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A Fault Diagnosis Method of Rolling Bearing Based on Attention Entropy and Adaptive Deep Kernel Extreme Learning Machine

Author

Listed:
  • Weiyu Wang

    (Wuling Power Corporation Ltd., Changsha 410004, China
    Hydropower Industry Innovation Center of State Power Investment Corporation Limited, Changsha 410004, China)

  • Xunxin Zhao

    (Wuling Power Corporation Ltd., Changsha 410004, China
    Hydropower Industry Innovation Center of State Power Investment Corporation Limited, Changsha 410004, China)

  • Lijun Luo

    (Wuling Power Corporation Ltd., Changsha 410004, China
    Hydropower Industry Innovation Center of State Power Investment Corporation Limited, Changsha 410004, China)

  • Pei Zhang

    (Wuling Power Corporation Ltd., Changsha 410004, China
    Hydropower Industry Innovation Center of State Power Investment Corporation Limited, Changsha 410004, China)

  • Fan Mo

    (Wuling Power Corporation Ltd., Changsha 410004, China
    Hydropower Industry Innovation Center of State Power Investment Corporation Limited, Changsha 410004, China)

  • Fei Chen

    (Department of Power and Electrical Engineering, Northwest A&F University, Xianyang 712100, China)

  • Diyi Chen

    (Department of Power and Electrical Engineering, Northwest A&F University, Xianyang 712100, China)

  • Fengjiao Wu

    (Department of Power and Electrical Engineering, Northwest A&F University, Xianyang 712100, China)

  • Bin Wang

    (Department of Power and Electrical Engineering, Northwest A&F University, Xianyang 712100, China)

Abstract

To address the difficulty of early fault diagnosis of rolling bearings, this paper proposes a rolling bearing diagnosis method by combining the attention entropy and adaptive deep kernel extreme learning machine (ADKELM). Firstly, the wavelet threshold denoising method is employed to eliminate the noise in the vibration signal. Then, the empirical wavelet transform (EWT) is utilized to decompose the denoised signal, and extract the attention entropy of the intrinsic mode function (IMF) as the feature vector. Next, the hyperparameters of the deep kernel extreme learning machine (DKELM) are optimized using the marine predators algorithm (MPA), so as to achieve the adaptive changes in the DKELM parameters. By analyzing the fault diagnosis performances of the ADKELM model with different kernel functions and hidden layers, the optimal ADKELM model is determined. Compared with conventional machine learning models such as extreme learning machine (ELM), back propagation neural network (BPNN) and probabilistic neural network (PNN), the high efficiency of the method proposed in this paper is verified.

Suggested Citation

  • Weiyu Wang & Xunxin Zhao & Lijun Luo & Pei Zhang & Fan Mo & Fei Chen & Diyi Chen & Fengjiao Wu & Bin Wang, 2022. "A Fault Diagnosis Method of Rolling Bearing Based on Attention Entropy and Adaptive Deep Kernel Extreme Learning Machine," Energies, MDPI, vol. 15(22), pages 1-19, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8423-:d:969200
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    References listed on IDEAS

    as
    1. Yao, Lei & Fang, Zhanpeng & Xiao, Yanqiu & Hou, Junjian & Fu, Zhijun, 2021. "An Intelligent Fault Diagnosis Method for Lithium Battery Systems Based on Grid Search Support Vector Machine," Energy, Elsevier, vol. 214(C).
    2. Shifei Ding & Nan Zhang & Xinzheng Xu & Lili Guo & Jian Zhang, 2015. "Deep Extreme Learning Machine and Its Application in EEG Classification," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-11, May.
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