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Generalized sparse filtering for rotating machinery fault diagnosis

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Abstract

This paper develops generalized sparse filtering (GSF) by applying general norm normalization to improve the feature learning ability. A rotating machinery fault diagnosis method is then developed by combining the GSF and softmax regression. A rolling bearing dataset is applied to validate the performance of the developed method. The influences of normalization parameters on the diagnostic performance are investigated in detail, and thus, the best parameter combinations are determined based on the diagnostic accuracy and computing time. A planetary gearbox dataset is also applied to further validate the diagnostic performance on rotating machinery. Finally, the mechanism of the GSF is explained using a simple example. The results show that the GSF has a more powerful feature learning capacity than standard sparse filtering, and the developed method can obtain excellent diagnostic performance. Two variants of the developed method are recommended for the rotating machinery fault diagnosis.

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Acknowledgements

The authors thank Nanjing University of Aeronautics and Astronautics for providing the gearbox data. This work was supported in part by the National Natural Science Foundation of China (Grant No. 51605191), the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (Grant No. 18KJD460003), the Natural Science Research Foundation of Jiangsu Normal University (Grant No. 18XLRS009), and Jiangsu Government Scholarship for Studying Abroad.

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Authors and Affiliations

  1. School of Mechatronic Engineering, Jiangsu Normal University, Xuzhou, 221116, China

    Chun Cheng & Yan Hu

  2. Department of Mechanical Engineering, CALCE, Center for Advanced Life Cycle Engineering, University of Maryland, College Park, MD, 20740, USA

    Chun Cheng, Haining Liu & Michael Pecht

  3. College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao, 266590, China

    Jinrui Wang

  4. School of Electrical Engineering, University of Jinan, Jinan, 250022, China

    Haining Liu

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  1. Chun Cheng
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  2. Yan Hu
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  3. Jinrui Wang
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  4. Haining Liu
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  5. Michael Pecht
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Correspondence to Chun Cheng.

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Cheng, C., Hu, Y., Wang, J. et al. Generalized sparse filtering for rotating machinery fault diagnosis. J Supercomput 77, 3402–3421 (2021). https://doi.org/10.1007/s11227-020-03398-5

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  • DOI: https://doi.org/10.1007/s11227-020-03398-5

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