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Bearing Fault Diagnosis with Impulsive Noise Based on EMD and Cyclic Correntropy

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Green, Smart and Connected Transportation Systems

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 617))

Abstract

Periodic pulses are an important fault feature of rolling bearings, so the ability to accurately and efficiently identify pulse components is important for bearing fault diagnosis. Due to the complicated wheel-rail contact relationship in actual train operation, it often generates many impulse noises which similar to the fault signal structure. Unfortunately, spectral kurtosis (SK) methods often fail to effectively diagnose under impulse noise. In order to solve this problem, this paper proposes a bearing fault diagnosis method based on Empirical Mode Decomposition (EMD) and cyclic correntropy (CCE) function. Compared with the SK method, the method proposed in this paper can effectively suppress the influence of impulse noise. Moreover, this paper also proposes a fault diagnosis evaluation index \( KR_{s} \) to quantitatively compare the diagnostic effects of different methods. Simulations and real data of the train axle are utilized to demonstrate the feasibility and effectiveness of the proposed method and index.

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Acknowledgements

This research is supported by the National Key Research and Development Program of China (Grant No. 2016YFB1200505-014), National Natural Science Foundation of China (Grant No. 61833002).

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Authors and Affiliations

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

    Yu-Ze Wang, Yong Qin, Xue-Jun Zhao, Shun-Jie Zhang & Xiao-Qing Cheng

  2. School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China

    Yu-Ze Wang, Xue-Jun Zhao & Shun-Jie Zhang

Authors
  1. Yu-Ze Wang
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  2. Yong Qin
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  3. Xue-Jun Zhao
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  4. Shun-Jie Zhang
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  5. Xiao-Qing Cheng
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Corresponding author

Correspondence to Yong Qin .

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Editors and Affiliations

  1. Beijing Institute of Technology, Beijing, China

    Wuhong Wang

  2. Ulm, Baden-Württemberg, Germany

    Martin Baumann

  3. Beijing Institute of Technology, Beijing, China

    Xiaobei Jiang

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Wang, YZ., Qin, Y., Zhao, XJ., Zhang, SJ., Cheng, XQ. (2020). Bearing Fault Diagnosis with Impulsive Noise Based on EMD and Cyclic Correntropy. In: Wang, W., Baumann, M., Jiang, X. (eds) Green, Smart and Connected Transportation Systems. Lecture Notes in Electrical Engineering, vol 617. Springer, Singapore. https://doi.org/10.1007/978-981-15-0644-4_112

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  • DOI: https://doi.org/10.1007/978-981-15-0644-4_112

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-0643-7

  • Online ISBN: 978-981-15-0644-4

  • eBook Packages: EngineeringEngineering (R0)

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