Feature extraction techniques for ultrasonic shaft signal classification

Authors:
Kyungmi Lee;Vladimir Estivill-Castro
Affiliations:
School of Computing and Information Technology, Griffith University, Nathan, QLD 4111, Australia;School of Computing and Information Technology, Griffith University, Nathan, QLD 4111, Australia
Venue:
Design and application of hybrid intelligent systems
Year:
2003

Citing 2
Cited 1

Feature Extraction From Wavelet Coefficients for Pattern Recognition Tasks

IEEE Transactions on Pattern Analysis and Machine Intelligence
An intelligent simulation methodology to characterize defects in materials

Information Sciences: an International Journal

A hybrid classification approach to ultrasonic shaft signals

AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence

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Abstract

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.