NASA/TMw2001-210812

Neural-Net Processed Characteristic Patterns

for Measurement of Structural Integrity of

Pressure Cycled Components

A.J. Decker

Glenn Research Center, Cleveland, Ohio

March 2001
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NASA / TMm2001-210812

Neural-Net Processed Characteristic Patterns

for Measurement of Structural Integrity of

Pressure Cycled Components

A.J. Decker

Glenn Research Center, Cleveland, Ohio

Prepared for the

12th Nondestructive Evaluation Subcommittee, 21st Rocket Nozzle Technology

Subcommittee and 34th Structures and Mechanical Behavior Subcommittee Joint Meeting

sponsored by the Joint Army, Navy, NASA Air Force Interagency Propulsion Committee

Cocoa Beach, Florida, March 26-30, 2001

National Aeronautics and

Space Administration

Glenn Research Center

March 2001
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NEURAL-NET PROCESSED CHARACTERISTIC PATTERNS

FOR MEASUREMENT OF STRUCTURAL INTEGRITY

OF PRESSURE CYCLED COMPONENTS

A.J. Decker

National Aeronautics and Space Administration

Glenn Research Center

Cleveland, Ohio 44135

ABSTRACT

A neural-net inspection process has been combined with a bootstrap training procedure

and electronic holography to detect changes or damage in a pressure-cycled International Space

Station cold plate to be used for cooling instrumentation. The cold plate was excited to vibrate in

a normal mode at low amplitude, and the neural net was trained by example to flag small

changes in the mode shape. The NDE (non-destructive-evaluation) technique is straightforward

but in its infancy; its applications are ad-hoc and un-calibrated. Nevertheless previous research

has shown that the neural net can detect displacement changes to better than 1/100 the

maximum displacement amplitude. Development efforts that support the NDE technique are

mentioned briefly, followed by descriptions of electronic holography and neural-net processing.

The bootstrap training procedure and its application to detection of damage in a pressure-cycled

cold plate are discussed. Suggestions for calibrating and quantifying the NDE procedure are

presented.

INTRODUCTION

Artificial neural networks have been demonstrated at NASA Glenn Research Center for

some interesting potential applications to optical diagnostics of flows, structures and instrument

alignment, ls But the successful practical application of neural nets has been the detection of

small variations in images caused by structural changes or damage. 4That application used

neural nets in an attempt to detect pressure-cycle induced damage in an International Space

Station (ISS) instrumentation cold plate. The images used for NDE (non-destructive-evaluation)

were the characteristic patterns or mode shapes generated by electronic time-average

holography. Electronic time-average holography (sometime called television holography or

electronic speckle pattern interferometry with a smooth reference beam) is described in many

• • 5-7 pubhcat_ons and has been available commercially in one form or another for many years. For

electronic time-average holography, the structure for NDE is excited to vibrate with a siren or

shaker. The excitation may be broadband noise or at a single frequency to excite a single normal

mode of vibration, but the vibration amplitude is very small. The amplitude is measured in

wavelengths of visible light (typically 2x10 s in. or 500 nm). The simplest output image is a socalled Bessel fringe pattern that clearly displays the vibration mode shape as well as information

about the vibration amplitude distribution. A vibration mode shape at low frequencies depends

critically on boundary conditions and on the internal composition of the structure. The mode

shapes can be monitored visually in an attempt to detect structural changes, but our new neuralnet software add-on now allows them to be monitored automatically. The software uses artificial

neural networks to flag small, gradual variations that might remain undetected by the eye. The

current procedure requires only the recording of a few mode shapes generated experimentally

from the structure before other tests are performed. Hence the effects on a mode shape can be

monitored where the structural changes are induced by another test.

This paper is concerned mainly with the neural-net training procedure for NDE and

Distribution Statement -"Approved for public release, distribution is unlimited."

NASA/TM--2001-210812 1
approaches to calibrating that procedure. However, the paper begins in the next section by

presenting a brief background on projects to develop the software and hardware needed for the

procedure. Then electronic holography and neural-net processing are discussed in more detail,

followed by the training procedure itself, under Apparatus. Finally the application to the Space

Station Cold Plate is discussed followed by approaches to calibrating the NDE procedure.

ADDITIONALBACKGROUND

Development efforts encompass neural-net technology, structural and optical modeling,

optical instrumentation, calibration and consistency with NASA standards. Developments of

calibration and consistency with NASA standards 89are most relevant to the NDE approach

discussed in this paper. However, other development efforts are introduced briefly here for

completeness. Some background on the development of the neural-net software is first

presented, including a brief description of neural nets, structural and optical modeling and the

necessary optical instrumentation

NEURAL NET TECHNOLOGY

The most useful definition of an artificial neural network technology is any system whose

input-output response is instantiated with a training set of exemplars. 1°-11The inputs to our nets

consist of speckled Bessel fringe patterns generated from a vibrating structure. Figure 1 shows

the Bessel fringe pattern from a vibrating blade. Outputs at different times have included modelgenerated distributions of vibration displacement (modified by some optical effects) and modelgenerated distributions of surface strain (also modified by some optical effects). But the most

straightforward output is simply an indicator. The indicator flags significant changes in the Bessel

characteristic fringe pattern. Determining the sensitivity to change and level of significance is part

of the calibration problem. Using neural nets effectively requires the selection of an optimum

training set, the selection of a neural-net algorithm or architecture, and the conditioning of the

input data for optimum training of that architecture. The creation of optimum training sets is an

on-going research problem in statistical decision theory. 12"13A general objective is to create a

generally noisy training set of input-output pairs that will provide optimum interpolation and some

extrapolation regardless Oftlie neural-net architecture while training the net to ignore irrelevant

effects. Typically we actually use feed-forward nets trained with a version of the so-called backpropagation algodthm. We also use nets occasionally based on Adaptive Resonance Theory, but

feed-forward nets are the most compact in software. The feed-forward net is described briefly

below. Neural-net algorithms or architectures are discussed in books on neural networks. 1°1_'_4

Finally the format of the input data may need to be conditioned or altered for the particular neuralnet architecture. Conditioning will be discussed in a reference. _5

Figure l mBessel Characteristic Fringe Pattern.

STRUCTURAL AND OPTICAL MODELING

The easiest way to create training sets is from model-generated data using finite-element

models for the structures and optical models of electronic holography. The optical model is quite

effective, _6but sample-to-sample variations in structural mode shapes and the effects of

boundary conditions have made the use of finite-element models less effective. 3 A hope is that

NASA/TM--2001-210812 2 : :
the use of probabilistic finite-element models will ameliorate this situation somewhat. Modelgenerated modes however permit different neural-net strategies to be tested effectively.

OPTICAL INSTRUMENTATION

We are continuing to develop electronic holography to record the characteristic patterns

to be processed by the neural networks. As stated electronic holography is well developed for

applications in the NDE laboratory and is supported by commercially available equipment. The

challenge is to use electronic holography in the field for NDE of stationary and moving structures.

Fiberscopes can be used to record electronic holograms of structures in generally inaccessible

places. Figure 2 shows a mode shape of the vibrating fan blade depicted in fig. 1, but this time

recorded through a fiberscope. It can be seen that the pattern recorded through the fiberscope is

acceptable although closer examination would show some degradation. A more challenging

problem is to record electronic holograms of objects that are moving. Examples include objects

subjected to excessive environmental disturbances or the rotating components of a jet engine.

Pulsed lasers can be used to freeze the motion of the object. Another approach, which has been

tested at Glenn, is to use gated image intensifiers with continuous-wave lasers. Intensifiers

unfortunately are noisy and have low spatial resolution. Magnifying the holograms prior to their

amplification by the intensifier can be used to compensate for the low resolution, but reduces the

portion of the object that can be inspected at one time. Figure 3 shows a square inch of a 50 kHz

mode recorded from 200 microsecond exposures through a gated image intensifier showing that

intensifiers can be used for electronic holography. Exposure times as short as 20 microseconds

have been recorded. But exposures 2 orders of magnitude shorter (200 nanoseconds) are

required at the tips of rotating fan blades. That level of performance would be achievable with

frame-straddled double-exposure electronic holography implemented with a modern Nd:YAG

laser and a frame straddling CCD camera. Such electronic holography has been conducted with

an old-model laser with poor spatial matching of the double exposures, and the performance has

been poor.

Figure 2--Bessel Fringes

Through Fiberscope.

Figure 3---Bessel Fringes at 50 kHz

From Image-Intensified Holograms.

The hardware and software necessary to perform neural-net NDE with electronic

holography are now described. A measure of the sensitivity for damage detection is presented.

Then a straightforward procedure is described for acquiring and instantiating the training sets.

An application of that procedure to inspection of a pressure-cycled cold plate is discussed. The

need for, and direction of, calibration activities are then discussed.

NASA/TM--2001-210812 3
APPARATUS

ELECTRONIC HOLOGRAPHY

Electronic holograms are recorded with a CCD camera, or intensified CCD camera,

frame-grabber combination. The holograms are processed in a computer to generate the socalled characteristic patterns shown in figs. 1,2 and 3. A single hologram can be used to

visualize a vibration characteristic pattern, but the fringe contrast is poor. In practice, tens,

hundreds, or even thousands of holograms may be recorded to increase contrast, reduce

electronic noise, reduce speckle-effect noise and extract quantitative displacement data. Figure

3, using an intensified CCD camera, required averaging 100 characteristic patterns. But the

simplest contrast-increasing step only two holograms as shown below. Then the vibration

characteristic patterns can be sampled as much as 30 times per second. Hence, our neural-net

inspections generally have been performed with hologram pairs.

An electronic hologram is an interference pattern formed between an image of a laserilluminated object and a coaxial reference beam. The interference pattern is formed on the CCD

array or on the intensifier. It may be necessary to magnify or scale the interference pattern for a

low-resolution intensifier as was done for the mode in fig. 3. The instantaneous value of the

interference pattern for a structure vibrating in a normal mode is given by

I1+ 12Cos((p - 2_K=_)

where I1,12, and (I)are random variables associated with the laser speckle effect. However, _5is

the instantaneous displacement at a point on the vibrating structure. The vector K is called the

sensitivity vector and is a parameter of the illumination and imaging system.

If two identical holograms with identical speckle patterns are recorded at the same

displacement value, but shifted in phase by _, and if the holograms are subtracted, then only the

so-called cross-interference term

2 12Cos(cp - 2=K*6)

is the remainder. This step is the simplest contrast enhancement step in electronic holography.

In fact, the structure vibrates during the exposure. If the structure vibrates for one or more

complete cycles, then the cross-interference term is given by

13Jo(2_Ko_)

where 13is a random variable encoding the laser speckle effect and J0 is the zero-order Bessel

function of the first kind. Here _ is the displacement amplitude. The absolute value of this Bessel

fringe pattern is displayed in figs. 1 and 2. Figure 3 in fact averages 100 such patterns.

In a laboratory, electronic holography can be performed with an ordinary interlaced TV

camera. The pair of holograms is recorded in adjacent frames of the camera, and the phase is

shifted by _ between frames. This technique needs to be modified outside the laboratory

at least to minimize the effects of environmental disturbances. Then so-called frame straddling

is employed. As a first step, the exposure time needed to record the hologram is minimized. (For

example, the shortest time to time-average al000 Hz mode is 1 millisecond.) In frame straddling,

one hologram is recorded at the very end of a frame and the second hologram of the pair is

recorded at the very beginning of the next frame. The inter-frame time is less than 1.0

microsecond for some cameras; thereby the time between holograms for extraneous

environmental disturbances is minimized. A second application of frame straddling is doubleexposure holography performed with a double-pulse laser. Double-exposure holography can be

NASA/TM--2001-2108I 2 4
performed without phase shifting, if the structure moves between the two exposures. Subtracting

the two holograms yields a characteristic pattern given by

12Cos(_p- 2_K.52) - 12Cos(_ - 2=K-51)

where a point on the vibrating structure is at different displacements for the exposures. An

interlaced camera can be used for frame straddling, but adjacent video fields must be subtracted

resulting in a significant reduction in quality. Frame transfer cameras have been developed for

frame straddling in particle velocimetry. One discussion of the frame straddling technique in

particle velocimetry is given in a reference. 17

The remainder of this paper assumes Bessel characteristic patterns. The assumption is

that at least one complete vibration cycle is recorded in each frame.

NEURAL -NET PROCESSING

A neural-net application as stated is defined by a training set of input-output exemplars.

The training set is used to train an architecture or algorithm embedded in software (or hardware).

The simplest example is to use characteristic patterns such as shown in figs. 1, 2 and 3 to train a

neural net to distinguish un-cracked specimens from those that develop a particular kind of crack.

This example has been studied using simulations and then tested on actual structures. 18-19

These simulations produce estimates of the sensitivity of the NDE method discussed herein. The

simulations in the references used finite element models and a model of the electronic

holography process to calculate vibration characteristic patterns for cracked and un-cracked

versions of cantilevers and fan blades. The minimum detectable difference in the displacement

distribution between the cracked and the un-cracked structures was then determined. In fact,

electronic holography estimates the distribution of the quantity K-_. But the vectors K and _ are

often nearly parallel thereby yielding the distribution of 5. Cases where K varies significantly are

harder to handle. Then one must arrange the most favorable view of the region most likely to be

damaged.

The neural nets developed for the referenced studies have been able to detect changes

in the characteristic patterns corresponding to displacement amplitude changes smaller than 1

percent of the maximum displacement amplitude. It is to be noted that a crack at a single location

affects the characteristic pattern over much of the structure. Hence the damage is communicated

to the net at several pixels and therefore at several inputs.

Briefly, the following specific properties defined the neural-net training sets and the neural

nets used for the referenced studies. First, the resolutions of the characteristic-pattern inputs

were reduced to the extent that the number of pixels equaled the number of finite elements in the

simulations. Figure 4 shows a finite-element-resolution characteristic pattern of a blade vibrating

in its first mode. This pattern involved some conditioning, is The model depicted here used 903

finite elements. One advantage of the lower resolution is that the CCD and computer

combination responded at 30 frames per second. Second, the training sets contained

independent examples of the speckle pattern. Previous research has shown that the number of

independent speckle patterns must constitute about 10 percent of the number of pixels which

equals the number of finite elements. 16About 100 patterns are required for the 903 finite

elements. The output most simply consisted of 2 or 3 classification nodes. The simplest case

contains the cracked or un-cracked classes. The third case adds an unrecognized class where

the patterns represent neither the cracked or un-cracked training examples. The third case might

represent conditions where the vibration amplitude is out of range.

NASAYrM---2001-210812 5
Figure 4---Finite-Element-Model Pattern

at Finite-Element Resolution.

The neural-net architecture which is most compact in software is the feedforward net

depicted in fig. 5. The nodes or neurons in the net sum their inputs, transform the sums

nonlinearly, and fan the outputs to nodes in the next layer. Training is accomplished by various

versions of the so-called back propagation algorithm, la11 There are three objectives for a net. It

should learn the training set; it should generalize by handling legitimate cases not found in the

training set; and it should learn quickly. The neural-net technology used for NDE satisfies these

criteria. The details are part of the neural-net-technology aspect of this work and are discussed in

the references.

Figure 5---Diagram of Feedforward Neural Network.

The training procedure used for NDE of the ISS cold plate differs in some details from the

procedures used to develop the neural-net technology and to test for sensitivity. That procedure

is described in the next section.

TRAINING FOR NDE

The training procedure is a bootstrap procedure and strictly ad-hoc at this stage of

development. Four experimentally visualized vibration modes and 4 associated characteristic

patterns were selected. One of these pattems was the un-excited pattern, where only

environmental effects were present. One of the remaining patterns was selected as the pattern to

be monitored for structural changes. The other two vibration patterns as well as the un-excited

pattern were selected as examples of significantly changed patterns. The pattern to be monitored

for changes was assigned a particular output coding. All the other patterns were assigned

another code. A code is contained in 2 or 3 output nodes. For training, a node is assigned the

value 0.2 or 0.8 (the Va:!_UgSdi_!er from 0 and 1 because of the normalization requirements of the

net architecture). The only other requirement for adequate training is that each mode has enough

independent speckle patterns to impart speckle pattern immunity. Independent speckle patterns

are easy to generate; since the net requires fewer pixels than recorded by the camera. Each

NASA/TM--2001-210812 6
pixel retained is considered to be a large pixel containing many of the smaller camera pixels.

One small pixel is selected at random for each large pixel to generate a sample pattern. The

process is continued until enough samples have been recorded for each mode. As stated

previously, the required number of samples is about 10 percent of the number of large pixels.

Calculating the correlation coefficients between samples of the same mode is used to check the

effectiveness of the process.

Executing the training process for a feedforward net consists of minimizing the rms error

between the training outputs and the net-generated outputs. A typical rms error is 0.01. The

output values generated by the net are not exactly 0.2 or 0.8, and some tolerance must be

assigned.

The procedure described so far has been a formula procedure: Select 4 modes; select

one mode to be monitored; assign a code to that mode and a code to the remaining 3 modes;

record enough uncorrelated speckle patterns for each mode; and train the neural net.

Unfortunately the selection of the modes, the accompanying adjustment of sensitivity and the

selection of neural-net tolerances are not formula procedures at this time. This ad-hoc nature of

the bootstrap procedure is discussed next.

First, different sets of 4 training-modes-per-set do not perform the same for showing a

particular structural effect. Boundary conditions, affected for example by setting mounting-bolt

torques, always have a very significant effect on the neural-net response for lower frequency

modes. But maximizing the response of the net for changes at a particular location on a structure

is a trial-and-error procedure. Currently that location is perturbed in some way (by application of

a light point load for example) to test sensitivity. The mode mixture is adjusted for best sensitivity.

The net needs to be trained and tested for each candidate set of modes to test for best sensitivity.

Second, the output of the neural net must be interpreted. Thresholds must be set. The

output of a feed forward net does not and should not change abruptly as the inputs differ from the

training set. Environmental disturbances such as air currents and extraneous vibrations have an

effect that must be discounted in setting the threshold. An advantage of the feedforward net is its

insensitivity to optical effects such as contrast changes and brightness changes. But there is

some degradation of the outputs in response to these optical effects. Graceful degradation is the

objective. In the absence of a statistically correct methodology for setting thresholds, the criterion

has been arbitrary. Typically, outputs larger than 0.65 or 0.7 are declared to differ insignificantly

from the training output 0.8. Another criterion is that high outputs in the 2-or-3-node codes for

two different classes differ by at least 0.2, although that distinction has not been required in

practice.

A result of using the existing ad-hoc NDE procedure is discussed next followed by a

discussion of approaches to calibrating the technique.

RESULTS AND DISCUSSION

USE OF NDE PROCEDURE FOR A COLD PLATE TEST

The ad-hoc NDE procedure is exemplified by a discussion of its application to a test of an

ISS instrumentation cold plate. 4 A cold plate was bolted to a frame through several of the holes

intended for mounting instrumentation to the cold plate. Bolt torque was quite critical for this test;

since modes shapes are very sensitive to the boundary conditions. The objective was to

pressure cycle the plate, with its cooling passages filled with water, to test for structural integrity.

Neural-net-processed electronic holography was used to inspect the plate for structural changes

before, during and after pressure cycling. Both the entire plate and a suspicious zoomed region

between 4 bolt holes were tested at different times. Different mode mixtures were used for the

two tests. A total test procedure, which employed both classical silver-halide-emulsion

holography and electronic holography, is discussed in the reference. Only the application of

NASA/TM_2001-210812 7
neural-net-processed electronic holography between the 4 bolt holes is discussed as an example

of the NDE procedure.

The test was conducted in a laboratory on a vibration-isolation table. Electronic

holography does not require the full vibration-isolation of the table, but silver-halide-emulsion

holography does. The bolt holes were on 4 in. (10 cm.) centers. A zoomed electronic holography

test was performed on a suspicious region (visualized previously with an ultrasonic C-scan)

between 4 holes. The zoomed test actually followed an inspection of the entire plate performed

during 2000 pressure cycles. The 4 steps of the application to the cold plate of the general adhoc procedure are stated specifically.

First 4 training modes were selected. The large-pixel zero-amplitude pattern is shown in

fig. 6, where the neural-net input consisted of 40x54 large pixels. The 3 vibration modes had

frequencies of 23t0 Hz, 780 Hz and 1597 Hz and are shown at full resolution in fig. 7 along with 2

additional modes that were also evaluated in the trial-and-error procedure.

Figure 6_Large-Pixel Zero-Amplitude Pattern of Cold-Plate Segment.

Figure 7mModes that Contain Cold-Plate Training Mixture.

Second trial-and-error training indicated that the mode at 2310 Hz was most sensitive to

perturbations, and that mode was selected for inspection. The zero-amplitude pattern and the

modes at 780 Hz and 1597 Hz were selected as training examples of changed patterns.

According to the 10-percent formula mentioned previously, 216 independent speckle patterns

were acquired for each mode. Software automatically directed the acquisition of these speckle

patterns.

Third, the neural-net outputs corresponding to the modes were encoded and interpreted

as stated above. The output of the net was immediately converted by software into colored largepixel characteristic patterns to make visualization easier. Patterns measured as unchanged from

the training pattern at 2310 Hz were colored green. Otherwise the patterns were colored yellow.

Fourth, the net was trained and used for inspection. Training to a rms error of 0.01

occurs in a few minutes.

NASA/TM--2001-210812 8
The execution of these four steps and the use of the trained net for inspection constituted

the ad-hoc NDE procedure. The cold plate was pressure-cycled 1000 times during the

application of the procedure and simultaneously excited to vibrate at 2310 Hz. Pressure cycling

proceeded at 0.2 Hz, and the plate was continuously monitored with the holography, neural-net

combination. The test was also interrupted every 250 cycles to record 20 neural-net-processed

characteristic patterns at the pressure-relaxed condition. The test showed no neural-net

detectable changes in the characteristic pattern at the pressure-relaxed condition. However, the

net did flag the mode shape of the pressurized plate as different. Figure 8 shows the large pixel

output of the holography, neural-net combination for the relaxed and pressurized conditions.

Figure 8 can be compared with fig. 7 that shows the full-resolution characteristic pattern. As can

be seen, the full-resolution pattern extends outside the region that was inspected shown in fig. 8.

Figure 8_Net-Colorized Characteristic Patterns for

Relaxed and Pressurized Cold-Plate Segments.

The neural net detected no significant changes in the characteristic pattern during or after

3000 pressure cycles to 120 psig (827 kPa). The 20-pattern sets recorded of the region between

the 4 bolts showed no indication of a change. Some of the patterns recorded of the whole cold

plate were flagged as changed, but these changes were associated with a period of instability of

some of the electronic accessories the holography setup. That instability caused a large decrease

in contrast of some of the characteristic patterns.

CALIBRATION AND QUANTIFICATION OF THE PROCEDURE

So far the NDE procedure has been straightforward but only semi-quantitative. That is,

only simulations have proven that pattern changes equivalent to displacement-amplitude changes

of 1 percent or less of the maximum displacement amplitude are detected. These simulations

have involved fan blades rather than complex structures like the cold plates. Three approaches

have been proposed for full quantification of the NDE procedure.

The first approach uses deterministic finite-element modeling for the structure to be

tested. The patterns predicted from deterministic finite-element models have been found to be

generally not accurate enough to train a neural net for inspections of an actual sample of a

physical structure. 3 But the modes predicted may be accurate enough to test the net for

sensitivity as a function of training-mode mixture, if the structural damage can also be modeled or

if the displacement distribution can be perturbed by known amounts. This approach was used to

evaluate neural-net sensitivity for detecting blade cracks and is the source of the 1 percent

estimate of sensitivity.

The second approach would use probabilistic finite-element modeling techniques. Then

a range of patterns would be generated including some patterns that were "out-of-range". The

net would be trained to ignore normal structural variations and to flag out-of-range patterns that

might indicate structural damage. Stochastic variations in the environment and electronic

holography can also be added to create a neural-net detection of a limit state.

The third approach would attach the new neural-net, holography NDE to existing vibration

testing procedures °.° making it perhaps more acceptable to the NDE community. Out-of-range

conditions would be specified with displacement, strain or force gages. The neural-net NDE

would be performed periodically during the normal test procedures. This approach has the

advantage that the calibration or quantification procedure would be developed in cooperation with

personnel who normally do the vibration testing.

NASA/TM--2001-210812 9
The second and third approaches have been included in proposals directed to NASA

project offices.

SUMMARY AND CONCLUSIONS

A NDE procedure has been developed at Glenn to use artificial neural networks to detect

structural damage or structural changes from the characteristic patterns or mode shapes

recorded using electronic holography. The electronic holograms have been recorded using CCD

and intensified-CCD technology directly and through fiberscopes. The electronic-holography

visualization technique is sensitive to the vibration-displacement distribution of the structure. The

procedure is a simple 4-step procedure, but so far is un-calibrated. Nevertheless, the

procedure's sensitivity has been estimated by using finite-element-model simulations and

laboratory tests. The procedure has been applied to inspection of a ISS cold plate for pressurecycle-induced damage or changes. The cold plate passed the test in that significant damage or

changes were not detected.

Perhaps the major conclusion is that artificial neural nets definitely can be used to

automate the sometimes-tedious inspection of structural images for changes.

The 4-step procedure is easy to apply and permits the performance of whole- surface

inspections for structural damage or changes. The inspections can be performed in real time (30

frames per second using standard video cameras). But the procedure is ad-hoc. Four vibration

modes must be selected for training the neural net and the sensitivity must be adjusted by varying

the mode selection. That sensitivity adjustment is currently a matter of judgment based on the

characteristics of the sample. Hence efforts have been proposed to calibrate or quantify the

procedure. These efforts will apply: finite element models, probabilistic finite element models, or

correlations with sensor readings used in implementing existing NASA standards.

REFERENCES

1. A.J. Decker and A.E. Buggele, "Automation of Some Operations of a Wind Tunnel

Using Artificial Neural Networks," AIAA J. 34, pp. 421-423, 1996.

2. A.J. Decker, "Self-Aligning Optical Measurement Systems," Applied Optics 31,

pp. 433-4340, 1992,

3. A.J. Decker, "Neural-Net Processing of Characteristic Patterns," Topics on

Nondestructive Evaluation Series, Volume 4, Automation, Miniature Robotics and Sensors for

Nondestructive Evaluation and Testing, Yoseph Bar-Cohen, Technical Editor (The American

Society for Nondestructive Testing, Inc., 2000), Chapter 7.1.

4. A.J, Decker, M.E. Melis and K.E. Weiland, "Inspection of Space Station Cold Plate

Using Visual and Automated Holographic Techniques," NASA/TM--1999-209388, Aug. 1999.

5. K.A. Stetson and W.R. Brohinsky, "Fringe-Shifting Technique for Numerical Analysis of

Time-Average Holograms of Vibrating Objects," Journal of the Optical Society of America A5.

pp. 1472-1476, 1988.

6. S. Nakadate, 'Vibration Measurement Using Phase-Shifting Speckle-Pattern

Interferometry," Applied Optics 25, pp. 4162-4167,1986.

7. K.A. Stetson and W.R. Brohinsky, "Electro-Optic Holography System for Vibration

Analysis and Nondestructive Testing," Optical Engineering 26, pp. 1234-1239, 1987.

8. "Dynamic Environmental Criteria," NASA-HDBK-7005, 2000.

9. "Payload Vibroacoustic Test Criteria," NASA-STD-7001, Jun. 1996.

10. Yoh-Han Pao, Adaptive Pattem Recognition and Neural Networks. Addison-Wesley,

New York, 1989.

11. B.D. Ripley, Pattern Recognition and Neural Networks. Cambridge University Press,

New York, i996.

12. M.T. Manry, et al., "Cramer Rao Maximum A-Posteriori Bounds on Neural Network

Training Error for Non-Gaussian Signals and Parameters," International Joumal of Intelligent

Control and Systems 1, pp. 381-391, 1996.

NASA/TM--2001-210812 10
13. M.T. Manry, S.J. Apollo and Q. Yu, "Minimum Mean Square Estimation and Neural

Networks," Neurocomputing 13, pp. 59-74, 1996.

14. D.E. Rumelhart, et al., PARALLEL DISTRIBUTED PROCESSING, Explorations in the

Microstructure of Cognition, Volume I: Foundations. MIT Press, Cambridge, 1988.

15. A.J. Decker, "Optimization of Training Sets for Neural-Net Processing of Characteristic

Patterns from Vibrating Solids," to be published in SPIE Proceedings.

16. A.J. Decker, E.B. Fire, Oral Mehmed and S.A. Thorp, "Processing Speckle Patterns

With Model Trained Neural Networks," Optical Technology in Fluid, Thermal, and Combustion

Flow III, Proc. SPIE 3172, pp. 285-293, 1997.

17. M.P. Wernet, "Particle Displacement Tracking Applied to Air Flows,"

NASA TM-I04481, Aug. 1991.

18. A.J. Decker, E.B. Fire, O. Mehmed and S.A. Thorp: "Vibrational Analysis of Engine

Components Using Neural-Net Processing and electronic Holography," Advanced Non-Intrusive

Instrumentation for Propulsion Engines, AGARD-CP-598, pp. 33-1-33-6, May 1998.

19. A.J. Decker, E.B. Fire, S.A. Thorp. and O. Mehmed "Comparison of computationalmodel and experimental-example trained neural networks for processing speckled fringe

patterns," Intemational Conference on Optical Technology and Image Processing Fluid, Thermal

and Combustion Flow, Proceedings of VSJ-SPIE '98, on compact disc in pdf, paper AB002,

December 1998.

NASA/TM--2001-210812 11
REPORT DOCUMENTATION PAGE FormApproved

OMBNO.0704-0188

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1. AGENCY USE ONLY (Leave b/ank) 2. REPORT DATE 3. REPORT TYPE AND DATES COVERED

March 2001 Technical Memorandum

4. TITLE AND SUBTITLE

Neural-Net Processed Characteristic Patterns for Measurement of Structural

Integrity of Pressure Cycled Components

6. AUTHOR(S)

A.J. Decker

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

National Aeronautics and Space Administration

John H. Glenn Research Center at Lewis Field

Cleveland, Ohio 44135-3191

9. SPONSORING/MONITORiNG AGENCY NAME(S) AND ADDRESS(ES)

National Aeronautics and Space Administration

Washington, DC 20546- 0001

15. FUNDING NUMBERS

WU-323-78-00--00

8. PERFORMING ORGANIZATION

REPORTNUMBER

E-12727

10. SPONSORING/MONITORING

AGENCY REPORT NUMBER

NASA TM--2001-210812

11. SUPPLEMENTARY NOTES

Prepared for the 12th Nondestructive Evaluation Subcommittee, 21 st Rocket Nozzle Technology Subcommittee and

34th Structures and Mechanical Behavior Subcommittee Joint Meeting, sponsored by the Joint Army, Navy, NASA Air

Force Interagency Propulsion Committee, Cocoa Beach, Florida, March 26-30, 2001. Reponsible person, A.J. Decker,

organization code 5520, 216--433-3639.

12a. DISTRIBUTION/AVAILABILITY STATEMENT

Unclassified- Unlimited

Subject Categories: 35 and 38 Distribution: Nonstandard

Available electronically at htto://-_ltrs.m'c.nasa._ov/GLTRS

This _aublication is available from the NASA Center for AeroSpace Information, 301--621-0390.

12b. DISTRIBUTION CODE

13. ABSTRACT (Maximum 200 words)

A neural-net inspection process has been combined with a bootstrap training procedure and electronic holography to

detect changes or damage in a pressure-cycled International Space Station cold plate to be used for cooling instrumentation. The cold plate was excited to vibrate in a normal mode at low amplitude, and the neural net was trained by example

to flag small changes in the mode shape. The N-DE (nondestructive-evaluation) technique is straightforward but in its

infancy; its applications are adhoc and uncalibrated. Nevertheless previous research has shown that the neural net can

detect displacement changes to better than 1/100 the maximum displacement amplitude. Development efforts that support

the NDE technique are mentioned briefly, followed by descriptions of electronic holography and neura]-net processing.

The bootstrap training procedure and its application to detection of damage in a pressure-cycled cold plate are discussed.

Suggestions for calibrating and quantifying the NDE procedure are presented.

14. SUBJECT TERMS

Neural networks; Nondestructive evaluation; Electronic holography

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