Neural pattern identification of railroad wheel-bearing faults from audible acoustic signals: comparison of FFT, CWT, and DWT features

Howard C. Choe; Yulun Wan; Andrew K. Chan

doi:10.1117/12.271772

3 April 1997 Neural pattern identification of railroad wheel-bearing faults from audible acoustic signals: comparison of FFT, CWT, and DWT features

Howard C. Choe, Yulun Wan, Andrew K. Chan

Author Affiliations +

Proceedings Volume 3078, Wavelet Applications IV; (1997) https://doi.org/10.1117/12.271772
Event: AeroSense '97, 1997, Orlando, FL, United States

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.

Citation Download Citation

Howard C. Choe, Yulun Wan, and Andrew K. Chan "Neural pattern identification of railroad wheel-bearing faults from audible acoustic signals: comparison of FFT, CWT, and DWT features", Proc. SPIE 3078, Wavelet Applications IV, (3 April 1997); https://doi.org/10.1117/12.271772

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