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Online bearing fault diagnosis using numerical simulation models and machine learning classifications

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  • Wang, Hui
  • Zheng, Junkang
  • Xiang, Jiawei

Abstract

Digital twin (DT) is the embodiment of the most advanced achievements of the current simulation technology theory development and the direction of intelligent development in the future. However, it is a great challenge to really integrate it into practical project application. Motivated by DT, an application method combining numerical simulation model and machine learning classification is proposed to show the advantages of digital twin. To ensure the reliability of the twin model, it is necessary to build a simulation model using a mature dynamic model, and modify it through the Pearson correlation coefficient (PCC) which is a kind of model online learning. Then, the required fault type is introduced by modifying the relevant fault influence factors, which is synchronously inserted into the normal operation model to obtain the normal, fault and other simulation numerical data. Finally, the machine learning model is used to predict the probability of each fault and feedback the impact value to the actual operation to guide the adjustment of actual parameters and the determination of maintenance plans. The experimental results show that this method can effectively predict the possibility of bearing failure synchronously and guide the adjustment and maintenance of actual bearing operating parameters.

Suggested Citation

  • Wang, Hui & Zheng, Junkang & Xiang, Jiawei, 2023. "Online bearing fault diagnosis using numerical simulation models and machine learning classifications," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    • Handle: RePEc:eee:reensy:v:234:y:2023:i:c:s0951832023000571
    DOI: 10.1016/j.ress.2023.109142
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    References listed on IDEAS

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    Cited by:

    1. Hu, Kui & He, Qingbo & Cheng, Changming & Peng, Zhike, 2024. "Adaptive incremental diagnosis model for intelligent fault diagnosis with dynamic weight correction," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    2. Yan, Shen & Zhong, Xiang & Shao, Haidong & Ming, Yuhang & Liu, Chao & Liu, Bin, 2023. "Digital twin-assisted imbalanced fault diagnosis framework using subdomain adaptive mechanism and margin-aware regularization," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    3. Zhao, Ke & Hu, Junchen & Shao, Haidong & Hu, Jiabei, 2023. "Federated multi-source domain adversarial adaptation framework for machinery fault diagnosis with data privacy," Reliability Engineering and System Safety, Elsevier, vol. 236(C).

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