to study the incoming signals as they occurred. This is quite beneficial for monitoring structures. For example, in the AE analysis of the structural integrity of pressure vessels it is possible
to determine from the real-time analysis of data that a leak is occurring and that failure is imminent [6]. Such information is invaluable because it allows the operator to relieve pressure before
irreparable damage occurs. The same technology was beneficial for this research, as the goal
was to identify and monitor in-flight fatigue crack growth in real time. The detection of a critical
defect at the earliest possible time is the key to saving lives and property and minimizing
maintenance costs.

Fig. 3. Graphical representation of AE data.
23

The four scatter plots that were used here to qualitatively discern the presence of different
AE mechanisms are shown in Fig. 3. These include duration vs. amplitude (Fig. 3a), counts vs.
energy (Fig. 3b), duration vs. counts (Fig. 3c), and hits vs. amplitude (Fig. 3d). The first threeplots show what appear to be three clearly discernable mechanisms. Analysis of the fourthgraph, hits vs. amplitude, would require classification and separation of the various source mechanism groupings, which typically overlap in the amplitude domain.
Neural Networks
Neural networks get their name from the fact that they closely mimic the operation of thehuman brain, the most powerful computing device known to man. The key component of thehuman brain that facilitates the processing of data is the neuron. This biological neuron is a formidable processor, composed of dendrites, soma, and axon [9]. Dendrites are the data collection
components of the neuron, as they gather data from other neurons. The main purpose of the soma is to sum the incoming data; hence, the name soma, for sum. The axon is the transmitter ofthe neuron. Its purpose is to send a signal to other neurons. There are quite a few similarities
between biological neurons and artificial neurons, which are at the heart of neural networks. Because of the fact that a method for classification of signals was required, it was necessary tochoose an artificial neural network that was well suited to the task. The Kohonen self-organizing
map (SOM) was chosen because of its excellent classification ability even when several variables (n-dimensional data) are involved.
The Kohonen Self-Organizing Map (SOM)
In order to understand how classification is accomplished, it is necessary to understand how aneural network operates. The most basic aspect of a neural network is that it accepts input data
through input neurons. In the case of the Kohonen self-organizing map used for the fatigue
crack analysis, the AE parameters recorded through digital signal processing were applied to the
input neurons. The six input neurons are the AE quantification parameters: amplitude, duration,
counts, energy, rise time, and counts-to-peak.
The function of a SOM is to operate as a topological map, where the output of the map is agraphical representation of the input data. For the purpose of the classification performed in thisanalysis, the main concern was to distinguish fatigue cracking signals from plastic deformationand mechanical noise, the two other mechanisms present. Since the concern of this analysis was
dealing with a small set of mechanisms, care was taken not to overcomplicate the classification
layer of the SOM, because misclassification can result if too many choices are provided. Therefore, a small neural network consisting of a 1 × 3 Kohonen layer was used (Fig. 4), giving the
network only three choices for classification.
The output of the SOM provides a graphical representation of data in the form of an x-y scatter plot showing visual clustering of data. The ability to visualize the output data allows thecomponent mechanisms to be readily identified. The only function served by the output layer isto generate the visual data; no computation is performed within this layer.
The SOM depicted in Fig. 4 shows the three Kohonen or classification neurons. Neuron 1 isused to classify a signal as the desired fatigue crack Mechanism 1. All signals classified as
Mechanism 2 are defined as plastic deformation signals, while the signals classified as Mechanism 3 represent mechanical noise, which includes rivet fretting and bearing failure.
24Fig. 4. Sample Kohonen self-organizing map (SOM).
Training a Kohonen SOM
In order for a neural network to be used for classification, it must first be trained. The connections between the input layer and the Kohonen layer represent weights that are used for training [9]. Initially, the weights are set as a collection of random numbers ranging from 0 to 1. The
weights are updated as the network learns. The updating of the weights is a mathematical function relating to the minimum Euclidean distance between the input variables and a particular neuron. The weights are updated according to the composition of the vector stored within the Kohonen neuron, after which the next set of input variables is processed. This process is continued
until all of the training vectors have clustered into three clearly definable regions on the x-yplane, at which point network training is complete.
Testing Data with a SOM
Data from the laboratory tests were used to train the network. An analysis of the data provided a reference set of what the AE parameter data from each mechanism “looked” like. The
test data were then classified using the trained network. The data obtained from the AE software
are time-ordered; therefore, the network classified signals in the order they were recorded. During testing with the neural network, the weights are no longer updated; rather, they remain constant, and the AE signals are simply classified by the network from the six AE parameter input
variables for each hit.
Laboratory Setup
The first segment of this research involved growing a fatigue crack in a controlled laboratoryenvironment using a notched sample, which was monitored with two wideband AE transducers.
The AE sensors used for this research were Physical Acoustics Corporation (PAC) WDIs, whichcontain built-in 60 dB preamplifiers.	
  The specimen tested was constructed of 7075-T6 aluminum[10] and bent into a channel configuration. The AE transducers were mounted with hot melt
glue in a 1/3 to 2/3 distance relationship on the structure. The purpose of this configuration was25to obviate the time difference that occurs due to the fact that it takes an AE signal from the stressconcentration notch longer to reach Channel 2 than Channel 1. If the transducers were mounted
at an equal distance from the stress concentration notch, the AE signals would tend to reach bothtransducers at the same time, thereby confusing the location analysis.
MTS Test Machine
An MTS test machine was used for the first laboratory fatigue test. One end of the specimen
was attached to a rigid support structure. The other end of the specimen was secured to the lower grip of the MTS machine (Fig. 5). The machine was programmed to displace a peak-to-peak
distance of 12.7 mm at a frequency of 2 Hz. An amplitude threshold of 30 dB was set, and a series of AE data files were recorded.

Fig. 5. MTS equipment setup.
VTS Shaker Table
For the second laboratory fatigue test, a VTS shaker table was used to cycle the specimen.
One end of the specimen was firmly attached to a rigid support structure. The other end of thespecimen was bolted to a vertical post attached to the table. The VTS machine, like the MTS
machine, was programmed to displace a peak-to-peak distance of 12.7 mm at a frequency of 2
Hz, and the AE data acquisition and equipment setup was as described previously. A third laboratory test was performed using the VTS shaker table with the equipment threshold set at both 10
dB and 30 dB in order to correlate the AE data taken at different threshold values. These correlations (discussed in reference [11]) allowed the data to be analyzed in order to sort out fatigue
crack growth signals from plastic deformation and mechanical noise signals from both the laboratory and the in-flight data.
In-Flight Setup
The second part of this analysis involved monitoring a fatigue specimen, similar to the laboratory specimen, during flight. Like the laboratory specimen, the in-flight fatigue specimen wasconstructed of 7075-T6 aluminum bent into a channel configuration. Figure 8 shows the specimen with two wideband AE transducers and installed in the vertical tail of the Cessna T-303
Crusader aircraft [11]. These transducers were wired into a portable computer running the realtime data acquisition software. The industrial grade portable computer received power from two
portable battery packs and not from the airplane electrical system.

26

Fig. 6. In-flight test setup.
Acoustic emission data were recorded during a variety of in-flight maneuvers including taxi,
takeoff, steady level flight, rolls, and Dutch rolls. However, the collection of data during inflight tests was limited to a single flight. This limited data collection was due to the fact that fatigue cracks discovered in the wing ribs after the first test flight led to the sale of the aircraft.
Another limitation in the ability to collect data arose from FAA regulations. Due to the fact thatthe installation of the fatigue specimen was deemed by the FAA to be an experimental modification, student pilots were prohibited from operating the aircraft. Moreover, because the installation of the fatigue specimen was considered an experimental modification, extensive preinstallation tests were required. The FAA also required that a Designated Engineering Representative (DER) sign off on the proposed installation. Consequently, there was insufficient time
to conduct the structural tests required for the installation of a fatigue specimen on an alternate
aircraft.
One other disappointment surfaced during the in-flight test phase. The amplitude threshold
was set to 10 dB for the first test. It was expected that additional flights would be possible andwould be conducted, like most of the laboratory data, at the higher 30 dB threshold. Since theseflights were not conducted, the data collected were limited to the 10 dB set. This necessitatedthe previously mentioned third laboratory test in order to correlate the data taken at the 10 dB
and 30 dB thresholds [11]. Once this was accomplished, it was found that the data collected during the in-flight test did indeed contain fatigue cracking signals.
Results
The results obtained during the laboratory and in-flight tests were analyzed using both AE
parameters and neural networks. The results from the laboratory test were used to train the neural network. The data from the in-flight test were then tested in the trained neural network in27

order to classify the signals as cracks, plastic deformation, or mechanical noise. The first step in
the analysis of the results recorded during the laboratory tests was a study of the AE data.
Source location is a very beneficial asset of AE testing. Depending upon the material andstructure through which the wave propagates, there is a characteristic velocity at which the wave
travels. The material used throughout the tests, 7075-T6 aluminum, is a relatively popular alloyin the aerospace industry. Since the material used throughout the tests was 0.040 inch thicksheet, the main concern was with Lamb waves, which are the stress waves that propagate in thinplates or sheets.
One problem encountered in AE study is the attenuation of waves that propagate through astructure. The major concern involved with thin plates is dispersion. Dispersion causes not only
the amplitude to diminish but also the shape of the waveform to change as different frequency
components travel at different speeds.
The velocity at which waves travel through a medium is the key to source location. The fundamental velocity at which waves travel in thin plates is the plate wave velocity [12]. In order tocalculate the plate wave velocity, the transverse or shear wave velocity, c2, was determined for7075-T6 aluminum to be 3.10 mm/µs. The velocity of the symmetrical (extensional) Lamb wave
is a function of transverse velocity and is calculated as 5.36 mm/µs for 1.02 mm plate. This value
was verified to be reasonably accurate, in that when it was employed in the location analysis, itcorrectly indicated activity emanating from the approximate location of the stress concentrationnotch.
A major consideration involved in the testing process is the determination of what a cracksignal looks like. The purpose of conducting the laboratory tests was to determine the AE parameters associated with fatigue cracking, plastic deformation, and mechanical noise. A methodfor determining these parameters had to be developed. Due to the design of the specimen, it wassafe to assume that all of the crack signals would originate at the stress concentration notch in thecenter of the specimen. It was also assumed that there would be a great deal of plastic deformation originating from an area in close proximity to the stress concentration notch or the tip ofthe fatigue crack. By using source location, it was possible to look at signals originating fromthe center section only (Fig. 7). It was also reasonable to assume that all signals found in thislocation were either cracking signals or plastic deformation signals and not mechanical noise.

Fig. 7. Source location zone.

28

Fig. 8. Location plot of all mechanisms.
The first step in the analysis was to use acoustic emission processing to determine what atypical waveform representing each type of mechanism looked like. Linear location analysis wasused to filter out mechanical noise by keeping only signals occurring in the center section of thespecimen. It was possible to develop a location plot representing events vs. distance (Fig. 8). Notice that the plot displays AE events and not individual hits. There was clear evidence of a higher
number of events around the 3 inch mark. These events represent fatigue cracking and plastic
deformation only. Any mechanical noise signals would show up at the far extremes of the plot.
Source location proved to be an excellent tool for differentiating mechanisms that were present in a particular data set. Figure 9 shows a typical duration vs. amplitude plot containing all
three mechanisms. Figure 10 is a plot of the exact same data, except that it was filtered using
source location to contain only fatigue crack and plastic deformation signals from the center ofthe specimen. Therefore, it was possible to sort all the AE signals into three categories. In Fig.
10, the high amplitude signals are fatigue crack signals, and the lower amplitude signals are plastic deformation signals. The rest of the signals (Fig. 9), those having longer durations, are mechanical noise.
A brief understanding of AE parameters leads to the realization that plastic deformation signals do not have the same AE parameters as cracking signals. Looking at only the signals concentrated around the notch yields a better representation of the data of interest (Fig. 10). From
this second plot it is evident that location analysis provides an effective means by which to isolate these two mechanisms from mechanical noise. Here it appeared that 67 dB was the dividing
line between fatigue cracking and plastic deformation. Thus, signals with an amplitude of 67 dB
or lower were classified as plastic deformation, while those with an amplitude of 68 dB or greater were classified as fatigue cracks.

29

Fig. 9. Duration vs. amplitude plot for all mechanisms.

Fig. 10. Duration vs. amplitude for fatigue cracking and plastic deformation.
Photographs of the fatigue specimen used in the first shaker table test were taken in order todocument the size and location of the fatigue crack (Fig. 11). The photographs indicate that afatigue crack did indeed grow where anticipated. In addition, a finite element analysis was performed [11] in which the color bands closely resembled the plastic deformation zone ahead of30

the crack tip. The plastic deformation zone is somewhat difficult to discern from the photographof Fig. 11; however, its presence along with the presence of the crack reinforces the validity of
the classification process.

Crack

(a)

(b)
Fig. 11. Fatigue crack, before (a) and after (b) formation.

The above techniques were used to classify the signals received by the AE transducers in thelaboratory tests. Reference [11] gives all of the details for each of the laboratory tests, plus explaining the steps required to sort, train, test, and classify AE signals as fatigue cracking, plastic
deformation, or mechanical noise. (This includes the correlation of data taken at the two different threshold values, 10 dB and 30 dB.) The correctly classified signals were subsequently used
to train a neural network.
The neural network was trained using the data recorded during the laboratory tests. The
training file was made up of 300 signals. Of these 300 signals, there were equal numbers ofcracking, plastic deformation, and mechanical noise -- 100 of each. These signals were gatheredrandomly with respect to time.
Once the neural network had been trained, it was possible to test the in-flight data by passing
it through the trained network. All 15 in-flight data files were tested one at a time. The output of
the neural network using the test data was graphed to provide a visual representation of the classified mechanisms (Fig. 12). The output file contains three distinct regions, representing thethree failure mechanisms. The first mechanism, fatigue cracking, is seen at the location x = 1
and y ≥ +0.212. The second mechanism, plastic deformation (PD), can be seen at x = 3 and –0.212 < y < +0.212. Finally, the third mechanism, mechanical noise (MN), is at x = 2 and y ≤ –0.212. The values on the x-y plane are the output values of the neural network and can perhaps
be represented most simply by the y ranges given in Table 2.
Table 2. Neural network x-y output.

Mechanism
Fatigue Cracking
Plastic Deformation
Mechanical Noise

Neural Network Output
y ≥ +0.212-0.212 < y < +0.212
y ≤ -0.212

31

0.4

0.3

0.2

0.1

0
0

0.5

1

1.5

2

2.5

3

3.5

-0.1

-0.2

-0.3

-0.4

Fig. 12. Sample neural network x-y output plot.
It is evident from the summary of the in-flight test results (Table 3) that a fatigue crack did
indeed grow in the specimen installed in the vertical tail of the Cessna T-303 Crusader. It is interesting to note that no fatigue cracking signals were recorded during taxi, either before or after
flight; this is most certainly due to the fact that there is little to no load applied to the tail section
during taxi.
Most of the ground loads experienced by this aircraft were experienced in the engine compartment and landing gear and were due to vibration of the engine. The ratio of mechanisms
classified during the taxi maneuvers was very similar, which came as a bit of a surprise since themaneuvers occurred so far apart. The vast majority of the signals recorded were plastic deformation signals. A small percentage of the signals were attributed to mechanical noise. The
probable explanation for this phenomenon arises from the fact that the loads are so small that
there are no large deflections, so the mechanical noise is subdued.
The fatigue crack growth activity begins to rise during takeoff. This seems quite reasonable,
as there are higher loads present in the vertical tail during takeoff, probably as a result of crosswinds. The percentage of signals resulting from mechanical noise also increases and may also be
attributed to the higher aerodynamic loads. There was a relatively similar breakdown of mechanisms during landing.
The crack signals increased during steady level flight. However, the signals subsided during
the initiation of the rolling maneuvers. This was not expected, but could be attributed to thecrack growth being stopped by a grain boundary. The mechanical noise signals continue to increase during the early rolls, indicating there is rivet fretting, bearing failure, and interface rubbing present under increased loading conditions.
32

Table 3. Summary of In-Flight Results.
Maneuver Crack Events Crack % PD EventsPD %MN Events

MN %

Taxi
Takeoff
FlightFlightFlightDutch Roll
Roll
Roll
Dutch Roll
FlightLanding
Taxi

93.0%
52.3%
43.3%
66.1%
52.5%
44.5%
48.7%
17.6%
15.2%
21.7%
69.0%
93.7%

73
159
398
400
87
131
364
31
30
104
138
60

7.0%
33.3%
33.4%
33.8%
43.9%
51.6%
46.3%
10.5%
10.9%
14.1%
28.8%
6.3%

0
69
277
1
7
10
40
212
204
472
11
0

0.0%
14.4%
23.3%
0.1%
3.5%
3.9%
5.1%
71.9%
73.9%
64.1%
2.3%
0.0%

963
250
515
782
104
113
383
52
42
160
331
895

During the second roll, the fatigue crack activity began to increase at a rapid rate, peaking
during the final Dutch roll. The Dutch roll is a wig-wag maneuver that imposes tremendous aerodynamic loads on the aircraft, especially the vertical tail. These increased loads were the main
reason that the crack activity increased at such a high rate. This higher rate of crack activity was
accompanied by a reduction in the presence of mechanical noise as well as plastic deformation.
Returning to steady, level flight led to a reduction in the crack signals, followed by landing,where the fall-off continued.
Conclusions
The goal of this research was to develop an aircraft structural health monitoring system forkeeping track of fatigue crack growth while in flight. It has been shown that it is indeed possible
to monitor fatigue crack signals in a noisy in-flight environment using AE nondestructive testing
techniques in combination with neural networks. In conclusion, this research successfully
demonstrated a prototype in-flight AE fatigue crack monitoring system for aircraft structures. It
is envisioned that the application of such structural health monitoring systems in aging aircraft
could warn of impending failure and allow for replacement of parts when needed rather than atconservatively calculated intervals.
Recommendations
The next step in the development would be to incorporate the current research being done inback-propagation neural network (BPNN) fatigue life prediction such that it can be applied to thedata gathered by in-flight aircraft structural health monitoring systems. Reasonably accurate
BPNN fatigue life predictions have been obtained in aerospace bellows [14], notched tensile fatigue specimens [15-16], and bridge steel I-beams [17]. In each case, the entire acoustic AE amplitude distribution for various ranges in the fatigue cyclic life (0-25%, 25-50%, and 50-75%)
was input to a BPNN, and the associated fatigue life predictions were made with worst case errors of between 5-20% for the three different structures.

33

Analogous work has been done for low proof load (≤25% Pburst) burst pressure predictions incomposite pressure vessels in which ±5% worst case errors have been obtained by using the matrix cracking only (since matrix cracking is a precursor to delamination and fiber breaks) portionof the AE amplitude distribution as input to the BPNN [18]. Hence, for the fatigue life prediction problem, it is recommended that the plastic deformation only data portion of the AE amplitude distribution be used as input to a BPNN (since plastic deformation is a precursor to bothplane strain and plane stress fatigue crack growth), which should reduce the worst case error predictions to a similar ±5% range. Such prediction accuracies would lead to minimized maintenance costs and extended service lives of aging aircraft, which is the ultimate goal of this research effort.
Acknowledgements
The authors would like to extend a special thanks to fellow graduate student Mr. Samuel G.
Vaughn III, whose help in conducting experiments and analyzing data increased the quality ofthis research. Master of Science in Aerospace Engineering thesis committee members Dr. James
G. Ladesic, who contributed by acting as the FAA Designated Engineering Representative(DER) in making modifications to the airplane, and Dr. Frank J. Radosta for his guidance in thefinite element analysis of the channel beam structure, must also be thanked. Finally, this material was based upon original work performed by graduate student Mr. Robert J. Demeski and supported by the National Science Foundation Grant # DMI-9503017 under an Embry-Riddle Aeronautical University (Daytona Beach, Florida) subcontract to Martingale Research Corporation
and Principal Investigator Mr. Harvey L. Bodine (now with Neuralytics, Richardson, TX).
References
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2. Parrish, B. “Acoustic Emission Techniques for In-Flight Structural Monitoring.” SAE
Technical Paper No. 801211. Society of Automotive Engineers, Warrendale, PA, 1980. pp.
1-4.
3. McBride, S.L. and Maclachlan, J.W. “Acoustic Emission Monitoring of Aircraft Structures.”
Journal of Acoustic Emission. Vol. 4, April 1985. pp. 151-154.
4. Scala, C.M., Bowles, S.J. and Scott, L.G. “The Development of Acoustic Emission for
Structural Integrity Monitoring of Aircraft.” Defense Technical Information Center Accession No. ADA196264. Aeronautical Research Labs, Melbourne, Australia, 1988. pp. 1-35.
5. Granata, D.M., Kulowitch, P. and Scott, W.R. “In-Flight Acoustic Emission Monitoring of
Crack Growth.” Review of Progress in Quantitative Nondestructive Testing: Vol. 13A. Editors D.O. Thompson and D.E. Chementi. Plenum Press, 1994. pp. 461-468.
6. Pollock, A.A. "Acoustic Emission Inspection." Metals Handbook. Ninth Edition, Vol. 17,
1989. pp. 278-292.
7. Hill, E.v.K. and Vaughn III, S.G., “Fatigue Crack Monitoring of an Aircraft Engine Cowling in
Flight,” Nondestructive Testing Handbook, Third Edition: Volume 6, Acoustic Emission
Testing, American Society for Nondestructive Testing, Columbus, OH, 2005, pp. 367-376.
8. Spanner, J.C., Sr. "Acoustic Emission Noise." Nondestructive Testing Handbook (3rd ed.):Vol. 6, Acoustic Emission Testing. Technical Editors R.K. Miller and E.v.K. Hill. Editor
P.O. Moore. Columbus, OH: American Society for Nondestructive Testing, 2005. pp. 4144.
34

9. Fausett, L.V. Fundamentals of Neural Networks. Englewood Cliffs, NJ: Prentice-Hall,
1994. pp. 5-7, 169-175.
10. "Aluminum Reynolds 7075b Uns_A97075 75f Temper_T6 - Aircraft Structure." CenBASE/Materials, www.Online.Internet. 1995.
11. Rovik, C.L. “Classification of In-Flight Fatigue Cracks in Aircraft Structures Using Acoustic
Emission and Neural Networks.” Master of Science in Aerospace Engineering Thesis. Embry-Riddle Aeronautical University, Daytona Beach, FL, 1998.
12. Egle, D.M. "Wave Propagation." Nondestructive Testing Handbook (3rd Edition): Vol. 6,
Acoustic Emission Testing. Technical Editors R.K. Miller and E.v.K. Hill. Editor P.O.
Moore. Columbus, OH: American Society for Nondestructive Testing, 2005. pp. 96-97.
13. Miller, R.K. "Fundamentals of Acoustic Emission Source Location." Nondestructive Testing Handbook (3rd Edition): Vol. 6, Acoustic Emission Testing. Technical Editors R.K. Miller and E.v.K. Hill. Editor P.O. Moore. Columbus, OH: American Society for Nondestructive Testing, 2005. pp. 126-127.
14. Ballard, D.L.W. and Hill, E.v.K. “Fatigue Life Prediction of Edge-Welded Metal Bellows
using Neural Networks and Acoustic Emission Amplitude Data.” ASNT Fall Conference andQuality Test Show Paper Summaries. Columbus, OH: American Society for Nondestructive
Testing, 1998. pp. 146-149.
15. Suleman, J., Hill, E.v.K., Villa, E. and Okur, M.A. “Neural Network Fatigue Life Predictionin Aluminum from Acoustic Emission Data.” Aging Aircraft 2009, The 12th Annual Joint
AIAA/ASME/DoD/NASA Conference on Aging Aircraft, Kansas City, MO, May 2009. 29
pages.
16. Barsoum, F.F., Suleman, J., Korcak, A. and Hill, E.v.K. “Acoustic Emission Monitoring and
Fatigue Life Prediction in Axially Loaded Notched Steel Specimens.” Journal of Acoustic
Emission.” Vol. 27, 2009. pp 40-52.
17. Barsoum, F.F., Hill, E.v.K., Suleman, J., Korcak, A. and Zhang, Y. “Neural Network Fatigue Life Prediction in Notched Bridge Steel I-Beams from Acoustic Emission AmplitudeData.” Journal of Acoustic Emission. Vol. 29, 2011. pp. 251-259.
18. Hill, E.v.K., Iizuka, J., Kaba, I.K., Surber (now McCann), H.L. and Poon, Y.P. “Neural
Network Burst Pressure Prediction in Composite Overwrapped Pressure Vessels Using
Mathematically Modeled Acoustic Emission Failure Mechanism Data.” Research in Nondestructive Evaluation, Vol. 23, 2012. pp. 89-103.

35

Acoustic Emission Monitoring of Composite Blade of
NM48/750 NEG-MICON Wind Turbine
Dimitrios Papasalouros1, Nikolaos Tsopelas1, Athanasios Anastasopoulos1,
Dimitrios Kourousis1, Denja J. Lekou2 and Fragiskos Mouzakis2
1
Mistras Group Hellas ABEE, El. Venizelou 7 & Delfon, 14452 Metamorphosis, Athens, Greece
2
Centre for Renewable Energy Sources and Saving (CRES), 19th km Marathonos Avenue, 19009
Pikermi, Greece,
Abstract
In-service monitoring of wind-turbine (W/T) blades during operation presents practical difficulties due to accessibility restrictions, nature of the material, size and geometry of the blades aswell as power requirements. As a result, attempts for health monitoring of wind turbine blades
are limited. In the present paper, health monitoring of the blades of an operating NEG-MICON
NM48/750 with acoustic emission (AE) will be discussed. Included are W/T instrumentation andvarious other technical apparatus that was applied in order to overcome power limitations, remote monitoring set up, as well as development of software for automated statistical processingof AE data. The AE module was successfully operated and acquired data, under real operating
conditions for more than six months. Results are presented from AE measurements acquired under different operating conditions, correlated with parametric data and analyzed with both linear
location and advanced AE data processing techniques. Finally the paper discusses the system
performance during its total installation time and operating conditions, demonstrating the fast
integration into existing structures and its modularity to external systems and data exchange,
making it a good candidate for real-time monitoring of industrial size wind generators.
Keywords: Wind turbine blades, composite materials, health monitoring, automated AE dataprocessing
1. Introduction
Until recently, in-service monitoring of wind turbine blades has not received major attention
due to many practical difficulties encountered, such as accessibility restrictions, nature of thematerial, size and geometry of the blades as well as power requirements. However, given the increased size of modern wind turbine (W/T) blades of lengths up to 75m on the latest wind generators and their growing complexity and fabrication cost, blade monitoring is becoming more andmore important to operators, since maintenance is difficult and the potential financial losses accruing from unscheduled outages are increasingly growing, especially when off-shore W/T areconcerned, where replacing a damaged blade is a major undertaking. In-service monitoring could
detect structural degradation long before ultimate blade failure, and would allow the operator todecide whether a preventive maintenance should occur as soon as possible or whether the blade
should be left turning until the next scheduled maintenance period.
Acoustic emission (AE) is highly effective in detecting and identifying damage in W/Tblades and generally composites under load, and has proven its validity through numerous laboratory tests including full-scale blade tests [1-7]. However, in-service monitoring experience of
W/T blades was limited [8] until recently when a NEG-MICON NM48/750 W/T of Center for
Renewable Energy Sources and Saving (CRES) wind farm was instrumented with a multichannel
AE system for structural health monitoring of the W/T blades during operation [9-11]. The
J. Acoustic Emission, 31 (2013)36© 2013 Acoustic Emission Groupsystem was successfully installed and operated for more than 6 months under real operating conditions, collecting a considerable amount of AE data, enabling evaluation of the system under
different operating conditions.
In the present paper results from AE measurements acquired under different operating conditions, correlated with parametric data and analyzed with both linear location and advanced dataprocessing techniques will be presented. In addition, presentation includes W/T instrumentationand various other technical apparatus that was applied in order to overcome power limitations, aswell as remote monitoring set up and development of software for automated statistical processing of AE data. Finally the paper discusses the system performance during its total installation time and operating conditions, demonstrating the fast integration into existing structures andits modularity for connection to external systems and data exchange, making it a good candidate
for real time monitoring of industrial size wind generators.
2. Instrumentation of an In-service Wind Turbine
AE monitoring of W/T blades during operation poses several challenges and difficulties to be
met by the condition monitoring system, not only due to the (inherent in composites) complex
nature of the blade’s defects aimed to be detected, but also for a number of application-specificfactors such as:• the harsh environment and the conditions under which such a system will operate,• accessibility reasons for system installation in the hub and sensor mounting (e.g. on theinside surface of the blades),• power issues for the system residing in the hub, and, most important,• the necessity for wireless data communication or telemetry posed by the rotation of theblades.
In addition, since AE may often be correlated to or caused by external parameters such as rotational speed, wind, loading, manipulations etc., the AE Condition Monitoring System (CMS)
should be capable of real-time acquisition of such parameters, in order to enhance analysis andinterpretation.
The AE CMS selected for this project meets the above requirements as it is a compact, multichannel AE system (Micro-II by Physical Acoustics Corporation (PAC)), with low power consumption (below 100W) and versatile to be powered by dc input (occasionally available in thenacelle of mid-sized turbines – as opposed to standard 220Vac), relatively light weight in order
to be quickly mounted on rotating parts and to minimize unbalancing forces due to its incorporation inside the hub, and also having the capability of being remotely controlled as well as transmit the collected data through a wireless network (Fig. 1a). In addition, it can be loaded with up
to 4 multi-channel AE boards (Fig. 1b), providing up to 32 active AE channels. Finally, for the
purpose of enhanced data interpretation and correlation with test and loading conditions, it accepts external signals such as wind velocity, rotation speed, torque, yaw angle, etc. from existing
wind turbine instrumentation.
The AE sensors used were three PAC-R6I-AST with resonant frequency of 60 kHz and five
PAC-R15I-AST with resonant frequency of 150 kHz. Both types of sensors incorporate integral
amplifier allowing them to drive long cables without the need for a separate preamplifier and also minimize the RFI/EMI interference. In addition, they feature an Auto Sensor Test (AST) capability allowing the sensors to act as pulsers and remaining as receivers simultaneously, enabling automated verification of each sensor coupling and performance at any time desired by the37user. AE sensors’ mounting was performed on the inner surface of one of the three blades (with theblades on the wind turbine) using special hold-down devices developed by CRES for the long term
attachment of the sensors while sensor cables were connected and securely routed to the rotor’s hub.

(b)

(a)

Fig. 1. (a) PAC Micro-II AE system installation inside the hub and (b) PCI-8 AE board.
Max
StrainPosition

Min
StrainPosition

0.75 m0.75 m

0.5 m

CH3

CH1

CH4
CH2

1.0 m

CH5

1.22 mCH6
WIRELESSROUTERSTRAINGAUGE

VIA WIFI CONNECTION /VIRTUAL DESKTOP AT
CLOSE PROXIMITY

CH7
CH8PAC SAMOS MICRO IIVIRTUAL DESKTOP ON REMOTE LOCATION TERMINALS /PERIODIC STATISTICS TO SCADA

Fig. 2. Schematic illustration of the structural condition monitoring AE system.
Using the eight AE sensors mentioned above, three different linear groups were considered, inorder to monitor three different areas of the blade. The first group, consisting of sensors (R15IAST) corresponding to channels 1 and 2, was used to monitor the AE activity on the internal sparof the blade. The second sensor group, consisting of channel 3 to channel 5 (R15I-AST), wasused to monitor the AE activity on the trailing side of the blade. Finally the third group, consisting of sensors corresponding to channels 6 to 8 (R6I-AST), was used to monitor the AE activity
on the root. Sensor placement along the blade, as well as an illustration of the complete structural
AE CMS, is shown in Fig. 2, while sample images of the sensors mounted using the speciallydeveloped hold-down fixtures are shown in Fig. 3 and Fig. 4.
38

(b)

(a)

Fig. 3. (a) Mounting of two AE sensors on the internal spar of the blade and (b) one of the three
AE sensors mounted on the inner surface of the trailing side of the blade

(b)

(a)

Fig. 4. One of the two AE sensors mounted on the internal spar of the blade and (b) one of thethree AE sensors mounted near blade’s root.
The system was powered with 12V dc through a slip-ring installed on the main shaft of thewind turbine by CRES personnel (Fig. 5a), since there was no power available inside the hub of
the stall control wind turbine. However, this slip-ring is not needed on latest wind turbines sincemost of them provide power inside the hub. Finally, a strain gauge bridge for monitoring edgewise bending moments was mounted on the outer surface of the blade and was connected to theparametric interface of the AE system (Fig. 5b). This would allow the exact information about
the blades’ position during AE activity to be recorded, as well as an approximate estimation of
the power output, enhancing the analysis of the AE data acquired by the system.
3. System Connection to SCADAThe AE CMS was connected through wireless connection provided by a wireless router positioned in the base of the W/T tower to the Ethernet network available on the wind turbine and,through this, to the Internet. This way the system could be monitored from anyplace with internet
access and it could also transmit the complete AE data acquired and/or only statistical data andalarms to the central monitoring system of the wind turbine. In addition, the AE system was able
to send automated emails notifying about detected faults, exceeding thresholds or normal operation of the system (Fig. 6a). A system upgrade could also enable notifications and alerts to besent automatically through SMS.
39

(a)

(b)

Fig. 5. (a) Installation of the slip ring to supply power to the AE CMS. (b) Mounting of a strain
gauge on the outer surface of the leading edge of the blade
In addition, through special software developed (Fig. 6b), the system was able to process inreal-time the recorded AE data and automatically produce periodical statistics for all the timedriven (TDD) and hit-driven (HDD) data features, which were then sent over the wireless network to the central monitoring server.

(a)

(b)

Fig. 6. (a) Setup of internet monitoring and automated email notifications. (b) Software interface
setup for performing automated AE statistical processing.
4. AE Setup & Location of Simulated AE Sources & Events
In order to estimate the AE generated from the different monitored parts of the W/T blade themounted sensors were grouped through the acquisition software in three separate linear locationgroups, as noted in Sec. 2. After sensor installation several tests were performed within the linear
segments that were defined from the sensor topology. For these tests, Hsu-Nielsen (HN) sources[14] were applied between all sensor pairs and real time linear location capabilities of the acquisition software were verified. Figure 7 shows the results of the X position of the linear event location between sensors corresponding to channels 1 and 2 that define the monitoring area of the40

spar, during introduction of artificial AE sources along the sensor line. Additionally, Fig. 8
shows located AE events at the monitoring area of trailing edge of the blade, within the linear
span defined by channels 3 to 5. In both Figs. 7 and 8, attenuation in dB/m can be also estimatedfrom the Amplitude (dBae) drop versus X position (m).

Channel 2
Channel 1
Fig. 7. Amplitude vs. Event Location (x - marks) and (#) Hits vs. X Event Location (Bars) for
AE located HN source applied in spar area showing located events.

Fig. 8. Amplitude vs. Event Location (x - marks) and (#) Hits vs. X Event Location (Bars) for
AE located HN source applied in trailing edge area. Color for channel of first hit. Red: Ch. 3,
Green: Ch. 4, Blue: Ch. 5.
Overall, the location of simulated AE events has yielded accurate results, thus providing highconfidence that, if damage occurs and starts accumulating under operating conditions, the system
will have the sensitivity to locate it within the monitored areas. Additional real-time location
alarms can be set up from the system as output triggers in order to provide extra monitoring inputs to any given central monitoring system.
5. Continuous Monitoring
System was successfully installed and operated via the connected networks. In total, morethan six months of data under real time conditions were acquired. Statistically, this time period islong enough to capture a large variation of the W/T operating parameters and conditions, which
may have different impact upon the AE of the inspected area. The AE module acquired morethan 300 GB of data during its continuous operation. The system performance and operation
were checked randomly during its total operating time, by different experienced AE operators, indifferent times from different local and international terminal locations in order to provide feedback for its usability and performance. On-line data analysis was performed using the advanced
AE analysis software NOESIS [13] installed in the same system, while the system was in dataacquisition mode. The system’s behavior was found to be very reliable and stable in all cases,
41

even during prolonged periods of power loss or technical difficulties, in which cases the system
always rebooted and resumed operation and acquisition when the power was restored. A presentation of the wind speed, generated power and wind direction variation during the last twomonths of system operation is shown in Fig. 9.

Fig. 9. Wind speed (m/sec), Average Generated Power (kW) of NEG – MICON 750 and Wind
Direction (degrees) vs. Time (days) from October 13th to December 21st 2011 (69 days of 10 min
statistical data from CRES). The color ranges of the data correspond to the average RPM of thewind turbine (0-22 RPM).
The additional color dimension in Fig. 9 represents the 10 minutes average rotational speedof the wind turbine rotor. The zero RPM values are colored green in order to show the periods
that the generator was stopped either from a lack of wind, maintenance or other reasons. The
blue color represents the low RPM operation (15 RPM) as well as any intermediate values thatthe generator RPM increases from zero to low RPM or low to high RPM (22 RPM). Finally the
red color was used in order to identify the periods that the generation was operating in highRPM. It is worth noting that even when the generator was at stop position there is still AE fromthe blade due to the wind loading indicating the stochastic nature of loading and thus of the AE
data. In addition, the distribution of Average Signal level (dB) in correlation with parametricvalues (Figs. 10 and 11) also highlights the stochastic nature of the data set. The attempt for a
simplified analysis based on Time Driven Data alone did not result in information that can beconnected with incipient damage.
6. AE Data Analysis
As previously mentioned, a simple review of Figs. 9, 10 and 11 should reveal the stochastic
nature of loading and the weather operating conditions that the wind turbine was subjected toduring the monitoring period. In order to overcome the limitations imposed from the simplified
Time Driven data analysis, the complete AE data set was acquired and processed. During the total time that the system was in acquisition, while installed inside the hub of the wind generator,
the volume of acquired data exceeded 300 GB. This six-month period should be statistically sufficient to encapsulate the generated AE from the majority if not all the operating and loading
conditions. This results in a unique and complete set of AE data that should reflect an actual operation of the wind turbine in larger installations.
42Fig. 10. Time Driven Average Signal Level (dBae) vs. Average Generated Power (kW) for theperiod between 13 October and 21 December 2011, color by wind velocity (m/sec) for every
channel.

Fig. 11. Time Driven Average Signal Level versus (dBae) vs. Wind Velocity (m/sec) for the period between 13 October and 21 December 2011, color by average generated power (kW) for
every channel. Channel order is identical to Fig. 10. [Enlarge page to see legends.]
In this section an example of a typical AE data set during the wind turbine’s normal operation under variable weather conditions is presented, with standard analysis steps in order to verify and assess any localization of genuine AE events that might be present in the areas of interest.
Figure 12 shows the acquired AE activity during the time period of from 20:30, 21.10.2011
to 00:20, 23.10.2011. Along with the acquired AE activity, the other graphs correspond to theWind Velocity (m/sec), the average RPM and the Average Generated Power (kW) of the wind43

turbine. The wind velocity, average RPM and generated power data were provided by CRES andcorrespond to average values over a 10-minute time period.

Fig. 12. From top to bottom: AE amplitude (dB) vs. time (sec), wind velocity (m/sec) vs. time(sec), rotational speed (RPM) vs. time (sec) & average power output (kW) vs. time (sec).

Fig. 13. First Hits of Located Events vs Time (sec). From top to bottom: Group of Channels 1-2(Spar Area), Group 2 of Channels 3-5 (Trailing Edge Area), Group 3 Channels 6-8 (Root Area).
The specific data set was selected in order to investigate and assess the generated AE, causedby the variable operating conditions. In the presented time segment, the wind generator goesfrom a complete stop to normal operation due to rising wind velocity. Periods of low RPM andhigh RPM are also visible along with the specific times that the rotational speed was changed
from low to high. Additionally, the wind throughout the selected time span appears to have significant variations and the resulting AE activity especially during the last hour of the time44

segment (specifically, 31 to 100 ks) is of interest due to the fact that an increasing trend is visible
in AE activity while the wind velocity significantly drops and then rises up again. Figure 13
shows the located events per sensor group during the time segment under investigation. Linear
location for each sensor group of the raw data of the specific time segment is performed andgraphs of the located first hits of the monitored areas are generated (Fig. 14). The first hits of thelocated AE events from each monitored area (Spar, trailing edge & root) were easily separatedusing NOESIS [13] for their features to be compared with the simulated HN sources that wereapplied on the same monitored area. An X position distribution of the located AE events (firsthits) from each monitored area is shown in Fig. 14.

Fig. 14. First Hits of Located Events vs. Event’s X Location (m). From top to bottom: Group of
Channels 1-2 (Spar Area), Group 2 of Channels 3-5 (Trailing Edge Area), Group 3 Channels 6-8(Root Area).
The following graphs (Fig. 15) show in blue the first hits of the located AE events on themonitored area of the spar during the 24 hour segment under analysis. Overlaped in red is thedataset of first hits of simulated HN sources. The red dataset (simulated sources) corresponds tothe expected AE events that should be present in the case of actual damage in this monitoredarea. Direct comparison of both data sets reveals that the acquired data clusters have a good
separation. The signatures obtained due to the stochastic wind loading and the estimated feature
characteristics that correspond to HN sources show that an qualitative AE signal estimation canbe made seperating the signals from the general wind loading and operation of the wind turbinewith those corresponding to incipient damage.
By reviewing these graphs useful conclusions can be drawn. The located AE events whencompared with the simulated sources applied on the same areas appear to have similar durations
and counts but an amplitude range lower and close to the low sensitivity limit of the system. The
large volume of AE that is acquired by the equipment corresponds to many different phenomenaduring the operation of the wind turbine. For the specific case, only sources that have similar AE
features to the simulated ones are of interest. In the specific area, a small amount of AE activity(~5 events out of more that 400,000 AE hits) can be considered relevant. Due to the fact that thisactivity corresponds to a full data set of approximately 24h, it can be also considered a very low
AE activity. The aforementioned facts yield an approximation of the AE rates, amplitudes andfeatures ranges that are to be expected during normal operation. These can be used to fine-tune
45

the AE system for minimal data acquisition (and therefore data volume efficiency) focused in thedesired and expected parameters.

Fig. 15. Signature graphs of AE features (in 2D & 3D feature space), in blue are the located AE
events in the spar area during the 24h time segment under analysis and in red the simulated HNsources. Circled Xs mark 5 relevant located AE events. Some outlier hits in red are also marked.
[These markings become clearly visible by expanding this figure to 500%. Also see arrows.]

Fig. 16. Signature graphs of AE features (in 2D & 3D feature space), in blue are the located AE
events in the trailing edge area during the 24h time segment under analysis and in red the simulated HN sources.
The same analysis is performed for the remaining two monitored areas of the trailing edge
and the root section of the blade of the wind turbine. Figure 16 shows in blue the first hits of thelocated AE events on the monitored area of the trailing edge during the 24h segment underanalysis. Overlaped in red is the dataset of first hits of simulated HN sources applied on the same
area. The red dataset (simulated sources) corresponds to the expected AE events that could bepresent in the case of incipient damage in this monitored area. As in the previous case, good46

cluster separation appears to exist using both two-dimensional and three-dimensional feature coordinates.
In the final case of the third monitored areas (root area), the clusters separation between normal turbine operation and simulated HN sources again appears to be good and to yield clear distinction between the two feature areas of interest. Figure 17 shows in blue the 24h data fromnormal turbine operation and in red the HN simulated sources applied on the same area. As in all
previous cases, these signatures can be used to fine-tune the system to acquire only the AE dataof interest that should correspond to incipient damage.

Fig. 17. Signature graphs of AE features (in 2D & 3D feature space), in blue are the located AE
events in the root area during the 24h time segment under analysis and in red the simulated HNsources. Selection of 191 relevant located AE events is shown. Additionally there is a clear cluster separation in 3d feature space
7. Conclusions
Based on the large continuous monitoring period and the selected analysis period, the AE signatures of the normal operation of the wind turbine are obtained. Based on this large statistical AE
data set and the AE signatures obtained from the simulated HN sources, a direct comparison isenabled on the corresponding monitored areas. This comparison can be used in order to maximize the efficiency of continuous monitoring in various respects. One example is that the ranges ofthe estimated normal operation signatures based on multiple features can be set as alarm and/or
front end filters. In this way the system is mainly focused to features space areas that have ahigher likelihood to correlate with incipient damage while discarding all other (irrelevant) data,
ensuring a more compact data set, thus greatly minimizing the data storage needs. In addition,
due to the more compact data set, streaming of data will be more efficient as well.
The AE signatures of the normal operation of the wind turbine blade are estimated in thiscase using representative 24h data. This data set was selected due to the fact that on the specifictimes all the different cases of variable wind velocity coexist. Therefore this can be considered as
the limit case data since the wind turbine goes from a complete stop to low and high RPM andthere is a significant wind velocity variation between low and high values. The previous figure
shows the different AE signatures from located AE data from the monitored areas of the blade in47

blue. In red, the HN simulated sources signatures are overlapped in order to enable an overall
comparison between the signatures of the different areas. It appears that the AE signature foreach area has a different and rather unique 2D cluster shape (Fig. 18).

Fig. 18. Signature graphs of AE features (in 2D feature space), in blue are the located AE eventsand in red the simulated HN sources. From left to right columns are the signatures of the spar,
trailing edge and root area.
Finally, from the analysis of a selected part of a large AE data volume, acquired from a permanently installed AE monitoring system on an operating wind turbine blade, promising results
were observed, based on the direct comparison and dissimilarities between simulated sources andlocated AE signal signatures on the monitored areas. The comparison is established on the basisof the degree of dissimilarity between three standard AE features. The correlation of these features usually provides an initial estimation about the origin of AE caused by different phenomena. The observations made can also be used for fine-tuning the real time response triggers of thesystem by setting up front end filters to issue alarms to any connected monitoring system. Additionally, the endurance and modularity of newer AE acquisition systems was successfully
demonstrated during actual daily operating conditions in the health monitoring of the NEGMICON NM48/750 blade. Future work includes the demonstration and modularity of the systemwith other types of wind generators, as well as other real time analysis capabilities or during postprocessing by using modern AE software suites.
Long-term acquisition until actual damage on the blade is developed and verified will enableevaluation of system’s capability for real-time early damage identification and warning. The
same may be, alternatively, achieved through installation of the system in already verified but
non-critically damaged blades (preferably similar to the ones already monitored).
Acknowledgements
The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7/2009-2013) under grant agreement no 239462.

48

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