IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v221y2022ics0951832022000370.html
   My bibliography  Save this article

Dual adversarial network for cross-domain open set fault diagnosis

Author

Listed:
  • Zhao, Chao
  • Shen, Weiming

Abstract

Recently, cross-domain fault diagnosis methods have been successfully developed and applied. Among them, the ones exhibiting the best performance rely on the common assumption that the training and testing data share an identical label space, implying that fault modes are the same in different engineering scenarios. However, fault modes in the testing phase are unpredictable and new fault modes usually occur, posing challenges for existing cross-domain methods regarding their effectiveness. To address such challenges, a novel open set domain adaptation network based on dual adversarial learning is proposed in this study. An auxiliary domain discriminator assigns similarity weights for individual target samples to distinguish between known and unknown fault modes, and weighted adversarial learning is employed to selectively adapt domain distributions. In separated adversarial learning, the feature generator and the extended classifier are set against each other to construct more accurate hyperplanes between known and unknown fault modes. Comprehensive experimental results for three test rigs demonstrate that the proposed method achieves a promising performance and outperforms existing state-of-the-art open set domain adaptation methods.

Suggested Citation

  • Zhao, Chao & Shen, Weiming, 2022. "Dual adversarial network for cross-domain open set fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
  • Handle: RePEc:eee:reensy:v:221:y:2022:i:c:s0951832022000370
    DOI: 10.1016/j.ress.2022.108358
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832022000370
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2022.108358?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. 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).
    4. 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).
    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. 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).
    2. 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).
    3. 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).
    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. 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).
    6. 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).
    7. 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).
    8. 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).
    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. Yu, Aobo & Cai, Bolin & Wu, Qiujie & García, Miguel Martínez & Li, Jing & Chen, Xiangcheng, 2024. "Source-free domain adaptation method for fault diagnosis of rotation machinery under partial information," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    12. 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).
    13. Zhang, Yongchao & Ji, J.C. & Ren, Zhaohui & Ni, Qing & Gu, Fengshou & Feng, Ke & Yu, Kun & Ge, Jian & Lei, Zihao & Liu, Zheng, 2023. "Digital twin-driven partial domain adaptation network for intelligent fault diagnosis of rolling bearing," Reliability Engineering and System Safety, Elsevier, vol. 234(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. 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).
    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. 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).
    4. 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).
    5. 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).
    6. 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).
    7. 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).
    8. 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).
    9. 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).
    10. 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).
    11. 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).
    12. 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).
    13. 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).
    14. Tang, Shengnan & Zhu, Yong & Yuan, Shouqi, 2022. "Intelligent fault identification of hydraulic pump using deep adaptive normalized CNN and synchrosqueezed wavelet transform," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    15. Gao, Dawei & Huang, Kai & Zhu, Yongsheng & Zhu, Linbo & Yan, Ke & Ren, Zhijun & Guedes Soares, C., 2024. "Semi-supervised small sample fault diagnosis under a wide range of speed variation conditions based on uncertainty analysis," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    16. Ding, Yifei & Jia, Minping & Zhuang, Jichao & Cao, Yudong & Zhao, Xiaoli & Lee, Chi-Guhn, 2023. "Deep imbalanced domain adaptation for transfer learning fault diagnosis of bearings under multiple working conditions," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    17. 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).
    18. 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).
    19. Xu, Yadong & Yan, Xiaoan & Feng, Ke & Zhang, Yongchao & Zhao, Xiaoli & Sun, Beibei & Liu, Zheng, 2023. "Global contextual multiscale fusion networks for machine health state identification under noisy and imbalanced conditions," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    20. Yin, Zhenqin & Zhuo, Yue & Ge, Zhiqiang, 2023. "Transfer adversarial attacks across industrial intelligent systems," Reliability Engineering and System Safety, Elsevier, vol. 237(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:221:y:2022:i:c:s0951832022000370. 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.