IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v234y2023ics0951832023000571.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832023000571
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2023.109142?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Guan, Yang & Meng, Zong & Sun, Dengyun & Liu, Jingbo & Fan, Fengjie, 2021. "2MNet: Multi-sensor and multi-scale model toward accurate fault diagnosis of rolling bearing," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    2. Xia, Min & Shao, Haidong & Williams, Darren & Lu, Siliang & Shu, Lei & de Silva, Clarence W., 2021. "Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    3. Rose, Rodrigo L. & Puranik, Tejas G. & Mavris, Dimitri N. & Rao, Arjun H., 2022. "Application of structural topic modeling to aviation safety data," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    4. Hyang-A Park & Gilsung Byeon & Wanbin Son & Hyung-Chul Jo & Jongyul Kim & Sungshin Kim, 2020. "Digital Twin for Operation of Microgrid: Optimal Scheduling in Virtual Space of Digital Twin," Energies, MDPI, vol. 13(20), pages 1-15, October.
    5. Theissler, Andreas & Pérez-Velázquez, Judith & Kettelgerdes, Marcel & Elger, Gordon, 2021. "Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    6. Guo, Jianchun & Si, Zetian & Liu, Yi & Li, Jiahao & Li, Yanting & Xiang, Jiawei, 2022. "Dynamic time warping using graph similarity guided symplectic geometry mode decomposition to detect bearing faults," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    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. 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. Zio, Enrico & Miqueles, Leonardo, 2024. "Digital twins in safety analysis, risk assessment and emergency management," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    3. Li, Xin & Li, Shuhua & Wei, Dong & Si, Lei & Yu, Kun & Yan, Ke, 2024. "Dynamics simulation-driven fault diagnosis of rolling bearings using security transfer support matrix machine," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    4. 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).
    5. 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).
    6. Dong, Yutong & Jiang, Hongkai & Yao, Renhe & Mu, Mingzhe & Yang, Qiao, 2024. "Rolling bearing intelligent fault diagnosis towards variable speed and imbalanced samples using multiscale dynamic supervised contrast learning," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    7. Chaleshtori, Amir Eshaghi & Aghaie, Abdollah, 2024. "A novel bearing fault diagnosis approach using the Gaussian mixture model and the weighted principal component analysis," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    8. Zhao, Zhigao & Chen, Fei & He, Xianghui & Lan, Pengfei & Chen, Diyi & Yin, Xiuxing & Yang, Jiandong, 2024. "A universal hydraulic-mechanical diagnostic framework based on feature extraction of abnormal on-field measurements: Application in micro pumped storage system," Applied Energy, Elsevier, vol. 357(C).

    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. Xu, Yadong & Yan, Xiaoan & Sun, Beibei & Liu, Zheng, 2022. "Global contextual residual convolutional neural networks for motor fault diagnosis under variable-speed conditions," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    2. Tan, Hongchuang & Xie, Suchao & Ma, Wen & Yang, Chengxing & Zheng, Shiwei, 2023. "Correlation feature distribution matching for fault diagnosis of machines," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    3. Guo, Jianchun & Si, Zetian & Liu, Yi & Li, Jiahao & Li, Yanting & Xiang, Jiawei, 2022. "Dynamic time warping using graph similarity guided symplectic geometry mode decomposition to detect bearing faults," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    4. Xu, Yadong & Yan, Xiaoan & Sun, Beibei & Liu, Zheng, 2022. "Dually attentive multiscale networks for health state recognition of rotating machinery," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    5. Guo, Junchao & He, Qingbo & Zhen, Dong & Gu, Fengshou & Ball, Andrew D., 2023. "Multi-sensor data fusion for rotating machinery fault detection using improved cyclic spectral covariance matrix and motor current signal analysis," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    6. Ma, Chenyang & Wang, Xianzhi & Li, Yongbo & Cai, Zhiqiang, 2024. "Broad zero-shot diagnosis for rotating machinery with untrained compound faults," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    7. Zhao, Chao & Shen, Weiming, 2022. "Dual adversarial network for cross-domain open set fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    8. Liu, Shaowei & Jiang, Hongkai & Wu, Zhenghong & Yi, Zichun & Wang, Ruixin, 2023. "Intelligent fault diagnosis of rotating machinery using a multi-source domain adaptation network with adversarial discrepancy matching," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    9. Pan, Yongjun & Sun, Yu & Li, Zhixiong & Gardoni, Paolo, 2023. "Machine learning approaches to estimate suspension parameters for performance degradation assessment using accurate dynamic simulations," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    10. Shi, Yaowei & Deng, Aidong & Deng, Minqiang & Xu, Meng & Liu, Yang & Ding, Xue & Li, Jing, 2022. "Transferable adaptive channel attention module for unsupervised cross-domain fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    11. Dong, Yutong & Jiang, Hongkai & Wu, Zhenghong & Yang, Qiao & Liu, Yunpeng, 2023. "Digital twin-assisted multiscale residual-self-attention feature fusion network for hypersonic flight vehicle fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    12. Zhu, Zuanyu & Cheng, Junsheng & Wang, Ping & Wang, Jian & Kang, Xin & Yang, Yu, 2023. "A novel fault diagnosis framework for rotating machinery with hierarchical multiscale symbolic diversity entropy and robust twin hyperdisk-based tensor machine," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    13. Wang, Chu & Dou, Manfeng & Li, Zhongliang & Outbib, Rachid & Zhao, Dongdong & Zuo, Jian & Wang, Yuanlin & Liang, Bin & Wang, Peng, 2023. "Data-driven prognostics based on time-frequency analysis and symbolic recurrent neural network for fuel cells under dynamic load," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    14. Zhang, Qing & Tang, Lv & Xuan, Jianping & Shi, Tielin & Li, Rui, 2023. "An uncertainty relevance metric-based domain adaptation fault diagnosis method to overcome class relevance caused confusion," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    15. Zio, Enrico & Miqueles, Leonardo, 2024. "Digital twins in safety analysis, risk assessment and emergency management," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    16. Rachid Darbali-Zamora & Jay Johnson & Adam Summers & C. Birk Jones & Clifford Hansen & Chad Showalter, 2021. "State Estimation-Based Distributed Energy Resource Optimization for Distribution Voltage Regulation in Telemetry-Sparse Environments Using a Real-Time Digital Twin," Energies, MDPI, vol. 14(3), pages 1-21, February.
    17. Wang, Chang & Zheng, Jianqin & Liang, Yongtu & Wang, Bohong & Klemeš, Jiří Jaromír & Zhu, Zhu & Liao, Qi, 2022. "Deeppipe: An intelligent monitoring framework for operating condition of multi-product pipelines," Energy, Elsevier, vol. 261(PB).
    18. Zhao, Chao & Shen, Weiming, 2022. "Adaptive open set domain generalization network: Learning to diagnose unknown faults under unknown working conditions," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    19. Li, Xin & Yang, Yu & Wu, Zhantao & Yan, Ke & Shao, Haidong & Cheng, Junsheng, 2022. "High-accuracy gearbox health state recognition based on graph sparse random vector functional link network," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    20. Eduardo Gómez-Luna & John E. Candelo-Becerra & Juan C. Vasquez, 2023. "A New Digital Twins-Based Overcurrent Protection Scheme for Distributed Energy Resources Integrated Distribution Networks," Energies, MDPI, vol. 16(14), pages 1-23, July.

    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:eee:reensy:v:234:y:2023:i:c:s0951832023000571. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    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.