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Research on Rolling Bearing On-Line Fault Diagnosis Based on Multi-dimensional Feature Extraction

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Communications, Signal Processing, and Systems (CSPS 2018)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 516))

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Abstract

In the paper, a novel rolling bearing fault diagnostic method was proposed to fulfill the requirements for effective assessment of different fault types and severities with real-time computational performance. Firstly, multi-dimensional feature extraction is discussed. And secondly, a gray relation algorithm was used to acquire basic belief assignments. Finally, the basic belief assignments were fused through Yager algorithm. The related experimental study has illustrated the proposed method can effectively and efficiently recognize various fault types and severities.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (61771154) and funding of State Key Laboratory of CEMEE (CEMEE2018K0104A).

Meantime, all the authors declare that there is no conflict of interests regarding the publication of this article.

We gratefully thank of very useful discussions of reviewers.

Author information

Authors and Affiliations

  1. College of Information and Communication Engineering, Harbin Engineering University, Harbin, China

    Tianwen Zhang

Authors
  1. Tianwen Zhang
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Corresponding author

Correspondence to Tianwen Zhang .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA

    Qilian Liang

  2. School of Information and Communication Engineering, Dalian University of Technology, Dalian, China

    Xin Liu

  3. School of Information Science and Technology, Dalian Maritime University, Dalian, China

    Zhenyu Na

  4. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Wei Wang

  5. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Jiasong Mu

  6. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Baoju Zhang

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Zhang, T. (2020). Research on Rolling Bearing On-Line Fault Diagnosis Based on Multi-dimensional Feature Extraction. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 516. Springer, Singapore. https://doi.org/10.1007/978-981-13-6504-1_116

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  • DOI: https://doi.org/10.1007/978-981-13-6504-1_116

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-6503-4

  • Online ISBN: 978-981-13-6504-1

  • eBook Packages: EngineeringEngineering (R0)

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