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

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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.

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Acknowledgments

The work was supported by National High Technology Research and Development Program of China (2009AA11Z217). The authors would like to thank the anonymous reviewers for spending their valuable time to review.

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Authors and Affiliations

  1. School of Information Science and Engineering, Central South University, Changsha, 410075, Hunan, People’s Republic of China

    Chenxi Wu & Tefang Chen

  2. School of Mechanical Engineering, Hunan Institute of Engineering, Xiangtan, 411101, Hunan, People’s Republic of China

    Chenxi Wu, Rong Jiang & Liwei Ning

  3. School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan, 430081, Hubei, People’s Republic of China

    Zheng Jiang

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  1. Chenxi Wu
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  2. Tefang Chen
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  3. Rong Jiang
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  5. Zheng Jiang
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Correspondence to Tefang Chen.

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Wu, C., Chen, T., Jiang, R. et al. A novel approach to wavelet selection and tree kernel construction for diagnosis of rolling element bearing fault. J Intell Manuf 28, 1847–1858 (2017). https://doi.org/10.1007/s10845-015-1070-4

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  • DOI: https://doi.org/10.1007/s10845-015-1070-4

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