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Bearing Fault Diagnosis Using Multiclass Self-Adaptive Support Vector Classifiers Based on CEEMD–SVD

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Abstract

Bearing fault diagnosis under variable conditions has become a research hotspot recently. To solve this problem, this paper presents a new classifier: multiclass self-adaptive support vector classifier (MSa-SVC). Firstly, self-adaptive SVC is created by combination of SVC and information geometry. Then, several binary Sa-SVCs are constructed as a multiclass classifier for fault diagnosis. The proposed MSa-SVC, in conjunction with complementary ensemble empirical mode decomposition (CEEMD) and singular value decomposition (SVD) is utilized for bearing fault diagnosis: (1) each signal is processed into singular features by CEEMD–SVD. (2) MSa-SVC is used for fault clustering under variable conditions. Finally, the proposed method was applied on bearing fault diagnosis in practice. The results show that this method provides an efficient approach for bearing fault diagnosis under variable conditions.

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Acknowledgements

This research is supported by the National Key R&D Program of China (No. 2016YFB1200203), State Key Laboratory of Rail Traffic Control and Safety (Contract Nos. RCS2016ZQ003 and RCS2016ZT018), as well as National Engineering Laboratory for System Safety and Operation Assurance of Urban Rail Transit.

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Authors and Affiliations

  1. State Key Lab of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China

    Zhipeng Wang, Limin Jia & Yong Qin

  2. National Engineering Laboratory for System Safety and Operation Assurance of Urban Rail Transit, Guangzhou, Guangdong, China

    Zhipeng Wang, Limin Jia & Yong Qin

  3. Beijing Research Center of Urban Traffic Information Sensing and Service Technologies, Beijing Jiaotong University, Beijing, China

    Zhipeng Wang, Limin Jia & Yong Qin

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  1. Zhipeng Wang
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  2. Limin Jia
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Correspondence to Zhipeng Wang.

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Wang, Z., Jia, L. & Qin, Y. Bearing Fault Diagnosis Using Multiclass Self-Adaptive Support Vector Classifiers Based on CEEMD–SVD. Wireless Pers Commun 102, 1669–1682 (2018). https://doi.org/10.1007/s11277-017-5226-8

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  • DOI: https://doi.org/10.1007/s11277-017-5226-8

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