Arthur J. Decker

Glenn Research Center, Cleveland, Ohio

Damage Detection Using Holography

and Interferometry

NASA/TM—2003-212078

January 2003

https://ntrs.nasa.gov/search.jsp?R=20030014823 2018-05-15T15:39:59+00:00Z
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Arthur J. Decker

Glenn Research Center, Cleveland, Ohio

Damage Detection Using Holography

and Interferometry

NASA/TM—2003-212078

January 2003

National Aeronautics and

Space Administration

Glenn Research Center
Acknowledgments

I would like to thank Mr. Kenneth E. Weiland for providing invaluable technical assistance over the years

and for preparing several figures.

Available from

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Available electronically at http://gltrs.grc.nasa.gov
NASA/TM—2003-212078 1

 

DAMAGE DETECTION USING HOLOGRAPHY 

AND INTERFEROMETRY 

Arthur J. Decker 

National Aeronautics and Space Administration 

 Glenn Research Center 

Cleveland, Ohio 44135

1. INTRODUCTION 

Some major goals of structural testing are the detection of local damage 

and, more recently, the general evaluation of structural health. Detection of 

local damage calls to mind several specialized practices. The most direct is 

simple visual inspection for cracks or visual inspection perhaps enhanced by 

dye penetration. This simplest of approaches is augmented by a variety of 

visualization techniques. These include,1

 for example, X-ray radiography; 

neutron radiography; ultrasonic techniques including C-scan imaging; 

optical, electron and scanning probe microscopy; eddy currents; both optical 

and non-optical thermography and the interferometric methods to be 

discussed in this chapter. 

The evaluation of the general health of structures depends on the 

detection and possibly sophisticated analysis of non-locally generated 

signatures. A very simple example to be discussed in this chapter is the 

measurement of the change in frequency of a resonant vibration mode of a 

structure that sometimes accompanies damage or cracking. A more 

sophisticated method is the detection of subtle changes in the vibration mode 

shape itself. 

The minimum objective for all these technologies is to detect possibly 

injurious structural changes or structural damage. A more advanced 

objective is to quantify the magnitude of the structural changes or damage. 
NASA/TM—2003-212078 2

In any case, the technologies often have little in common except for the 

objectives. This chapter will discuss specific optical techniques based on 

interferometry and holography. But the approach to be discussed for using 

neural networks to interpret interference patterns should be applicable to 

processing multiple channels of data from some of the other technologies. 

2. SURVEY OF HOLOGRAPHY AND 

INTERFEROMETRY FOR DAMAGE 

DETECTION 

2.1 Principle of Operation 

Laser holography and interferometry have been used to detect structural 

damage nearly from the time of their discovery in the early 1960’s. The 

approach to damage detection has not changed much since that time. An 

interference pattern or “fringe pattern” is created and monitored for the 

object of interest. Visual clues about changes in the structural integrity of the 

object are sought from variations in the interference pattern. Silver-halideemulsion holograms were used extensively for these inspections during the 

first twenty-five years of laser holography. Real-time, double-exposure and 

time-average holographic interferometry, as well as speckle interferometry, 

were used to generate the interference patterns.2 The important feature of the 

silver-halide emulsion was that it could store multiple holograms or speckle 

photographs of the surface of an object recorded at different times.3

 The 

reconstructions from the different holographic snapshots of the time varying 

surface could then be accomplished at the same time. The reconstructed 

beams interfered, and non-uniformities or anomalies in the interference 

patterns were identified with damage. Holographic techniques are primarily 

sensitive to out-of-plane motions of the surface of an object, whereas speckle 

techniques are primarily sensitive to in-plane motions. Another optical 

method called moiré deflectometry could be used to detect local changes in 

the orientation of the surface of an object.4 

The silver-halide emulsion has largely been replaced with electronic 

recording in the past fifteen years.5

 Combination of the CCD camera with 

phase shifting interferometry offers most of the practical damage-inspection 

benefits of silver-halide-emulsion holography plus the ease of digital data 

acquisition and processing. The CCD camera has sufficient resolution to 

record image-plane holograms whose maximum spatial frequencies are set 

by low-pass filtering property of the camera lens and the accompanying 
NASA/TM—2003-212078 3

pixel size. Unrestricted three-dimensional viewing still requires the silverhalide emulsion, but stereoscopic viewing can capture two perspectives from 

two electronic cameras. Electronic recordings of holograms and speckle 

patterns permit all the sophisticated tools of image processing and 

computing to be applied. But the user is still confronted with the challenges 

of detecting changes in fringe patterns and associating them with structural 

changes and damage. 

2.2 Classical Approaches 

Both early and more recent demonstrations of damage detection have 

often depended on rather dramatic structural failures such as severe cracks 

and de-bonding to generate spectacular local variations in the interference 

patterns. Figure 1 shows the rather obvious effect on a double-exposure 

fringe pattern of a de-bond defect in a flat-plate structure. Double exposures 

have been used dramatically to show separations in automobile tires,6

 for 

instance. Various enhancements have been used to visualize less dramatic 

damage. One approach uses continuous phase shifting of the fringe pattern, 

and the accompanying local time variation in contrast, to call attention to a 

crack. Another approach is to visualize measured or computed patterns that 

map differences or derivatives of displacement rather than the surface 

displacements directly.7 

Figure 1. Defect shown by double-exposure fringe pattern 

Figure 2 shows a fringe pattern from a time-average hologram of a 

simple mode of a cantilever, and the corresponding maps of the second 

derivatives of the interference phases for cracked and undamaged versions of 

that cantilever. The patterns in figure 2(b) and (c) were generated by an 

artificial neural network in response to the mode shape in figure 2(a). The 

second derivative mapped in figure 2(c) clearly shows the effect of a crack. 
NASA/TM—2003-212078 4

The bending induced strain at the surface can, in fact, be calculated from the 

second spatial derivatives of displacement evaluated in the local tangent 

planes at the surface.8

 The measured interference phase really maps the dot 

product of displacement and a sensitivity vector, thereby complicating the 

interpretation somewhat. But the sensitivity vector often varies slowly 

enough that the second derivatives approximately map bending induced 

strain. Regardless, the second derivatives of interference phase sometimes 

provide a way to visualize damage. 

Optical inspections of solid surfaces for structural damage suffer from 

two major defects. First, inspections depend on human interpretation of 

visualizations and are observer dependent. A more serious defect is that 

optical patterns may not change dramatically in the presence of significant 

structural damage or changes. 

a b c 

Figure 2. Fringe pattern (a) and second derivatives of interference phase 

from undamaged (b) and cracked (c) cantilevers. 

As stated above, most demonstrations of holographic and interferometric 

techniques have depended on dramatic changes such as manifested by cracks 

and de-bonding. But structures can change substantially as measured by nonoptical methods without affecting the interference patterns recorded by using 

electronic holography. For example, a flat plate held on two sides by visegrips was vibrated in its fifth mode of bending, and electronic holography 

recorded the interference pattern of this mode (fig. 3). Each vise-grip was 

attached with four screws. The torque of one of these screws was adjusted 

back and forth from 0 to 70 inch-pounds, and the resonant frequency was 

recorded at 6 torque settings, with 10 measurements per setting. 

Figure 4 shows that the resonant frequency of this vibration mode clearly 

changes with torque. But a visual examination of the associated vibration 

mode shapes shows no discernable variation. Two of these patterns are shown 

in figure 5. In other cases, mode shapes change when frequencies do not. 
NASA/TM—2003-212078 5

Figure 3. Fifth vibration mode of optical strain-gauge plate 

0 25 35 45 55 70

466

468

470

472

474

Simultaneous Comparisons Between Groups 

F

r

e

q

H

z

TORQUE (in lb)

Group Means 

Grand Average 

Error bounds for

Grand Average.

Figure 4. Holography-measured decrease in resonant frequency with increasing screw torque 

Figure 5. Mode at screw torques of 25 and 70 in. lb 
NASA/TM—2003-212078 6

This example shows both the strengths and weaknesses of using wholefield interferometry for damage detection. Electronic time-average holography 

is easy to use to identify a vibration mode and its resonant frequency.10 A 

change-in-frequency is a commonly used signature of structural changes and 

damage. Resonant frequencies and modal identification are important design 

parameters for components such as fan blades. But the interferometervisualized mode shape of a component of a structure is not necessarily a 

reliable signature of structural health. A crack, de-bond, or other defect 

appearing in the region being visualized may indeed display a dramatic local 

effect on the fringe pattern or its derivatives as exemplified by figures 1 and 2. 

Or it may not appear to change the pattern at all. 

2.3 Whole-Field Approaches 

Fortunately, electronic recording and computing enable another approach 

to damage detection from whole-field interference patterns. The entire 

pattern can be compared somehow with a pattern from the undamaged 

component11–13 or even with a design-model-generated pattern.14–16 This 

approach does not depend on the presence of dramatic local structural effects 

such as cracks. A slight change in mode shape caused by a remote event, 

such as the change in screw torques associated with figures 3 through 5, can 

be detected. 

Artificial neural networks are one tool for parallel processing of wholefield interference patterns. A neural net can be trained to respond to very 

small changes in the total interference pattern. The simplest approach 

involves electronic time-average holograms. There is a technique for training 

neural networks to respond to very small changes in the shape of a vibration 

mode where the vibration mode is visualized using electronic holography. 

The technique assumes nothing about the nature or cause of the change in 

mode shape and uses modes of the undamaged structure for training. 

Neural nets are used to automate the damage detection process and do 

not require visualization enhancements for human intervention. In that sense, 

they are consistent with the expectations for machine vision. The neural-net 

inspection technique is discussed in some detail next. 
NASA/TM—2003-212078 7

3. NEURAL-NET DETECTION OF DAMAGE 

3.1 Neural-Net Essentials 

Artificial neural networks were rediscovered and started to assume some 

popularity about 15 to 20 years ago.17 It turns out that it is not especially 

straightforward to get good computing performance from neural networks. 

In fact it is not particularly easy to define an artificial neural network; since 

the number of different neural-net architectures is large. The principal 

requirements for getting good performance are to understand the intended 

application thoroughly and to select an appropriate architecture. Even then 

there is a confusing array of competing parallel processing methods with 

names such as fuzzy logic, expert systems and data mining. A single neuralnet architecture might have several training procedures. 

Fortunately certain kinds of artificial neural networks handle the patterns 

generated from electronic holograms very well and can be trained to detect 

damage from these patterns. The so-called feed-forward artificial neural 

networks are especially well suited for training for damage detection from 

mode-shape patterns such as shown in figure 3. The feed-forward neural net 

embodies most of the key concepts of an elementary neural network; can be 

trained by executing a formula procedure such as the back- propagation 

technique;18 and tolerates the laser speckle effect exhibited by electronic 

holograms of diffusely reflecting structures. In fact the feed-forward net 

handles noise very well and conceivably could be trained to distinguish 

damage from random, but normal, structure-to-structure variations. The feedforward net is not the only architecture for interpreting fringe patterns, but 

actually is quite compact in software when compared with other architectures. 

The feed-forward net is completely adequate for the damage-detection 

application and is discussed exclusively in the following paragraphs. 

Figure 6 shows schematically a feed-forward net, sometimes called a 

multi-layer perceptron for historical reasons. The architecture consists of 

processing nodes or neurons represented by circles. The nodes are arranged in 

layers. Most often information is transmitted in one direction only from left to 

right; hence, the net is called a feed-forward net. The nodes in one layer are 

connected to the nodes in the previous layer via weighted connections. These 

weighted connections are sometimes identified loosely with biological 

synapses. Typically the weighted inputs are summed and then transformed 

with a non-linear transfer function. Sigmoid and hyperbolic tangent functions 

are typically used for the transformation. The transformed output is then 

transmitted to the next layer of nodes for similar processing. The single output 

of a node is sometimes loosely identified with a biological axon. 
NASA/TM—2003-212078 8

Figure 6. Example of feed-forward neural network 

The nodes in the leftmost layer in figure 6 have single inputs to receive, 

for example, the pixel values from a fringe pattern. The outputs of the 

rightmost nodes are the outputs of the neural network. The layers in between 

are sometimes called hidden layers. A single hidden layer is most common. 

Occasionally a net will perform better with two hidden layers. The neural net 

can be trained to respond to specific inputs by outputting specific outputs. A 

set of records consisting of input-output pairs is used to train the neural 

network. Training is accomplished by adjusting the weights of the weighted 

connections to the nodes. One training criterion is that the sum of the squares 

of the differences between the actual neural-net outputs and the training 

outputs be minimized for all the training records. Training typically occurs 

in steps. A training step may involve all, a subset, or one of the training 

records. The best-known procedure is the so-called back-propagation 

algorithm. That procedure is essentially a steepest descent search in weight 

space to minimize the sum-of-squares mentioned above. 

There is a vast literature on the details and vagaries of the various neuralnet architectures and training procedures. Ripley presents an exhaustive 

treatment of the mathematical subtleties.19 Unfortunately it is possible to 

read much of that literature without learning how to use nets to handle 

particular kinds of data. Fortunately there are free and commercial software 

packages for creating and training neural networks20 and ample opportunity 

to discover by experience which procedures work. The next section 

discusses the essentials for using the feed-forward net to recognize fringe 

patterns. 
NASA/TM—2003-212078 9

3.2 Design of Neural Nets and Training Sets for Damage 

Detection 

The rules for designing both neural nets and training sets for fringepattern recognition were evolved originally from computer experiments. The 

experiments were conducted using model-generated fringe patterns that 

included a model-generated speckle effect.14–16 The modeled object was 

initially a simple ideal cantilever, and the displacement distributions of the 

vibration modes were computed from a finite-element model. Modes were 

generated for cracked and undamaged versions of the cantilever, and 

multiple uncorrelated speckle patterns were calculated for each mode. The 

early experiments actually used as outputs the computed second derivatives 

of the model-generated dot product of displacement and the interferometric 

sensitivity vector. This choice of output specifically recognized that bending 

induced surface strain is proportional to the local second derivatives of 

displacement.8

 The latest examples simply use a mode-identifying index 

called the degradable classification index (DCI) as the neural net output. 

In any case, the current objective is that a neural net be trained to 

recognize a vibration mode of an undamaged structure in the presence of the 

laser speckle effect, and that the net respond to any changes in the mode 

shape. A property of neural networks called graceful degradation makes 

them useful for characterizing changes in the mode shape. Specifically, the 

proper design of the net and the training set will lead to a gradual and 

possibly controllable change in the DCI as the mode shape changes. Mode 

shapes21 depend on boundary conditions, distributions of material properties, 

excitation levels and damage. 

The proper design techniques were deduced from the model-generated 

data using statistical analysis. Figure 7 shows some examples of measured 

and model-generated data from the first chord-wise mode or lyre mode of a 

flat blade. Figure 7(a) was photographed from the reconstruction from an old 

silver-halide time-average hologram of a vibrating blade. Figure 7(b) was 

obtained from electronic holograms of a flat blade. Note the reduced spatial 

resolution caused by the relatively large camera pixels. Figure 7(c) was 

calculated from a finite element model of the flat blade and a model of the 

laser speckle effect. Note that the mode shape or characteristic pattern is 

displayed only for each of the 903 finite-element nodes and is portrayed as 

large pixels. Finally, figure 7(d) shows a measured pattern, sub-sampled 

from electronic holograms, at the same amplitude and with the same number 

of pixels used to generate figure 7(c). 
NASA/TM—2003-212078 10 

Figure 7. Measured and model generated chord-wise modes. (a), (b), and (d) are measured 

and (c) is model generated. Both (c) and (d) are sub-sampled. 

The first thing to note is that the input data for the neural net is supplied 

at less than CCD-camera resolution and, in fact, at finite-element-model 

resolution. A few hundred to a few thousand finite elements are typically 

used for a compressor blade design. The second thing to note is that the 

patterns in figures 7(c) and 7(d) look quite similar. In fact, the performances 

of the neural networks have been quite comparable for model-generated and 

experimental data. The author is quite confident in changing from one kind 

of data to the other in testing neural-net behavior. But the fringe patterns 

predicted from finite-element models are not necessarily accurate 

themselves. Considerable work in the development of these finite-element 

models needs to be done, if neural nets are to be used to compare finiteelement structural designs with measured mode shapes. 

Most often the patterns modeled or measured have been the characteristic 

patterns of classical time-average holography. The hologram is recorded, in 

principle, during at least one cycle and, in practice, over several cycles of 

vibration. Most simply subtracting two identical holograms whose reference 

beams differ only by a phase shift of π generates the characteristic pattern. 

The mathematical form of the model-generated patterns, and ostensibly the 

measured patterns, is given by 

( Pattern) (2 ) Speckle ∗ J 0 π K • δ (1) 

Here, K is the sensitivity vector or the difference between the incidentlight-ray vector and the reflected-light-ray vector, all divided by the 

wavelength. The vibration displacement amplitude is denoted by δ. The 

symbol J denotes a Bessel function. The speckle pattern is generated from a 
NASA/TM—2003-212078 11 

model of the speckle effect by using the random number generators of the 

software package. The pattern represented by equation (1) can be signed or 

unsigned, but selecting the absolute value is convenient for visualization. For 

model-generated patterns, the displacement is computed at each node of the 

finite-element model. 

A measured pattern is likely to be corrupted by a number of additional 

effects. These include electrical pattern noise, non-uniformities in 

illumination, pixel saturation and differences in the two holograms used to 

calculate the patterns. In practice, the net has proven to be reasonably 

immune to these effects. The feed-forward net is quite effective at handling 

Gaussian noise sources. The patterns can be averaged to improve the signalto-noise ratio for speckle-effect and electrical noise, but the objective has 

been to use two holograms per pattern when possible. Processing is done 

with software, and the combination of 2-hologram patterns and finiteelement resolution has permitted 30-frame-per-second visualization. 

The rules to be stated for the net and training-set designs also apply to 

double-exposure electronic holograms. An additional complication in 

double-exposure holography is synchronization or the point in the vibration 

cycle where the two exposures are recorded. Fluctuations constitute an 

additional noise source. The remainder of this chapter discusses timeaverage holography exclusively. 

The design rules are based on results of statistical design-of-experiments 

and response-surface methods22 as well as experience. In effect, the neural 

network is treated as a black box, and its response is measured 

experimentally. Among the factors that could be considered are: the number 

of independent or uncorrelated speckle patterns per mode, the number of 

distinct vibration modes or classes of modes, the number of input nodes, the 

intensity range of the test patterns, the accompanying pixel saturation 

effects, the vibration amplitude and number of characteristic fringes, the 

transfer functions of the neurons, additive electrical noise, the nature of the 

output code, the number of hidden layers, the number of hidden layer nodes, 

and the coding, conditioning and transformation of the input patterns. 

Early experiments measured performance as a function of the number of 

speckle patterns per mode, the number of hidden layer nodes, pixel 

saturation, and the size of a modeled crack.14 A later study showed that the 

coding, conditioning and transformations of the input patterns affected 

significantly the magnitude of the minimum detectable change in the 

vibration amplitude distribution.13 The results of these studies have proven to 

be effective for both model and experimental data. The design rules listed 

next were developed for feed-forward nets that contain one hidden layer.14
NASA/TM—2003-212078 12 

First, the neural nets are called sparse, in that they contain few hidden 

layer nodes. In general, 3 hidden-layer nodes are used for each distinct mode 

or class of modes to be learned. For example, a net can be trained to learn a 

particular mode for both cracked and uncracked versions of a cantilever by 

using a net with 6 hidden-layer nodes. The vibration level at the tip is the 

same for both the cracked and uncracked versions. Or a net can learn to 

assign one mode to a single class and several other modes to another class, if 

that net has 6 hidden-layer nodes (3 for each class). The class codes, in this 

case, are called degradable classification indices (DCI). The reason for this 

terminology is that a change in the mode shape of the mode in a class by 

itself decreases the corresponding node output. Typically each class has its 

own output node. This property is used to implement the whole-field 

damage-detection technique. The rule for the number of hidden-layer nodes 

is not exact. In some cases, fewer than 3 hidden-layer nodes per class are 

effective. The use of more hidden-layer nodes has little effect. A sparse net 

can have large numbers of input and output nodes without taxing the 

software; hence, sparse nets are convenient for processing images and fringe 

patterns with large numbers of pixels. Real-time (30-frame-per-second) 

processing can be maintained. 

Second, there must be enough uncorrelated speckle patterns per mode for 

the net to learn to ignore the speckle effect. In effect, the net has to see 

enough independent noise samples to learn to treat speckle-effect noise as 

irrelevant. An effective rule is to select uncorrelated speckle patterns equal 

in number to 10 percent of the number of input pixels. A typical pattern, for 

example, would contain 2000 sub-samples. A training set would then contain 

200 uncorrelated speckle patterns per mode. 

Third, the net can tolerate some saturated inputs. Unsigned inputs 

typically occupy the range 0 to 1 for the feed-forward nets. By contrast, the 

hidden-layer and output-layer nodes typically use the S-shaped sigmoid 

transfer function. The sigmoid function approximates the unit step function. 

Output codes are normally chosen to be [0.2, 0.8] instead of [0, 1]; since the 

latter values would require sigmoid-transfer-function arguments of minus 

and plus infinity. 

The tolerance of input-pattern saturation was proven by multiplying the 

inputs by constant saturation factors and clipping the inputs greater than 1. 

The results were not affected even when the saturation parameter was as 

large as 4. No doubt, the large number of unclipped darker speckles was the 

reason that such a large saturation factor could be tolerated. 
NASA/TM—2003-212078 13 

Fourth, the preparation and conditioning of input patterns can have a 

significant effect on the ability of the net to learn to distinguish patterns.13

Early experiments with the raw absolute value patterns represented by 

equation (1) had difficulty distinguishing the model-generated cracked and 

uncracked cantilever modes. It was necessary to amplify the difference in 

displacement distributions, before the net would learn the difference. A data 

transformation technique called folding13 changed that result significantly. 

Following folding, the crack could be de-amplified, and the net would learn 

to detect it. 

Folding is an intensity dependent transformation of the raw input data. 

Consider the signed characteristic pattern, generated from one model of the 

speckle effect, given by: 

mode = A cos(Θ)J (2π K • δ ) 0 (2) 

In equation (2), A is a positive random quantity, and Θ is a random 

number uniformly distributed between 0 and 2π. The sub-sampled signed 

quantity can be used to train the net, or the data can be folded before 

training. The folding transformation is shown in figures 8 and 9 for one and 

3 folds, respectively. Signed input data are normalized in the range [–1, 1] 

and unsigned input data are normalized in the range [0, 1]. One fold is 

equivalent to taking the absolute value of the pattern in equation (2). The 

contrast of the transformation can be reversed as well. 

Figure 8. Intensity data folded once. Equivalent to absolute value. 
NASA/TM—2003-212078 14 

Figure 9. Intensity data folded three times. 

The effectiveness of folding is proven for the blade and finite-element 

node- pattern shown in figure 10. Three copies of the blade shown in the 

upper right of figure were manufactured. One of the blades had a small crack 

in its base. Finite-element models were created for both the cracked and 

undamaged blades. A neural net was trained using the mode shapes 

computed from the finite-element models, using various numbers of folds. 

The training errors are shown to decrease as the number of folds increases 

up to 8. Folding increases the sensitivity of the neural-net inspection process 

for identifying cracks. The crack amplitude difference was actually deamplified by a factor of 10 for this demonstration. A training error, a test 

error (obtained from different speckle patterns than used for training) and a 

test percent-error-rate are shown as a function of the number of folds. 

Other kinds of data transformations and preparation have been used to 

improve learning. Stretching individual pixel intensity distributions into the 

same intensity range is sometimes used, but leads to over fitting and the 

tendency to learn the noise rather than to learn to ignore it. A net that uses 

more general transformations of input data is the Functional Link Net.18 But 

folding handles the damage detection application very effectively and in a 

most simple manner. Nets have been trained using folding to detect changes 

in displacement distributions, where the maximum displacement change was 

as little as 10 nanometers. 

The suggested net and training-set design rules can be verified using 

model-generated data. The next section discusses briefly how to record 

experimental speckle patterns for training. Experimental training records are 

required for the damage detection method to be discussed shortly. 
NASA/TM—2003-212078 15 

Figure 10. Training error, test error and error rate versus number of folds for 

finite-element-model data generated for the blade and node pattern shown 

3.3 Experimental Training Sets 

The recording of experimental patterns for training a neural net to detect 

structural damage and changes, or to compare with computationally 

generated patterns, is especially advantageous. Experimental recording of 

sub-sampled patterns automatically encompasses the vagaries of the 

holography rig, including beam fluctuations and non-uniformities, electrical 

noise, illumination and reflection variations, difficult-to-model artifacts, 

random material and geometrical effects, ill-defined boundary conditions, 

and any of the hidden effects that are easily overlooked in modeling. 

The major requirement is to record enough uncorrelated speckle patterns 

according to the second rule in section 3.2. Physically, speckle patterns do 

not change rapidly enough, without external inducement, to accomplish this 

efficiently, although they do vary with vibration-induced beam changes and 

the like. Fortunately, the large pixels solve that problem. Measurements are 

made at random positions within the large pixels, such as make up 

figure 7(c), to build up the sub-sampled input array. A single full-resolution 

characteristic pattern could, in principle, be used to generate an entire 

training set of sub-sampled patterns. But selecting a new characteristic 

pattern each time a training record is assembled allows the other vagaries to 

manifest themselves. In any case, nets trained in this manner tolerate 

variations in the speckle effect as well as do model-trained nets. 
NASA/TM—2003-212078 16 

It should be noted that data acquired to compare with finite-element 

models will need to be consistent with the positional variations of the 

element size. Figure 10 shows how the spacing of the nodes in a finiteelement pattern can vary. 

The next section discusses how to use the material of sections 3.2 and 3.3

to train a neural net to detect structural damage or structural changes. 

3.4 Training Neural Nets to Detect Structural Damage 

by Using the Mode Shapes of the Undamaged 

Structure 

The following procedure is designed to accompany a possibly destructive 

test of a structure or a test where structural changes might occur. Examples 

include, but certainly are not limited to, pressure-cycle-induced damage, 

fatigue failure, de-bonding in composite structures, loose fasteners, and 

bearing changes. The assumption is that the structure is available for 

holographic vibration-mode analysis before conducting the possibly 

destructive test. The procedure uses only the vibration modes of the 

unchanged or undamaged structure for training.11,12 

First, use electronic holography to identify about 5 vibration modes of 

the structure to be inspected. 

Second, establish that parameters such as fastener torques, object 

alignment, excitation, and amplitude measurement are under control. The net 

will detect effects of boundary conditions, for example, resulting from 

changes in fastener torques. The holographic inspection is most easily done 

on a vibration-isolation table. It may be convenient to conduct both the 

inspection and the possibly destructive test on the same table. Otherwise, 

attention must be paid to realigning the object accurately relative to the 

holocamera for each inspection. Random alignment errors are a noise source, 

and feed-forward nets can learn to tolerate them to some extent. Field 

inspections are possible, and inspections have been performed using fiberbundle holocameras for remote access to an object. But it is still best and 

easiest to use the optical table. 

Third, establish the sampling grid for the large pixels. Non-rectangular 

and curved objects may require large pixels that vary in size. For the sample 

applications discussed in section 4, the pixel coordinates of the edges of the 

object were measured through the holocamera and supplied as inputs to a 

grid-calculating software package. Typically 1,000 to 2,000 nodes and the 

associated large pixels are used. 
NASA/TM—2003-212078 17 

Fourth, select three vibration modes, and choose one of these modes as 

the mode to be monitored for structural changes and damage. This process 

might require some trial and error. The mode mix will determine the 

sensitivity of the process to some extent, and affords some choice, for 

example, in calibrating or quantifying the inspection. 

Fifth, excite the vibration mode to be monitored for damage. The 

excitation level must be controlled. Fluctuations in the amplitude constitute a 

noise source. It is convenient to monitor a point on the object using a laser 

interferometer to assure that the vibration amplitude is controlled. An expert 

operator may also be able to set the amplitude adequately by viewing the 

characteristic pattern. Typically, the maximum excitation amplitude will not 

exceed one wavelength of light. 

Sixth, record the required number of speckle patterns as discussed in 

section 3.2, and assign an output code for the mode to be monitored. For 

example, two output nodes might be employed. The output code might be 

[0.8, 0.2] for feed-forward nets as already discussed. The first number is the 

DCI of the pattern to be monitored. The other vagaries in the set-up and 

procedure automatically will be incorporated in setting the code. 

Seventh, record the required number of speckle patterns as discussed in 

section 3.2 for the other two modes and for the zero-amplitude condition. 

Use another code for these patterns that places them in the same class. The 

output code might be [0.2, 0.8]. In effect, these patterns are examples of 

patterns that differ from the pattern-to-be-monitored. Changes in the mode 

shape of the pattern being monitored will then result in a decrease in the DCI 

generated by the trained net. 

Eighth, construct a feed-forward net with 6 hidden-layer nodes as 

discussed in section 3.2, and train the net to an RMS error of 0.01 or less. 

The 6 hidden layers support 2 classes. The RMS error is computed from the 

squares of the differences between the training outputs and measured outputs 

for all training records. 

Ninth, test the net for sensitivity. Ideally, the net is calibrated or at least 

quantified to be consistent with certain structural test criteria. One approach 

is to make the neural-net test consistent with NASA vibration handbook 

standards.23 In the absences of calibration or quantification, simple judgment 

may be the only alternative. For example, light point loads can be applied at 

critical locations to test the neural net response, or fastener torques can be 

varied. 

Tenth, change the vibration-mode mix, if the sensitivity is not correct, 

and repeat the above steps. The vibration-mode-to-be-monitored is the most 

critical. 
NASA/TM—2003-212078 18 

Eleventh, establish criteria for interpreting the results of the test. For 

example, a change in the DCI from 0.8 to 0.7 might be declared to be an 

indicator of damage. At 0.7, an alarm can be triggered. One approach is to 

color the fringe pattern differently, when the DCI decreases below 0.7. A 

change of the fringe color from green to yellow or red can be used as an 

indicator. This is decidedly an ANOVA viewpoint. For noisy data, a 

regression of the DCI against some test parameter such as time or the 

number of vibration cycles or the number of pressure cycles might be more 

appropriate. A trend can be established and its significance measured. 

Interpretation will be discussed in more detail together with the applications. 

Experience has shown that these steps can be executed very quickly. The 

time required to record a training set, train the net, and link the net with 

other software was typically twenty minutes for the two sample applications 

to be discussed. 

The first application of neural-net detection of damage demonstrates some 

of the statistical considerations involved in interpreting results and applying 

quantification or calibration techniques. The second application discusses an 

early use of unfolded neural-net technology to detect pressure-cycle-induced 

damage in an International Space Station instrumentation cold plate. 

4. SAMPLE APPLICATIONS 

4.1 Optical Strain Gauge Mounting Plate 

This application demonstrates the ability of the neural networks to detect 

changes in a vibration mode shape in a region remote from the cause. In that 

sense, the application is a health monitoring application. The object is a plate 

used to mount and test optical strain gauges,9

 and was discussed in 

section 2.2 in connection with changes in frequencies of vibration-modes. 

The frequency changes, in turn, were caused by a change in the torque of a 

mounting screw. Figure 3 showed a vibration mode of the plate mounted 

between two vise-grip mounts. Figure 11 shows the plate in one of the 

electronic holography arrangements used to measure it. As already stated, 

frequency changes were induced by adjusting the torque of the second screw 

from the top on the left. 

The plate itself was rather thin at 813 μm, and was 152.4 mm wide and 

101.6 mm high. Optical strain gauges typically are to be mounted along the 

center line, when the plate is to be used for that purpose. 
NASA/TM—2003-212078 19 

The key point is that only the plate itself, and not the vise-grips and 

screws, were visible to the neural nets. Hence, only the mode-shape change 

on the plate itself was relevant. Figure 12 shows the mode shapes as well as 

part of the mounting hardware. Screw torques were initially set to 70 in. lb. 

The torque of the second screw from the top on the left was then backed off 

fully and set to 25 in. lb for training. The training sets, subsequently 

recorded, remained valid for weeks, provided that the torques were 

periodically re-adjusted to the initial values. 

Figure 11. Optical strain-gauge plate in an electronic holography arrangement 

Figure 12. Mode shapes for optical-strain-gauge mounting plate. 

Resonant frequency increases left to right and top to bottom. 
NASA/TM—2003-212078 20 

The sampling grid was almost uniform, and was 42 nodes wide and 

28 nodes high for a total of 1176 nodes. Hence, the neural net had 

1176 input nodes. The full resolution of the image that was used for the 

inspections was 537 pixels wide and 395 pixels high. The software 

automatically adjusted the large pixels at the end of a row and the bottom to 

handle round off. The average large pixel then contained about 180 speckles 

from which the random patterns were generated. 

The fifth mode at about 473 Hz was placed in a class by itself for 

monitoring; since the frequency changes of that mode were found to vary 

most strongly with changes in the mounting screw torque. The first mode, 

the second mode and the zero-amplitude condition were all selected as 

members of the second or comparison class. Each comparison mode was 

assigned the same output code. 

A single point of the plate was monitored with a laser interferometer, so 

that the vibration amplitude at that point could be set and maintained at the 

same value. The point was located on the rear of the plate, 19 mm from the 

bottom and 29 mm from the edge of the left vise-grip as viewed from the 

holocamera side. The peak-to-peak amplitude was set and maintained at 

0.32 μm for the fifth mode at the laser-interferometer detection point. The 

fluctuation in the amplitude, due to various noise sources, was about 

0.025 μm. That fluctuation consisted primarily of a superimposed first mode, 

and constitutes one of the vagaries of the training procedure. It can lead to a 

rather substantial variation in the monitored DCI; since the first mode was 

already assigned to the comparison class. Perhaps the first mode should be 

avoided as a member of the training set; since it is so easily excited. But the 

first mode was in fact included in the training set for this sample application. 

Ten percent of the number of large pixels or input nodes is equal to 118. 

Hence, in accordance with section 3.2, 118 sampled uncorrelated speckle 

patterns were recorded for the first, second, and fifth modes and the zeroamplitude condition. Needless to say the zero-amplitude condition contained 

a small amount of environmentally excited first mode, as did the other 

modes. The software automatically performed random selection of the 

speckles from one pattern to the next. The fifth mode was assigned the 

output [0.8, 0.2], and the other modes together were assigned the output 

[0.2, 0.8]. For those cases where folding was employed, the input data were 

also subjected to a 5-fold folding transformation as discussed in connection 

with figures 8 and 9. 

A feed-forward net was then constructed in software. It consisted of 

1176 input nodes, 6 hidden-layer nodes with sigmoid transfer functions, and 

2 output nodes with sigmoid transfer functions. The net was then trained to 

an RMS error less than 0.01. 
NASA/TM—2003-212078 21 

The trained net was converted into C-language code and compiled and 

linked to the software of the electronic-holography inspection system. As 

mentioned before, the entire process requires about 20 minutes. 

4.2 Statistical Results 

Tests variously involved recording 10 uncorrelated speckle patterns per 

torque setting and recording 100 uncorrelated speckle patterns per torque 

setting, each averaged 10 times for electrical noise. 

The net had no trouble detecting changes, even in a 10 in. lb interval, 

when the amplitude was not reset to its constant value of 0.32 μm. But the 

mode shape does not change much, as stated before, at a constant amplitude 

setting. Nevertheless, the neural nets did detect these mode-shape changes. 

Figure 13 shows the results of an ANOVA performed on 100 uncorrelated 

speckle patterns per torque setting. Each of these patterns was averaged 

10 times for electrical noise, random first-mode vibration noise and other 

noise sources. The DCI can be observed to decline as the screw torque is 

increased from the training value of 25 to 70 in. lb. The Bonferroni 

simultaneous confidence intervals are clearly separated. The most important 

point is that this test was repeated several times over a 2-week period using 

the same training set. The DCI decreased significantly in all cases. Hence 

optical alignment effects, and other systematic and random effects, were not 

critical. An F Value test of significance indicated that the probability of the 

observed variation being due to random chance was less the 0.001. 

25 45 70

0.4

0.5

0.6

0.7

0.8

0.9

D

C

I

TORQUE (in lb)

Figure 13. Neural-net response to change in mode shape induced by a change in screw torque. 
NASA/TM—2003-212078 22 

The above data were also tested with a linear regression using the screw 

torque as the only factor. This test provides a better measure of the scatter of 

the data and goodness of fit. The R2

 value was 0.75 indicating quite a bit of 

scatter. But the T-value for the slope was almost –30 indicating that the 

significance of the slope was much better than 0.001. The slope is given by 

–0.008202 (in. lb)–1. 

Regression also allows the lower detection limit to be quantified. For that 

purpose, a trend was sought over a 10 in. lb range only from 25 to 35 in. lb. 

Ten patterns were recorded at each station without averaging, and there were 

stations at 25, 31, 34, and 35 in. lb. The input data were folded 5 times in the 

manner of figures 8 and 9. The slope obtained from these measurements was 

–0.008636 (in. lb)–1, but R2

 was only 0.085, and the significance of 0.069 

was poorer than the old industrial standard for significance in statistical 

testing (0.05). 

 For this application, crisp red-lining of changes in the mode shape 

induced by changes in the screw torque required more than 10 patterns per 

station with station separations of 10 in. lb or more. Crisp results required 

100 patterns per torque value. Trends could be established with less data by 

using regression. But both regression and ANOVA require repeat 

measurements because of noise. Hence, visualization needs to proceed much 

more slowly than 30 frames per second. An application that was 

accomplished at the full visualization rate with unfolded training data is 

discussed next. 

4.3 Inspection of International Space Station Cold Plate 

This application11 was performed before the discovery of the folding 

technique; hence a more sensitive test might have been possible. 

Nevertheless, this application defines the kind of test that is intended for 

damage detection from neural-net inspection of fringe patterns. The 

application was conducted with real-time visualization as well as by 

examining repeat samples. The subject was an International Space Station 

(ISS) instrumentation cold plate shown in figure 14. The cold plate had holes 

for mounting instrumentation and internal cooling passages for cooling the 

instrumentation. It was suspected that the cooling passages might not 

withstand pressurization; hence it was decided to perform pressure cycling 

of the cold plate and inspect the cold plate for ensuing damage. The cold 

plate was in fact inspected with a variety of techniques including classical 

silver-halide-emulsion holography and ultrasonic C-scan imaging. Only the 

neural-net inspection is discussed. A key point is that the holographic nondestructive inspection and the potentially destructive test were performed 
NASA/TM—2003-212078 23 

simultaneously on the same vibration isolation table. Another point is that 

the decision of the net was crisply reported. That is, the DCI was noted only 

as being greater than or equal to a redline condition. The monitored fringe 

pattern was colored green for a DCI greater than or equal to 0.65 and yellow 

otherwise. 

The cold plate was bolted to a frame through several of the holes 

intended for mounting instrumentation. The cold plate was filled with water 

and connected to a pressure cycling source. Neural net inspections were 

performed before, during and after pressure cycling. Both the entire plate 

and a suspicious region between four boltholes were inspected. The method 

of 3.4 is discussed only for the inspection between the four boltholes. The 

boltholes are on 4 in centers. 

Figure 14. ISS cold plate from front 

Figure 15 shows five modes covering a region including the suspicious 

region between the four boltholes. 

Fasteners were set, and the cooling passages were connected to the 

pressure source at 120 psig. The source could be cycled at 0.2 Hz. 

The sampling grid consisted of 40 by 54 large pixels. Figure 16 shows 

the large-pixel pattern at zero amplitude. 

The selection of an adequate set of training modes actually required more 

than one attempt. The selection process involved applying slight point loads 

to the cold plate and observing the neural-net response. Clearly a statistical 

approach or a calibration would have been preferable. The mode at 2310 Hz 

was found to be most sensitive to perturbations, and that mode was selected 

for inspection. The modes at zero amplitude, 780 and 1597 Hz were used for 

the comparison class. A laser interferometer controlled the vibration 

amplitude. 
NASA/TM—2003-212078 24 

Figure 15. Modes covering region between four bolt holes 

Figure 16. Large-pixel sampling pattern between four bolt holes 

There were 2160 large pixels covering a region of 314 by 423 small 

pixels. Hence the training set for each mode contained 216 uncorrelated 

speckle patterns. 

The feed-forward net contained one hidden layer and that layer contained 

6 hidden-layer nodes as discussed before. Training proceeded quickly. As 

stated the process of section 3.4 needed repeating before the most sensitive 

training set was found. 

The trained net was then used during a 1000 pressure-cycle test and 

inspection of the region between the boltholes. A 2000 pressure-cycle test 

had been conducted previously for the whole plate. The plate was 

continuously monitored during the test, even during pressure cycling. 
NASA/TM—2003-212078 25 

Pressure cycling was interrupted every 250 cycles to record 20 neural-netprocessed characteristic patterns at the pressure-relaxed condition. 

The net flagged a significant change in mode shape at maximum 

pressure, but always flagged an insignificant change at the pressure-relaxed 

condition. Figure 17 shows the mode between the bolts for the pressurerelaxed and pressurized conditions. Note that only part of the mode shown in 

figure 15 actually appears between the boltholes. 

The conclusion of this test was that the neural-net inspection of mode 

shapes did not show significant changes or damage from pressure cycling. 

This conclusion was in agreement with the conclusions reached using 

ultrasonic C-scan testing. 

Figure 17. Response of neural-net to pressure-relaxed and pressurized conditions. 

A DCI less than 0.65 is coded yellow in displaying the fringe pattern. 

5. SUMMARY AND FUTURE TRENDS 

Whole-field laser holography and interferometry are non-destructiveevaluation (NDE) methods. They are used to determine whether a 

component, structure, or system changes from its design condition during 

normal operations or other testing. The NDE method is non-invasive and 

does not itself change the structure. 

Electronic recording of interference patterns makes the inspections much 

more efficient. The data are also available immediately for computer analysis. 

Neural-net processing allows the entire fringe pattern to be examined 

simultaneously and in parallel. The inspection process and ensuing decisions 

are automated. The approach reported in this chapter is essentially a formula 

procedure. It can be consistent from one application to another and from one 

laboratory to another. It is subject to calibration and quantification. It 

provides a solid answer to a question that has been asked since laser 

holography was discovered: “Now that we have all this data, how do we 

interpret and use it?” 
NASA/TM—2003-212078 26 

The ultimate goal, and one of the original goals for the neural-net 

processing of fringe patterns, was to create an efficient interface between 

damage models and data. A computational model of a structure and an 

optical model of an inspection process can be used to generate training 

records. The trained net can be then be used to examine experimental 

patterns. The inverse process of using experimentally trained nets to 

examine model-generated patterns also is relevant. So far the structural 

models used have not been accurate enough, in general. But the noise 

immunity of the feed-forward net and the availability of probabilistic finiteelement models24 offer future hope for the model interface.

 The technology of damage detection using neural-net inspection of 

fringe patterns is definitely good enough to be placed in the hands of 

designers and NDE specialists. The technology constitutes a standard that 

their models and procedures hopefully will meet, eventually. The most 

straightforward approach is to incorporate the neural-net procedure into 

existing standards for vibration testing.23 The response of the net and the 

responses of standard sensors such as strain gauges and displacement sensors 

can be correlated. 

Most of this chapter has discussed the fringe patterns from electronic 

time-average holography. But the general procedure of section 3.4 certainly 

can be adapted to other kinds of damage sensitive patterns. These patterns 

can be obtained variously from speckle photography and interferometry, 

moiré deflectometry, thermography, and temperature sensitive paints, large 

angle scattering, and any other image forming method that is affected by 

damage. The algorithmic approach of section 3.4 could be applied virtually 

unchanged. 

The technological limit of the neural-net inspection technique is that it is 

based in software and is limited to a few thousand sub-samples. An 

improvement would require efficient channels for direct neural-net 

processing of optical data. 
NASA/TM—2003-212078 27 

REFERENCES 

1. Y. Bar-Cohen, ed., Automation, Miniature Robotics and Sensors for Nondestructive 

Evaluation and Testing, Vol. 4, Topics On Nondestructive Evaluation, B. B. Djordjevic 

and H. D. Reis , The American Society for Nondestructive Testing, Inc., Columbus, OH 

(2000). 

2. C.M. Vest, Holographic Interferometry, Wiley, New York (1979). 

3. R.J. Collier, C.B. Burckhardt and L.H. Lin, Optical Holography, Academic Press, New 

York (1971). 

4. Ref. 1, 125-126. 

5. S.A. Benton, ed., Practical Holography VI, Proc. SPIE 1167 (1992). 

6. Ref. 3, 451. 

7. A.J. Decker, “Holographic interferometry with an injection seeded Nd:YAG laser and 

two reference beams,” Appl. Opt. 29(18), 2696-2700 (1990). 

8. Ref. 2, 147-177. 

9. J. Lekki, G. Adamovsky, P. Flanagan and K. Weiland, “Evaluation of mechanical modal 

characteristics using optical techniques,” NASA TM 2002-211678 (2002). 

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

Analysis and Nondestructive Testing,” Optical Engineering 26(12), 1234-1239 (1987). 

11. 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 (1999). 

12. A.J. Decker, “Neural-Net Processed Characteristic Patterns for Measurement of 

Structural Integrity of Pressure Cycled Components,” Proceedings of JANNAF, 

12thNondestructive Evaluation Subcommittee, Cocoa Beach, Florida (2001). 

13. A.J. Decker, “Optimization of training sets for neural-net processing of characteristic 

patterns from vibrating solids,” Proc. SPIE 4448, 209-217 (2001). 

14. A.J. Decker, E.B. Fite, O. Mehmed and S.A. Thorp, “Processing speckle patterns with 

model trained neural networks,” Proc. SPIE 3172, 285-293 (1997). 

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

patterns,” International Conference on Optical Technology and Image Processing Fluid, 

Thermal, and Combustion Flow, Proc. VSJ-SPIE `98, on compact disc in pdf, paper 

AB002 (1998). 

16. Ref 1, 309-321. 

17. D.E. Rumelhart, J.L. McClelland, and the PDP Research Group, PARALLEL 

DISTRIBUTED PROCESSING, Explorations in the Microstructure of Cognition, 

Volume 1: Foundations, MIT Press, New York (1986). 

18. Y-H. Pao, Adaptive Pattern Recognition and Neural Networks, Addison-Wesley, New 

York, 120-129 (1989). 

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

Cambridge (1996). 

20. Examples in this chapter were created using NeuralWorks Professional II Plus. 

21. W.C. Hurty and M.F. Rubinstein, Dynamics of Structures, Prentice-Hall (1964). 

22. Statistical analysis in this chapter was performed with RS/Suite software. 

23. “Dynamic Environmental Criteria,” NASA-HDBK-7005 (2000). 

24. W.K. Liu, T. Belytschko, and Y.J. Lua, “Probabilistic Finite Element Method,” 

Probabilistic Structural Mechanics Handbook, Theory and Industrial Applications, C. 

(Raj) Sundararajan, ed., Chapman & Hall, 70-105 (1995). 
This publication is available from the NASA Center for AeroSpace Information, 301–621–0390.

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33

Damage Detection Using Holography and Interferometry

Arthur J. Decker

Holography; Interferometry; Non-destructive evaluation; Neural network

Unclassified -Unlimited

Subject Category: 35 Distribution: Nonstandard

Responsible person, Arthur J. Decker, organization code 5520, 216–433–3639.

This paper reviews classical approaches to damage detection using laser holography and interferometry. The paper then

details the modern uses of electronic holography and neural-net-processed characteristic patterns to detect structural

damage. The design of the neural networks and the preparation of the training sets are discussed. The use of a technique

to optimize the training sets, called folding, is explained. Then a training procedure is detailed that uses the holographymeasured vibration modes of the undamaged structures to impart damage-detection sensitivity to the neural networks.

The inspections of an optical strain gauge mounting plate and an International Space Station cold plate are presented as

examples.