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Intelligent Eddy Current Crack

Detection System Design Based on

Neuro-Fuzzy Logic

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Data fusion – ECT signal processing

Oct. 09th , 2013 Baoguang Xu MASc. Concordia University Montreal

Outline

Project description and goals

Eddy current signal feature extraction and analysis using fuzzy logic. Signal de-noise

Signal features and feature extraction

Fuzzy logic

Experiments and results

Conclusions and Future work

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1. Project description and goals

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Team members

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Project description and goals

• In aerospace industry especially in aircraft maintenance, ECT is performed manually.

Time and cost consuming

Result depends on human experience.

• Currently no signal recognition system is able to indicate crack features such as depth and shape etc. automatically

In aircraft maintenance and manufacturing especially for quality control, ECT signal recognition largely relies on the properties of cracks

• The goal of the project--- to provide the aerospace industry with a user friendly, AI aided signal recognition system to obtain the cracks’ information automatically.

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Program Design Diagram

Known Signal input Signal de-noiseSignal

processing(feature extraction)

Fuzzy logic training (ANFIS)

Signal from unknown crack

Trained fuzzy logic

Signal de-noiseSignal

processing(feature extraction)

Crack information

output (depth, width shape

etc.)

Training

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2. Eddy current signal feature extraction and analysis

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Signal de-noise

• Filter choices

High/Low pass filter

Fourier transform.

Wavelet transform. √

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Wavelet de-noise

S(signal

)Filter

cA1

Filter

cD1

Filter

cA2

Filter

cD2

Filter

cA3

Filter

cD3

1. Decompose signal into wavelet components (in which case noises are been

separated).

2. Define the right wavelet C coefficient in order to miniature or remove noises.

3. Reconstruct processed signal by defining C coefficient [1]

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De-noise Result

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Signal features and feature extraction

Impedance of eddy current test Typical crack signal for differential probe

2 2

LZ R X

: ImpedanceZ

: eactanceLX R

: esistanceR R

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Signal features and feature extraction

Before After

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Approaches to obtain the crack information based on ECT signal

• Theoretical model

Analytical Modeling

Numerical Modeling

Drawbacks: It is hard to establish the theoretical models due to the complexity of crack geometry and the non-accessible detailed coil information of the probe.

• Artificial intelligence

Fuzzy logic

Neural networks

Drawbacks: Artificial intelligence techniques require large set of reliable data to train the system.

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Fuzzy logic

• Fuzzy logic is known as an artificial intelligence tool to describe complicated physical phenomena and to anticipate the linear or nonlinear results based on collected input and output data [2].

• Instead of using absolute 1 (true) and 0 (false) to make traditional logical decision, fuzzy logic introduces the concept of membership to describe how the input and output weight (in between 1 and 0) as a member in a certain membership.

Fuzzifier DuzzifierInput OutputFuzzy rules

Membership

FunctionFuzzy logic system

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ANFIS (Adaptive-Network-Based Fuzzy Inference System)

• Neuro-based fuzzy logic is inspired by neural network, similar to that of neural network which constitutes input and output mapping via their membership functions and related parameters [3].

Neuro mapping structure, Fuzzy rules samples after training

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3. Experiments and Results

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Hardware information

Olympus: Nortec 500S Frequency Range 50 Hz -12 MHz Probe: differential reflection probe (PRL/500 kHz - 3 MHz/D) Crack sample: Nortec TB-S1 Standard (deep notches: 8mil; 20mil; 40mil)

Pictures come from:[5][6]

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Hardware information

National instruments: NI USB-6009 8 analog inputs (14-bit, 48 kS/s) 2 analog outputs (12-bit, 150 S/s); 12 digital I/O; 32-bit counter Compatible with LabVIEW, LabWindows™/CVI, and Measurement Studio for Visual Studio .NET

Pictures come from:[7]

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computer Data Acquisition Card

ECT equipment

Hardware Connection

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Testing Sample

Material: Aluminum 7075-T6 notch crack sample

0.008 in. (0.203 mm), 0.020 in. (0.508 mm) and 0.040 in. (1.016 mm) 0.0315in. (0.80mm), 0.0591in. (1.50mm), 0.0787in. (2.0mm)

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Experimental results

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Experimental results

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Experimental results

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User interface

ECT user friendly interface

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Experimental results (angled crack)

angled crack sample

One important finding: The crack angle

could have relation with the ratio of upper

loop width and down side loop width,

which will be investigated in the future

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4. Conclusions and Future work

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Conclusions

The feature extraction algorism is able to process differential ECT signal

Trained fuzzy logic possess an accurate result of crack depth predication with proper tuning method

User interface is partial functional and is able to implement the feature extraction as well as the crack definition.

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Future work

• More data collection

Smaller cracks

Cracks with complicated crack geometry

Data from robotic scan system

• Theoretical model exploration

Impedance trajectory simulation

Data collection and training using theoretical modeling

• User interface improvement and implementation

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References

1. Takagi T., Bowler J.R., Yoshida Y., “Electromagnetic Nondestructive Evaluation”, Volume 1, IOS Press, 1997 .Smaller cracks

2. Wang, L. X. “A Course in Fuzzy systems and Control”, Prentice Hall PTR, 1997.

3. Jang, J.-S. R., “ANFIS: Adaptive-Network-based Fuzzy Inference Systems,” IEEE Transactions on Systems, Man, and Cybernetics, Vol. 23, No. 3, pp. 665-685, May 1993.

4. Garcia-Martin, J., Gomez-Gil, J. and Vazquez-Sanchez, E., "Non-Destructive Techniques Based on Eddy Current Testing", Sensors, vol.11, no.3, March 2011, pp. 2525-2565.

5. " Nortec 500Series Portable Eddy Current Flaw Detectors Operation Manual " , PN 7720140.00, Revision B, July 2013

6. " Olympus Eddy Current Probes “catalog

7. National Instruments Product spec. http://sine.ni.com/nips/cds/view/p/lang/en/nid/201987

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Thank you!

Q&A