IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i5p1094-d327100.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/5/1094/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/5/1094/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    3. 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.
    4. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Jia Luo & Jingying Huang & Jiancheng Ma & Siyuan Liu, 2024. "Application of self-attention conditional deep convolutional generative adversarial networks in the fault diagnosis of planetary gearboxes," Journal of Risk and Reliability, , vol. 238(2), pages 260-273, April.
    3. Waseem El Sayed & Mostafa Abd El Geliel & Ahmed Lotfy, 2020. "Fault Diagnosis of PMSG Stator Inter-Turn Fault Using Extended Kalman Filter and Unscented Kalman Filter," Energies, MDPI, vol. 13(11), pages 1-24, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:5:p:1094-:d:327100. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.