Classification of ultrasonic shaft inspection data using discrete wavelet transform
Lee, Kyungmi, and Estivill-Castro, Vladimir (2003) Classification of ultrasonic shaft inspection data using discrete wavelet transform. In: Proceedings of the (AIA 2003) Artificial Intelligence and Applications Conference, pp. 673-678. From: (AIA 2003) Artificial Intelligence and Applications Conferences, 8-10 September, 2003, Benalmádena, Spain.
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Artificial Neural Networks have been used to process ultrasonic signals for many non-destructive scenarios. However, this scenarios usually involve very shallow surfaces. When testing shafts, the signals are long and the new problem of mode-converted reflections emerges. They are echoes that do not correspond to cracks in the material, neither to characteristics of the shaft. Also, the length of the signals demands the application of feature extraction mechanism to reduce the dimension of the pattern vectors and make classifier training feasible. The results here establish experimentally that DWT provides faster and more reliable feature extraction for ANN in these long signals in shafts. This results match the recent studies for shallow signals where comparisons between FFT and DWT indicate DWT as the preferred feature extraction policy.
|Item Type:||Conference Item (Refereed Research Paper - E1)|
|Keywords:||neural networks; learning algorithms and training; wavelets; signal processing applications in engineering|
|Date Deposited:||12 Jul 2010 04:59|
|FoR Codes:||08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation @ 40%
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