Intelligent fault diagnosis of rolling element bearings using sparse wavelet energy based on overcomplete DWT and basis pursuit

Cong Wang (), Meng Gan () and Chang’an Zhu
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Cong Wang: University of Science and Technology of China (USTC)
Meng Gan: University of Science and Technology of China (USTC)
Chang’an Zhu: University of Science and Technology of China (USTC)

Journal of Intelligent Manufacturing, 2017, vol. 28, issue 6, No 9, 1377-1391

Abstract: Abstract This paper explores sparse time-frequency distribution (TFD) using overcomplete discrete wavelet transform (DWT) and sparse representation techniques. This distribution is discovered for characterizing the periodic transient information embedded in rolling element bearings and extracting effective features that can discriminate different fault conditions. Based on the sparse TFD, a new sparse wavelet energy (SWE) feature is obtained by three main steps: first, an overcomplete discrete DWT is employed to decompose the fault signal and construct a redundant dictionary; second, the redundant dictionary is optimized by basis pursuit to obtain the sparsest TFD; finally, SWE is calculated from the new TFD to produce a feature vector for each signal. SWE features that combine the merits of overcomplete DWT and sparse representation techniques can precisely reveal fault-induced information, thereby exhibiting valuable properties for automatic fault identification by intelligent classifiers. The effectiveness and advantages of the proposed features are confirmed by simulation and the practical fault pattern recognition of rolling bearings.

Keywords: Overcomplete discrete wavelet transform (DWT); Basis pursuit; Sparse wavelet energy (SWE); Intelligent fault diagnosis (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (7)

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DOI: 10.1007/s10845-015-1056-2

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