IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v241y2024ics0951832023006191.html
   My bibliography  Save this article

Adaptive incremental diagnosis model for intelligent fault diagnosis with dynamic weight correction

Author

Listed:
  • Hu, Kui
  • He, Qingbo
  • Cheng, Changming
  • Peng, Zhike

Abstract

Intelligent fault diagnosis (IFD) has become a research hotspot in the fields of prognostics and health management. Existing mechanical IFD methods cannot continuously learn and integrate new diagnostic knowledge. In engineering, new fault data is continuously collected over time, and it is costly to retrain IFD models when new fault mode data arrives. To solve this problem, this paper proposes a new adaptive incremental diagnosis model (AIDM) with incremental capabilities. The AIDM is composed of a feature extraction module, an exemplar library, and a series of linear classifiers. By adding new output nodes and adopting knowledge distillation loss, the quick reconstruction and updating of AIDM can be realized on the premise of avoiding catastrophic forgetting. In addition, to solve the stability-plasticity dilemma, a new dynamic weight correction algorithm is proposed to dynamically adjust the biased weight of different linear classifiers. In this way, the stable and reliable incremental training and dynamic updating of IFD models are realized. Finally, the proposed method is verified on bearings and gearboxes. The results show that the proposed AIDM has outstanding performance in incremental diagnosis tasks, which provides a new solution for the adaptive updating of the IFD model.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:241:y:2024:i:c:s0951832023006191
    DOI: 10.1016/j.ress.2023.109705
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832023006191
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2023.109705?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. Shi, Yaowei & Deng, Aidong & Deng, Minqiang & Xu, Meng & Liu, Yang & Ding, Xue & Bian, Wenbin, 2023. "Domain augmentation generalization network for real-time fault diagnosis under unseen working conditions," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    2. 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).
    3. 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).
    4. Wang, Jinrui & Zhang, Zongzhen & Liu, Zhiliang & Han, Baokun & Bao, Huaiqian & Ji, Shanshan, 2023. "Digital twin aided adversarial transfer learning method for domain adaptation fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    Full references (including those not matched with items on IDEAS)

    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. Zheng, Xiaorong & Nie, Jiahao & He, Zhiwei & Li, Ping & Dong, Zhekang & Gao, Mingyu, 2024. "A fine-grained feature decoupling based multi-source domain adaptation network for rotating machinery fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 243(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. Tian, Jilun & Zhang, Jiusi & Jiang, Yuchen & Wu, Shimeng & Luo, Hao & Yin, Shen, 2024. "A novel generalized source-free domain adaptation approach for cross-domain industrial fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    4. Liu, Jiale & Wang, Huan, 2024. "A brain-inspired energy-efficient Wide Spiking Residual Attention Framework for intelligent fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    5. Zio, Enrico & Miqueles, Leonardo, 2024. "Digital twins in safety analysis, risk assessment and emergency management," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    6. Deng, Congying & Deng, Zihao & Miao, Jianguo, 2024. "Semi-supervised ensemble fault diagnosis method based on adversarial decoupled auto-encoder with extremely limited labels," Reliability Engineering and System Safety, Elsevier, vol. 242(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. Liu, Jianing & Cao, Hongrui & Luo, Yang, 2023. "An information-induced fault diagnosis framework generalizing from stationary to unknown nonstationary working conditions," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    9. 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).
    10. 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).
    11. Wang, Jun & Ren, He & Shen, Changqing & Huang, Weiguo & Zhu, Zhongkui, 2024. "Multi-scale style generative and adversarial contrastive networks for single domain generalization fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    12. 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).
    13. 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).
    14. Wang, Chenxi & Zhang, Yuxiang & Zhao, Zhibin & Chen, Xuefeng & Hu, Jiawei, 2024. "Dynamic model-assisted transferable network for liquid rocket engine fault diagnosis using limited fault samples," Reliability Engineering and System Safety, Elsevier, vol. 243(C).

    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:241:y:2024:i:c:s0951832023006191. 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.