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A modified SOM method based on nonlinear neural weight updating for bearing fault identification in variable speed condition

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Abstract

Fault identification for bearings of special electromechanical equipment is significant to avoid catastrophic accidents. However, spectral aliasing and nonstationarity resulted from variable speed condition make this task difficult. In this paper, a modified self-organizing maps (SOM) based on nonlinear neural weight updating way is proposed to solve the problem of bearing fault severity identification in variable speed condition. Firstly, a multi-domain features extraction method based on angular re-sampling technique is introduced. Then considering the nonlinear relationship between fault severity and fault features, the traditional Euclidian distance of SOM is substituted with the geodesic distance when update the neural weight and select the best-matching cell, which can improve the nonlinear identification ability of proposed method. Finally, two cases are performed and the results show that the method can identify bearing fault with different severities effectively and have practical significance when considering both accuracy and time cost compared with other methods.

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Acknowledgments

This research is supported financially by the National Key Research and Development Program of China (No. 2017YFC0805701), National Natural Science Foundation of China (No. 51775411, No. 61633001), National Science and Technology Major Project of China (No. 2017ZX04011013), China Postdoctoral Science Foundation (No. 2018M631145) and Shaanxi Natural Science Foundation (No. 2019JM-041). The authors would like to sincerely thank all the anonymous reviewers for the valuable comments that greatly helped to improve the manuscript.

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Authors and Affiliations

  1. School of Mechanical Engineering, Xi’an Jiaotong University, Xi‘an, 710049, China

    Zitong Zhou, Jinglong Chen, Yanyang Zi & Tong An

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  1. Zitong Zhou
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  2. Jinglong Chen
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Correspondence to Jinglong Chen.

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Recommended by Editor No-cheol Park

Zitong Zhou received the B.S. degree in Mechanical Engineering from Xi’an Jiaotong University, Xi’an, China, in 2014. He is currently working towards the Ph.D. degree in Mechanical Engineering at Xi’an Jiaotong University. His research interests include condition monitoring of mechanical equipment, and mechanical signal processing.

Jinglong Chen received the Ph.D. degrees in Mechanical Engineering from Xi’an Jiaotong University, Xi’an, China, in 2014. He is currently an Associate Professor of Mechanical Engineering at Xi’an Jiaotong University. His research interests focus on sensor-dependent vibration data processing, machinery condition monitoring and fault diagnosis, mechanical signal processing, and mechanical system reliability.

Yanyang Zi received the Ph.D. degrees in Mechanical Engineering from Xi’an Jiaotong University, Xi’an, China, in 2001. He is currently a Professor of Mechanical Engineering at Xi’an Jiaotong University. His research interests focus on machinery condition monitoring and fault diagnosis, mechanical signal processing, and mechanical system reliability.

Tong An received the B.S. degree in Mechanical Engineering from Southeast University, Nanjing, China, in 2016. He is currently working towards the Master degree in Mechanical Engineering at Xi’an Jiaotong University. His research interests include safety assessment.

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Zhou, Z., Chen, J., Zi, Y. et al. A modified SOM method based on nonlinear neural weight updating for bearing fault identification in variable speed condition. J Mech Sci Technol 34, 1901–1912 (2020). https://doi.org/10.1007/s12206-020-0412-0

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  • DOI: https://doi.org/10.1007/s12206-020-0412-0

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