Feature extraction techniques for ultrasonic shaft signal classification
Lee, Kyungmi, and Estivill-Castro, Vladimir (2003) Feature extraction techniques for ultrasonic shaft signal classification. In: Proceedings of Third International Conference on Hybrid Intelligent System (HIS 2003) Frontiers in Artificial Intelligence and Applications (104), pp. 479-488. From: Third International Conference on Hybrid Intelligent System (HIS 2003), 14-17 December 2003, Melbourne, VIC, Australia.
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Discrete Wavelet Transform (DWT) coefficients of ultrasonic test signals are considered useful features for input into classifiers due to their effective time-frequency representation of non-stationary signals. However, DWT exhibits a time-variance problem that has resulted in reservations for, its wide acceptance. In this paper, a new technique to derive a preprocessing method for time-domain A-scans signal is presented. This techniques offer consistent extraction of a segment of the signal from long signals that occur in the Non Destructive Testing of shafts. We performed a comparison using artificial neural networks and evaluated classification performance of the classifier supplied with features generated by our method. We compared with other alternatives and report the results here. We established experimentally that DWT coefficients can be used as a feature extraction scheme more reliably by using our new preprocessing technique.
|Item Type:||Conference Item (Refereed Research Paper - E1)|
|Date Deposited:||12 Jul 2010 04:17|
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