School of Business, James Cook University, Cairns, Queensland 4870, Australia

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Abstract—A-scans from ultrasonic testing of long shafts are complex signals, thus the discrimination of different types of echoes is of importance for non-destructive testing and equipment maintenance. Research has focused on selecting features of physical significance or exploring classifier like Artificial Neural Networks and Support Vector Machines. This paper summarizes and reports on our comprehensive exploration on efficient feature extraction schemes and classifiers for shaft testing system and further on the diverse possibilities of heterogeneous and homogeneous ensembles.

Index Terms—Signal Classification, Non-Destructive Testing, Signal Feature Extraction

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Cite: Kyungmi Lee, " Exploration on Feature Extraction Schemes and Classifiers for Shaft Testing System," Journal of Computers vol. 5, no. 5, pp. 679-686, 2010.