Journal
MEASUREMENT
Volume 46, Issue 4, Pages 1551-1564Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.measurement.2012.12.011
Keywords
Wavelet packet paving; Statistical parameters; Distance evaluation technique; Support vector regression; Machine fault diagnosis
Funding
- Natural Science Foundation of China [51075379]
- Research Grants Council of the Hong Kong Special Administrative Region, China [CityU 122011]
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The fault diagnosis of rotating machinery has attracted considerable research attention in recent years because such components as bearings and gears frequently suffer failure, resulting in unexpected machine breakdowns. Signal processing-based condition monitoring and fault diagnosis methods have proved effective in fault identification, but the revelation of faults from the resulting signals requires a high degree of expertise. In addition, it is difficult to extract the fault-induced signatures in complex machinery via signal processing-based methods. In this paper, a new intelligent fault diagnosis scheme based on the extraction of statistical parameters from the paving of a wavelet packet transform (WPT), a distance evaluation technique (DET) and a support vector regression (SVR)-based generic multi-class solver is proposed. The collected signals are first pre-processed by the WPT at different decomposition depths. In this paper, the wavelet packet coefficients at different decomposition depths are referred to as WPT paving. Statistical parameters are then extracted from the signals obtained via the WPT at different decomposition depths. In selecting the sensitive fault features for fault pattern expression, a DET is employed to reduce the dimensionality of the feature space. Finally, a SVR-based generic multi-class solver is proposed to identify the different fault patterns of rotating machinery. The effectiveness of the proposed intelligent fault diagnosis scheme is validated separately using datasets from bearing and gearbox test rigs. In addition, the effects of different wavelet basis functions on the performance of the proposed scheme are investigated experimentally. The results demonstrate that the proposed intelligent fault diagnosis scheme is highly accurate in differentiating the fault patterns of both bearings and gears. (C) 2013 Elsevier Ltd. All rights reserved.
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