A novel approach to wavelet selection and tree kernel construction for diagnosis of rolling element bearing fault

Chenxi Wu, Tefang Chen (), Rong Jiang, Liwei Ning and Zheng Jiang
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Chenxi Wu: Central South University
Tefang Chen: Central South University
Rong Jiang: Hunan Institute of Engineering
Liwei Ning: Hunan Institute of Engineering
Zheng Jiang: Wuhan University of Science and Technology

Journal of Intelligent Manufacturing, 2017, vol. 28, issue 8, No 6, 1847-1858

Abstract: Abstract A novel methodology for early diagnosis of rolling element bearing fault is employed based on continuous wavelet transform (CWT) and support vector machine (SVM). CWT is especially suited for analyzing non-stationary signals in time–frequency domain where time information is retained as well as frequency content. To better approximate non-stationary vibration signals from rolling element bearing, a wavelet choice criterion is established to select an appropriate mother wavelet for feature extraction. The Shannon wavelet is picked out of several considered wavelets. The classification tree kernels (CTK) are constructed to address nonlinear classification of the characteristic samples derived from the wavelet coefficients. By using Fuzzy pruning strategy, a large variety of classification trees are generated. The trees with diverse structures can effectively explore intrinsic information among samples. Then, the tree kernel matrices can be acquired through ensemble statistical learning, which eventually reveal the similarity of samples objectively and stably. Under such architecture of kernel methods, a classification tree kernel based support vector machine (CTKSVM) is proposed to identify bearing fault. The performance of the methodology involving CWT and CTKSVM (CWT–CTKSVM) is evaluated by cross validation and independent test. The results show that the CWT–CTKSVM totally is superior to other SVM methods with common kernels. Therefore, it is a prospective technique for detection and identification of rolling element bearing fault.

Keywords: Continuous wavelet transform; Classification tree kernel; Support vector machine; Fuzzy pruning strategy; Tree ensemble statistical learning (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s10845-015-1070-4

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