SelfScan

Signal Processing and Defect Detection

Pavlos Stavrou

SelfScan Webinar

19th April 2012

About myself Education ◦ Graduate of the Informatics and Telecommunications

Department of the university of Athens

◦ Currently completing a PhD on 3D Graphics and Pattern Recognition

Experience ◦ 5 years as a researcher and developer in R4SME projects

◦ 2 years as a Senior Project Manager

◦ 3 years as a researcher for CERETETH working on R4SME for NDT

Expertise ◦ Computer Vision, Pattern Recognition, Neural Networks,

3D Graphics

◦ Software design and development in C/C++, .NET Framework, Java

◦ Embedded application development for low-power mCUs

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Automated Defect Detection and Characterization Goals

◦ Filter the received signal to reduce noise

◦ Automatically recognize defects based on

the received LRUT signals

Tools

◦ Probabilistic classifiers

◦ Neural networks

◦ Signal processing routines

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Signal Processing Software

Prior to being input to the Neural Network for classification LRUT signals must undergo pre-processing

Signal conditioning and display software developed

Functionality ◦ Temporal and spectral visualization

◦ Central Frequency Estimation

◦ Bandwidth Detection

◦ Pulse Isolation

◦ Gaussian Filtering

◦ Low/High/Band-pass Filters

◦ Signal Envelop

◦ Power Density Spectrum

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Signal Processing Software

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Reads .asc files directly from Teletest unit

Features calculated upon successful file loading

Signal Processing Software

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Frequency and Power Density

Spectrum

Signal Processing Software

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Pulse Isolation (requires an expected arrival time input).

Signal Processing Software

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Filtering Example (Low pass – 100 KHz)

Images correspond to original and power density spectrum after filtering

Building a Pattern Recognition System Steps

◦ Feature Generation

◦ Feature Selection

◦ Neural Network Design

◦ Training the Neural Network

◦ Neural Network Validation and Error

Probability Estimation

◦ Neural Network Refinement (Fine-Tuning)

◦ Field Testing and Evaluation

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Feature Generation Feature Types

◦ Textural Features 1st Order Statistics

Variance (σ2) Mean Value

2nd order statistics Correlation / Partial Correlation

Contrast

Shape Features Perimeter Size

Slope

Curvature

◦ Frequency Space Features Magnitude

Phase

Amplitude Spectrum Curvature

Bandwidth

◦ Temporal Features Time of Flight

Pulse duration

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Feature Selection

Goals ◦ Select the optimum number N of features

◦ Select the “best” n features The best features refer to those with high discriminatory power

Classes and Features ◦ n must be large enough to learn

What makes classes different

What makes patterns in the same class similar

◦ n must be small enough to learn what makes pattern of the same class different

◦ In principle we need our discriminatory ability to be large between class distance and small between class variance

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Feature Selection

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Feature Selection

Process ◦ Features are evaluated and selected by being

examined jointly as vectors

◦ Given a set of measurements for all features and known classification for each vector

◦ Define a function for classification (Bayess classifier, simple perceptron)

◦ Iterate between permutations of features to construct feature vectors

◦ Construct truth tables by omitting one measurement per iteration for training and compute the probability of error for each permutation

◦ Select the feature vector with the lowest error probability

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Neural Networks

What are the advantages of using neural networks ? ◦ Distributed/Parallel information processing

◦ Robustness

◦ Training

How can NNs aid in defect detection ? ◦ Since LRU signals acquired from structures

with complex geometry are very complex, we need the processing and training capability of neural networks to detect even the finest differences in signals in order to classify them accurately.

Multi-Layer Perceptron

Features ◦ Can classify non-linearly separable classes

◦ Feed-forward Classifier

◦ Each node in a layer is connected to all nodes to the layer to its right

◦ Each vector is augmented by one constant input of +1

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. . . . . . . . . . . . . . . .

Multi-Layer Perceptron

Each additional layer in the MLP

correspond to higher order

discriminatory ability

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MLP Training Goal ◦ Find weights for all connections so that the desired output

is obtained for all feature vectors

◦ Calculate the discrepancy between the input and output and adjust the weights accordingly so it is minimized

Steps ◦ Initialize the weights with random value in the range of [-

1,1]

◦ Feed the input layer with feature vector Fi of class i. The desired output of the corresponding class output neuron should be 1 and the rest 0.

◦ Calculate the output vector

◦ Find the error vector for all neurons of the MLP , where is the desired value of output node k.

◦ Adjust weights

◦ Return to the second step until weight values converge

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( ) (1 )dj j j j je y y c y d

jy

Technique Development

Features examined for aircraft

component defect detection

◦ Estimated Central Frequency

◦ Central Frequency Deviation

◦ Bandwidth

◦ Dominant Pulse Power

◦ Standard Deviation

◦ Covariance

◦ Covariance with reference non-defective

signal

◦ Correlation with reference non-defective

signal

Experimental Evaluation

(fatigue) NN for detecting defects in fatigued sample

Data were collected for frequencies of 500 KHz and 700 KHz

Dataset size : 55 averaged measurements for each transducer pair. ◦ Feature Vector Size : 3

◦ Accuracy Top-Side

93 % when with 0-2mm cracks render the sample defective

100% when a sample is considered defective when crack is over 2.5mm

Notch-Side 46 % when with 0-2mm cracks render the sample defective

98% when a sample is considered defective when crack is over 2.5mm

Bottom-Side 68 % when with 0-2mm cracks render the sample defective

94% when a sample is considered defective when crack is over 2.5mm

Experimental Evaluation

(notch) NN for detecting defects in samples with artificial defect added.

Data were collected for frequencies from 50 to 500 KHz with a step of 50 KHz.

Dataset size : 40 measurements for each frequency, 400 measurements for each temperature class

Extensive data collection for 30 oC @ 100 KHz , roughly 1200 measurements from two different samples

To account for environmental factors temperature was controlled and logged along with measurements. ◦ Feature Vector Size : 4

◦ Accuracy 20 oC :100%

30 oC :100%

40 oC :100%

Extensive 30 oC tests at 100 KHz : 92%

◦ Since the above NNs were specific to temperature we increased the feature vector size and added temperature as a feature.

◦ Accuracy with temperature as feature : 100%

Experimental Evaluation

Conclusion ◦ The developed NN can successfully detect

defective specimens with crack sizes over 2.5mm and artificial cracks

◦ The best transducer arrangement for achieving high accuracy is the Top-side 1-3 composite

◦ The correlation coefficient between reference signal from non-defective samples severely decreases when calculated against defective samples

◦ Correlation on its own is not enough to achieve high accuracy. Fusing with dominant pulse power and other features increases detection accuracy

◦ Temperature does affect the received signal but the NN is able to compensate by using it as a feature

Future Work

Expand the neural network capability to classify defective samples with respect to the defect size or type

Gather data from in-service aircraft components and evaluate the developed neural networks

Include additional cases of aircraft components for NDT

Present and promote our work in conferences, journals.