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Rolling Bearing Fault Prediction Method Based on QPSO-BP Neural Network and Dempster–Shafer Evidence Theory

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  • Lanjun Wan

    (School of Computer, Hunan University of Technology, Zhuzhou 412007, China
    Hunan Key Laboratory of Intelligent Information Perception and Processing Technology, Hunan University of Technology, Zhuzhou 412007, China)

  • Hongyang Li

    (School of Computer, Hunan University of Technology, Zhuzhou 412007, China
    Hunan Key Laboratory of Intelligent Information Perception and Processing Technology, Hunan University of Technology, Zhuzhou 412007, China)

  • Yiwei Chen

    (School of Computer, Hunan University of Technology, Zhuzhou 412007, China
    Hunan Key Laboratory of Intelligent Information Perception and Processing Technology, Hunan University of Technology, Zhuzhou 412007, China)

  • Changyun Li

    (Hunan Key Laboratory of Intelligent Information Perception and Processing Technology, Hunan University of Technology, Zhuzhou 412007, China)

Abstract

To effectively predict the rolling bearing fault under different working conditions, a rolling bearing fault prediction method based on quantum particle swarm optimization (QPSO) backpropagation (BP) neural network and Dempster–Shafer evidence theory is proposed. First, the original vibration signals of rolling bearing are decomposed by three-layer wavelet packet, and the eigenvectors of different states of rolling bearing are constructed as input data of BP neural network. Second, the optimal number of hidden-layer nodes of BP neural network is automatically found by the dichotomy method to improve the efficiency of selecting the number of hidden-layer nodes. Third, the initial weights and thresholds of BP neural network are optimized by QPSO algorithm, which can improve the convergence speed and classification accuracy of BP neural network. Finally, the fault classification results of multiple QPSO-BP neural networks are fused by Dempster–Shafer evidence theory, and the final rolling bearing fault prediction model is obtained. The experiments demonstrate that different types of rolling bearing fault can be effectively and efficiently predicted under various working conditions.

Suggested Citation

  • Lanjun Wan & Hongyang Li & Yiwei Chen & Changyun Li, 2020. "Rolling Bearing Fault Prediction Method Based on QPSO-BP Neural Network and Dempster–Shafer Evidence Theory," Energies, MDPI, vol. 13(5), pages 1-23, March.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:5:p:1094-:d:327100
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    References listed on IDEAS

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    1. Jianqiang Liu & Aifeng Chen & Nan Zhao, 2018. "An Intelligent Fault Diagnosis Method for Bogie Bearings of Metro Vehicles Based on Weighted Improved D-S Evidence Theory," Energies, MDPI, vol. 11(1), pages 1-21, January.
    2. Tengda Huang & Sheng Fu & Haonan Feng & Jiafeng Kuang, 2019. "Bearing Fault Diagnosis Based on Shallow Multi-Scale Convolutional Neural Network with Attention," Energies, MDPI, vol. 12(20), pages 1-19, October.
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    Cited by:

    1. Xiongchao Lin & Wenshuai Xi & Jinze Dai & Caihong Wang & Yonggang Wang, 2020. "Prediction of Slag Characteristics Based on Artificial Neural Network for Molten Gasification of Hazardous Wastes," Energies, MDPI, vol. 13(19), pages 1-18, October.
    2. Wagner Fontes Godoy & Daniel Morinigo-Sotelo & Oscar Duque-Perez & Ivan Nunes da Silva & Alessandro Goedtel & Rodrigo Henrique Cunha Palácios, 2020. "Estimation of Bearing Fault Severity in Line-Connected and Inverter-Fed Three-Phase Induction Motors," Energies, MDPI, vol. 13(13), pages 1-17, July.
    3. Min Yi & Wei Xie & Li Mo, 2021. "Short-Term Electricity Price Forecasting Based on BP Neural Network Optimized by SAPSO," Energies, MDPI, vol. 14(20), pages 1-17, October.
    4. Shijun Xu & Yi Hou & Xinpu Deng & Kewei Ouyang & Ye Zhang & Shilin Zhou, 2021. "Conflict Management for Target Recognition Based on PPT Entropy and Entropy Distance," Energies, MDPI, vol. 14(4), pages 1-25, February.

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