IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v226y2022ics0951832022003064.html
   My bibliography  Save this article

Adaptive open set domain generalization network: Learning to diagnose unknown faults under unknown working conditions

Author

Listed:
  • Zhao, Chao
  • Shen, Weiming

Abstract

Recently, domain generalization techniques have been introduced to enhance the generalization capacity of fault diagnostic models under unknown working conditions. Most existing studies assume consistent machine health states between the training and testing data. However, fault modes in the testing phase are unpredictable, and unknown fault modes usually occur, hindering the wide applications of domain generalization-based fault diagnosis methods in industries. To address such problems, this paper proposes an adaptive open set domain generalization network to diagnose unknown faults under unknown working conditions. A local class cluster module is implemented to explore domain-invariant representation space and obtain discriminative representation structures by minimizing triplet loss. An outlier detection module learns optimal decision boundaries for individual class representation spaces to classify known fault modes and recognize unknown fault modes. Extensive experimental results on two test rigs demonstrated the effectiveness and superiority of the proposed method.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:226:y:2022:i:c:s0951832022003064
    DOI: 10.1016/j.ress.2022.108672
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832022003064
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2022.108672?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. Wang, Xu & Shen, Changqing & Xia, Min & Wang, Dong & Zhu, Jun & Zhu, Zhongkui, 2020. "Multi-scale deep intra-class transfer learning for bearing fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    3. Deng, Minqiang & Deng, Aidong & Shi, Yaowei & Liu, Yang & Xu, Meng, 2022. "A novel sub-label learning mechanism for enhanced cross-domain fault diagnosis of rotating machinery," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    4. Wang, Han & Liao, Haitao & Ma, Xiaobing & Bao, Rui, 2021. "Remaining Useful Life Prediction and Optimal Maintenance Time Determination for a Single Unit Using Isotonic Regression and Gamma Process Model," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    5. Zhou, Taotao & Han, Te & Droguett, Enrique Lopez, 2022. "Towards trustworthy machine fault diagnosis: A probabilistic Bayesian deep learning framework," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    6. Zhao, Chao & Shen, Weiming, 2022. "Dual adversarial network for cross-domain open set fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    7. Wu, Jingyao & Zhao, Zhibin & Sun, Chuang & Yan, Ruqiang & Chen, Xuefeng, 2021. "Learning from Class-imbalanced Data with a Model-Agnostic Framework for Machine Intelligent Diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    8. Lee, Jinwook & Kim, Myungyon & Ko, Jin Uk & Jung, Joon Ha & Sun, Kyung Ho & Youn, Byeng D., 2022. "Asymmetric inter-intra domain alignments (AIIDA) method for intelligent fault diagnosis of rotating machinery," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    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. 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).
    2. Rombach, Katharina & Michau, Gabriel & Fink, Olga, 2023. "Controlled generation of unseen faults for Partial and Open-Partial domain adaptation," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    3. Zhang, Xingwu & Zhao, Yu & Yu, Xiaolei & Ma, Rui & Wang, Chenxi & Chen, Xuefeng, 2023. "Weighted domain separation based open set fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    4. Yu, Xiaolei & Zhao, Zhibin & Zhang, Xingwu & Chen, Xuefeng & Cai, Jianbing, 2023. "Statistical identification guided open-set domain adaptation in fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    5. 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).
    6. Ma, Yulin & Yang, Jun & Li, Lei, 2023. "Gradient aligned domain generalization with a mutual teaching teacher-student network for intelligent fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    7. Wang, Jianyu & Zeng, Zhiguo & Zhang, Heng & Barros, Anne & Miao, Qiang, 2022. "An hybrid domain adaptation diagnostic network guided by curriculum pseudo labels for electro-mechanical actuator," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    8. 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).
    9. 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).
    10. Zhao, Zeyun & Wang, Jia & Tao, Qian & Li, Andong & Chen, Yiyang, 2024. "An unknown wafer surface defect detection approach based on Incremental Learning for reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    11. Tong Zhang & Haowen Chen & Xianqun Mao & Xin Zhu & Lefei Xu, 2024. "A Domain Generation Diagnosis Framework for Unseen Conditions Based on Adaptive Feature Fusion and Augmentation," Mathematics, MDPI, vol. 12(18), pages 1-15, September.

    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. 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).
    2. Yu, Xiaolei & Zhao, Zhibin & Zhang, Xingwu & Chen, Xuefeng & Cai, Jianbing, 2023. "Statistical identification guided open-set domain adaptation in fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    3. 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).
    4. Rombach, Katharina & Michau, Gabriel & Fink, Olga, 2023. "Controlled generation of unseen faults for Partial and Open-Partial domain adaptation," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    5. 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).
    6. Ma, Yulin & Li, Lei & Yang, Jun, 2022. "Convolutional kernel aggregated domain adaptation for intelligent fault diagnosis with label noise," Reliability Engineering and System Safety, Elsevier, vol. 227(C).
    7. 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).
    8. Zhao, Zeyun & Wang, Jia & Tao, Qian & Li, Andong & Chen, Yiyang, 2024. "An unknown wafer surface defect detection approach based on Incremental Learning for reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    9. 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).
    10. Zhao, Chao & Shen, Weiming, 2022. "Dual adversarial network for cross-domain open set fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    11. Zhang, Xingwu & Zhao, Yu & Yu, Xiaolei & Ma, Rui & Wang, Chenxi & Chen, Xuefeng, 2023. "Weighted domain separation based open set fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    12. Chen, Pengfei & Zhao, Rongzhen & He, Tianjing & Wei, Kongyuan & Yuan, Jianhui, 2023. "A novel bearing fault diagnosis method based joint attention adversarial domain adaptation," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    13. 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).
    14. 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).
    15. Han, Te & Li, Yan-Fu, 2022. "Out-of-distribution detection-assisted trustworthy machinery fault diagnosis approach with uncertainty-aware deep ensembles," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    16. Feng, Tingting & Li, Shichao & Guo, Liang & Gao, Hongli & Chen, Tao & Yu, Yaoxiang, 2023. "A degradation-shock dependent competing failure processes based method for remaining useful life prediction of drill bit considering time-shifting sudden failure threshold," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    17. Zuo, Lin & Xu, Fengjie & Zhang, Changhua & Xiahou, Tangfan & Liu, Yu, 2022. "A multi-layer spiking neural network-based approach to bearing fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    18. Li, Qi & Chen, Liang & Kong, Lin & Wang, Dong & Xia, Min & Shen, Changqing, 2023. "Cross-domain augmentation diagnosis: An adversarial domain-augmented generalization method for fault diagnosis under unseen working conditions," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    19. 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).
    20. Park, Chan Hee & Kim, Hyeongmin & Suh, Chaehyun & Chae, Minseok & Yoon, Heonjun & Youn, Byeng D., 2022. "A health image for deep learning-based fault diagnosis of a permanent magnet synchronous motor under variable operating conditions: Instantaneous current residual map," Reliability Engineering and System Safety, Elsevier, vol. 226(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:226:y:2022:i:c:s0951832022003064. 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.