an inspector calls [nondestructive testing]

3
insDector calls Composite laminate structures offer many advantages compared with their conventional metal counterparts, but defects can be much harder to detect. Dietrich Schuschel describes how neural networks provide the key to a new, intelligent technique for non-destructive testing ow can you pack more payload on a helicopter? This question is H critical whether you are a com- mercial operator tqmg to move people to offshore oil rigs or a military user trylng to carry an extra missile.Every additional pound a helicopter weighs means one less pound of payload, so weight is crucially important to helicopter man- ufacturers and their customers. It is no surprise that composite lam- inate structures, with their high strength to weight ratio, are attractive to man- ufacturers. But if they are to be used in flight-critical applications, non-destruct- ive test (NDT) maintenance inspection is necessary The experts who perform composite NDT duties typically require hundreds or even thousands of hours of training and experience, and tradition- ally have been highly paid and in short supply Now McDonnell Douglas Heli- copter Systems, manufacturer of the Apache attack helicopter (Fig l), has combined signal processing and neural networks in a new approach to testing that is simple to use and requires minimal human intervention. Composites have been applied to aerospace structures because of the weight and performance advantages they can provide compared with conven- tional metal structures. An equivalent weight composite structure is usually significantly stronger than its metal counterpart, and any weight savings result in additional payload or reduced fuel costs. But with these advantages comes a need for support at field level. This requirement has developed primarily with the application of composite struc- tures in army rotorcraft as they have evolved from the almost completely metal structures of 40 years ago to be- come one of the main structural applic- ations of composites today And as the proportion of composite structures has increased, so has the criticality of many of IEE REVIEW SEPTEMBER 1997 them. While substantial defects and damage may still allow a fairing to perform its assigned task safely equiv- alent damage might render a structural component unsafe. To make inspection more complicated, damage and defects, especiallyimpact damage, may be harder to detect in composite structures than in conventional metal structures. A metal structure that experiences impact severe enough to cause sigruficant damage will probably show evidence. A composite structure on the other hand may be damaged internally but show little or no evidence externally Specialised methods using ultrasound or other specialised NDT techniques are therefore needed to support composite structuresin the field. McDonnell Douglas has pursued techniques to develop a field-level detec- tion and evaluation capability for its mil- itary and commercial customers. High- level requirements are for a portable computer-aided system that can inspect a wide range of composite structures rapidly and in situ, detect, identrfy and 1 McDonnell Douglas Helicopter Systems is investigating whether neural nets could be used to automate the expensive testing demanded by the use of composite structures in rotorcraft like the AH-64D Longbow Apache 189

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Page 1: An inspector calls [nondestructive testing]

insDector calls Composite laminate structures offer many advantages compared with their conventional metal counterparts, but defects can be much harder to detect. Dietrich Schuschel describes how neural networks provide the key to a new, intelligent technique for non-destructive testing

ow can you pack more payload on a helicopter? This question is H critical whether you are a com-

mercial operator tqmg to move people to offshore oil rigs or a military user trylng to carry an extra missile. Every additional pound a helicopter weighs means one less pound of payload, so weight is crucially important to helicopter man- ufacturers and their customers.

It is no surprise that composite lam- inate structures, with their high strength to weight ratio, are attractive to man- ufacturers. But if they are to be used in flight-critical applications, non-destruct- ive test (NDT) maintenance inspection is necessary The experts who perform composite NDT duties typically require hundreds or even thousands of hours of training and experience, and tradition- ally have been highly paid and in short supply Now McDonnell Douglas Heli- copter Systems, manufacturer of the Apache attack helicopter (Fig l), has combined signal processing and neural networks in a new approach to testing that is simple to use and requires minimal human intervention.

Composites have been applied to aerospace structures because of the weight and performance advantages they can provide compared with conven- tional metal structures. An equivalent weight composite structure is usually significantly stronger than its metal counterpart, and any weight savings result in additional payload or reduced fuel costs.

But with these advantages comes a need for support at field level. This requirement has developed primarily with the application of composite struc- tures in army rotorcraft as they have evolved from the almost completely metal structures of 40 years ago to be- come one of the main structural applic- ations of composites today And as the proportion of composite structures has increased, so has the criticality of many of

IEE REVIEW SEPTEMBER 1997

them. While substantial defects and damage may still allow a fairing to perform its assigned task safely equiv- alent damage might render a structural component unsafe. To make inspection more complicated, damage and defects, especially impact damage, may be harder to detect in composite structures than in conventional metal structures. A metal structure that experiences impact severe enough to cause sigruficant damage will probably show evidence. A composite structure on the other hand may be

damaged internally but show little or no evidence externally Specialised methods using ultrasound or other specialised NDT techniques are therefore needed to support composite structures in the field.

McDonnell Douglas has pursued techniques to develop a field-level detec- tion and evaluation capability for its mil- itary and commercial customers. High- level requirements are for a portable computer-aided system that can inspect a wide range of composite structures rapidly and in situ, detect, identrfy and

1 McDonnell Douglas Helicopter Systems is investigating whether neural nets could be used to automate the expensive testing demanded by the use of composite structures in rotorcraft like the AH-64D Longbow Apache

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Page 2: An inspector calls [nondestructive testing]

2 Ultrasonic echo responses in which the x axis indicates sample position. The ‘good’ waveform at the top is characterised by a clean front-wall echo (samples 1-25), low noise in the interior of the part (samples 2 5 3 5 ) and a clean back-wall echo (samples 3555) . After sample 55 the pulse has left the part and the remaining echoes represent the pulse ‘ringing’. The lower waveform, representing the response from a part that has suffered impact damage, has a poor looking front wall which is inverted and of low amplitude. There is a lot of noise in the interior part and the back wall has disappeared.

evaluate damage and defects, and eval- as an interactive inspection document. uate completed repairs. When the form in the document is filled

Established test techniques use ultra- out and the action buttons pushed, the sonic pulses to examine a sample of a system runs the classification routines in

providing status up-

The most challenging part of the automation is the classification system, toward the trans-

er of requirements:

The type of material, including sk of composition and finish, is an input.

the Manufacturing variations, which an primarily affect density and porosity

are unknown Material thickness is known within acceptable manufacturing variation.

utomated system to Classification system hardware var- e maintenance doc- iation can occur. This includes sensor

variation, electronics calibration, and ers implies several requirements of its environmental effects of temperature

and humidity Aging of both the classification system and the material will induce variation. The information to be classified consists of a digitised signal

ns 50-300 samples. . The output consists of a diagnosis of

’good’, ’impacted, ’foreign object’ etc. The classifier is presented with a lot of information which has high noise content and witlun which the useful information varies in amplitude, time, shape and frequency.

developed by McDonnell Douglas auto- mates this process to the greatest extent possible, leaving only a few simple man- ual tasks. The test procedure is presented

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Take a look at the ultrasonic echo res- ponses in Figure 2 and see if you can tell which represents impact damage with- out referring to the caption. Now, to make matters worse, throw in all the mentioned above. This sums sifier’s job.

The successful classifier needs to be able to ignore the noise and pick out relevant features of the signal which could vary as described above. though the signal is digtised, it resents an analogue signal, sampl discrete intervals and discrete amp- litude values. Signal processing is there-

important element of the sol-

cia1 neural networks, or neural nets as they are commonly known, are well suited to this job. They have proved to be capable of d e h g with analogue information which cont do not need to be told is classified in a certain way - they need to be shown examples.

To evolve a mature system quickly a ment model was chosen in

iterations of a proto- d, programmed, tested

and evaluated to quickly ‘home in’ on the best solution.

How it’s done A loMHz l 2 V pulse is sent in to the transducer which sends an acoustic pulse in to the test laminate. The receiver then listens to the echo through the same transducer before the electrical analogue signal is digtised at 1 O O M H z and 8bits and stored on a fixed disc.

MATLAB arrays. Raw amplitude is not a reliable indicator however, due to ins- trument drift and variation in transducer efficiency so the traces are normalised and have their DC bias removed. They are then time indexed to the ’front wall’ negative peak. An exponential attenuat- ion curve is applied to the waveform to locate the first negative peak which is the time index, and the waveforms are then time aligned by moving them in the time domain unid each of their time indices is at the same position (the mean time index).

computed for each trace. Although theoretically a redundant input, provid-

The digitised waveforms

The power spectral d

lEE REVIEW SEPTEMBER 1997

Page 3: An inspector calls [nondestructive testing]

Compression is an important step if the neural

net is to be kept at a manageable size

ing the frequency representation as input to the neural net allows faster training and convergence.

Compression is an important step if the neural net is to be kept at a manageable size. In the first step of compression a wavelet transform is applied to the waveform and its PSD, with the resulting output matrices together forming the coefficient matrix. The top coefficients are chosen f" this matrix and used as training, test and runtime patterns for input to the neural nets.

The top coefficients, selected from signal processing, which provided inputs to the neural nets were broken down into two sets, one to train the neural net and another to test it. Three different neural nets were initialised to train on three different selections of input patterns, saving the training and testing results. This resulted in a total of nine neural nets. It's important to train multiple times

as the starting weights and biases in a net are randomised and they may be good or bad. If they are bad, the net might become stuck in a local minimum which means that it can't get any better, but a solution exists. I have found that this effect is best countered by the above procedure.

The output of the neural nets is made up of one or two LogSig nodes. The first node of the output layer is for impact damage and second, when needed, is for foreign substance damage.

The primary measure of these r3ystems is their accuracy and adjustment of sys- tem parameters resulted in continuous improvements over time. Significant improvements were achieved through signal processing and by the end of the project most of the neural net classifiers had achieved 100% test accuracy

Classification of ultrasonic pulse echo signals is a promising application of signal processing neural networks.

0 Soil rcsistivitv data analvsis Grounding analysis: low to high frequencies; transients Linekable constants: overhead or buried; complex pipeenclosed cables

0 Current distribution in skywires, neutrals, shields and metallic paths Inductive, conductive and capacitive interference in shared corridors

0 Frequency and transient analysis of electromagnetic fields 9

1544 Viel, Montreal, Quebec, Canada, H3M 1G4 Tel : (514) 336-2511 Fax. (514) 336-6144 1-800-668-3737 (U.S.A. & Canada) emad [email protected] web site http://www.sestech.com

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IEE REVIEW SEPTEMBER 1997

The extremely high accuracies that can be achieved mean that other potential applications include stock market indicators, voice recognition, seis- mology and astronomy as well as helicopter health and usage monitoring (HUM) MATLAB provides an easy to use environment and specialised toolboxes to test out you ideas. With today's powerful computers and vast amounts of data, we should be seeing some interesting classifier/analysis applications soon.

0 EE 1997

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Dietrkh schuschel is employed by McDonnell Douglas Helicopter Systems, where he dev- elops leading-edge signal analysis systems while working on his Master of Computing Science Degree. He can be contacted by email at [email protected] Matlab is currently available in the UK from Cambridge Control Ltd (telOl223 423 289)

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