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Lookup NU author(s): Dr Wenxian YangORCiD
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Continuous wavelet transforms (CWTs) are widely recognized as effectivetools for vibration-based machine fault diagnosis, as CWTs can detect both stationary and transitory signals. However, due to the problem of overlapping, a large amount of redundant information exists in the results that are generated by CWTs. The appearance of overlapping can smear the spectral features and make the results very difficult to interpret for machine operators. Misinterpretation of results may lead to false alarms or failures to detect anomalous signals. Moreover, as conventional CWTs only use a single mother wavelet to generate daughter wavelets, the distortion of the original signal in the resultant coefficients is inevitable. Obviously, this will significantly affect the accuracy in anomalous signal detection. To minimize the effect of overlapping and to enhance the accuracy of fault detection, a novelwavelet transform, which is named as exact wavelet analysis, has been designed for use in vibration-based machine fault diagnosis. The design of exact wavelet analysis is based on genetic algorithms. At each selected time frame, the algorithms will generate an adaptive daughterwavelet to match the inspected signal as exactly as possible. The optimization process of exact wavelet analysis is different from other adaptive wavelets as it considers both the optimization of waveletcoefficients and the satisfaction of the admissibility conditions of wavelets. The results obtained from simulated and practical experiments prove that exact wavelet analysis not only minimizes the undesirable effect of overlapping, but also helps operators to detect faults and distinguish the causes of faults. With the help from exact waveletanalysis, sudden shutdowns of production and services due to the fatal breakdown of machines could be avoided.
Author(s): Tse PW, Yang W, Tam HY
Publication type: Article
Publication status: Published
Journal: Journal of Sound and Vibration
Year: 2004
Volume: 277
Issue: 4-5
Pages: 1005-1024
ISSN (print): 0022-460X
ISSN (electronic): 1095-8568
Publisher: Elsvier Ltd.
URL: http://dx.doi.org/10.1016/j.jsv.2003.09.031
DOI: 10.1016/j.jsv.2003.09.031
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