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Proceedings Paper

Neural pattern identification of railroad wheel-bearing faults from audible acoustic signals: comparison of FFT, CWT, and DWT features
Author(s): Howard C. Choe; Yulun Wan; Andrew K. Chan
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Paper Abstract

Current railroad wayside Hot Bearing Detector systems were developed in the 1960s to identify failing friction bearings. While the electronics used in these systems have been upgraded to microprocessor technology, the basic detection principles have not changed over the last 30 years. In this paper, we present a novel method to detect, recognize, and classify a variety of railroad wheel-bearing defects using audible acoustic signals at several different train speeds. Our algorithm consists of a data preprocessor, a feature extractor, and a single multilayer neural network. The feature extractor can use any one of four different transforms to generate feature vectors from input acoustic data: the fast Fourier transform (FFT), the continuous wavelet transform, the discrete wavelet transform, and the wavelet packet. The classification performance using each feature vector type is presented. This algorithm can be applied to many kinds of bearings in rotational machinery to perform nondestructive fault detection and identification.

Paper Details

Date Published: 3 April 1997
PDF: 17 pages
Proc. SPIE 3078, Wavelet Applications IV, (3 April 1997); doi: 10.1117/12.271772
Show Author Affiliations
Howard C. Choe, Texas A&M Univ. (United States)
Yulun Wan, Texas A&M Univ. (United States)
Andrew K. Chan, Texas A&M Univ. (United States)


Published in SPIE Proceedings Vol. 3078:
Wavelet Applications IV
Harold H. Szu, Editor(s)

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