IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v253y2025ics0951832024006434.html
   My bibliography  Save this article

Generalized zero-sample industrial fault diagnosis with domain bias

Author

Listed:
  • Cai, Li
  • Deng, Xuanhong
  • Yin, Hongpeng
  • Lin, Jingdong
  • Qin, Yan

Abstract

Generalized zero-sample fault diagnosis (GZSFD) is a challenging task involving the diagnosis of all samples from both previously seen and unseen faults. However, the scarcity of unseen samples for training causes that existing methods are hindered by domain bias, where unseen faults are more likely to be misclassified as seen faults. In this article, an efficacious solution is proposed by constructing an unseen fault detector for test samples in GZSFD with domain bias, which utilizes the detected unseen-sample knowledge to enhance the diagnosis performance. Specifically, a ResNet-based one-dimensional convolutional neural network is designed for high-quality feature extraction. Also, a Kullback–Leibler divergence-based distribution threshold detector is constructed for the identification of test samples. Afterwards, test samples are detected and distinguished into seen or unseen classes. In detected unseen classes, a zero-sample fault diagnosis (ZSFD) problem is undertaken, while in detected seen classes, a sub-GZSFD problem is addressed. For ZSFD tasks, to leverage the unseen samples in the test set, a clustering-based scheme without a predefined cluster number is used for the detected unseen fault. For sub-GZSFD tasks, combined with classification results in the ZSFD task, two embedding strategies are proposed to further mitigate the domain bias. They learn a shared weight and the optimal weights of semantic attributes from the feature space to the semantic embedding space, respectively. Using the shared fine-grained semantic attribute descriptions as auxiliary information, the final fault category can be determined. Experimental results showcase that the proposed strategies effectively alleviate the domain bias in GZSFD tasks.

Suggested Citation

  • Cai, Li & Deng, Xuanhong & Yin, Hongpeng & Lin, Jingdong & Qin, Yan, 2025. "Generalized zero-sample industrial fault diagnosis with domain bias," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
  • Handle: RePEc:eee:reensy:v:253:y:2025:i:c:s0951832024006434
    DOI: 10.1016/j.ress.2024.110571
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832024006434
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2024.110571?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. 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).
    2. Tian, Jilun & Jiang, Yuchen & Zhang, Jiusi & Luo, Hao & Yin, Shen, 2024. "A novel data augmentation approach to fault diagnosis with class-imbalance problem," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    3. Chen, Xu & Zhao, Chunhui & Ding, Jinliang, 2023. "Pyramid-type zero-shot learning model with multi-granularity hierarchical attributes for industrial fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 240(C).
    4. Bai, Ruxue & Meng, Zong & Xu, Quansheng & Fan, Fengjie, 2023. "Fractional Fourier and time domain recurrence plot fusion combining convolutional neural network for bearing fault diagnosis under variable working conditions," Reliability Engineering and System Safety, Elsevier, vol. 232(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. Wang, Haoyu & Li, Chuanjiang & Ding, Peng & Li, Shaobo & Li, Tandong & Liu, Chenyu & Zhang, Xiangjie & Hong, Zejian, 2024. "A novel transformer-based few-shot learning method for intelligent fault diagnosis with noisy labels under varying working conditions," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    2. Zhang, Guowei & Kong, Xianguang & Wang, Qibin & Du, Jingli & Wang, Jinrui & Ma, Hongbo, 2024. "Single domain generalization method based on anti-causal learning for rotating machinery fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    3. Ma, Hongbo & Wei, Jiacheng & Zhang, Guowei & Kong, Xianguang & Du, Jingli, 2024. "Causality-inspired multi-source domain generalization method for intelligent fault diagnosis under unknown operating conditions," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
    4. Yu, Tian & Li, Chaoshun & Huang, Jie & Xiao, Xiangqu & Zhang, Xiaoyuan & Li, Yuhong & Fu, Bitao, 2024. "ReF-DDPM: A novel DDPM-based data augmentation method for imbalanced rolling bearing fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    5. 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).
    6. Zhou, Haoxuan & Wang, Bingsen & Zio, Enrico & Wen, Guangrui & Liu, Zimin & Su, Yu & Chen, Xuefeng, 2023. "Hybrid system response model for condition monitoring of bearings under time-varying operating conditions," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    7. Li, Qikang & Tang, Baoping & Deng, Lei & Yang, Qichao & Zhu, Peng, 2024. "Adaptive centroid prototype-based domain adaptation for fault diagnosis of rotating machinery without source data," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    8. Li, Ying & Zhang, Lijie & Liang, Pengfei & Wang, Xiangfeng & Wang, Bin & Xu, Leitao, 2024. "Semi-supervised meta-path space extended graph convolution network for intelligent fault diagnosis of rotating machinery under time-varying speeds," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    9. Li, Duowei & Wong, Yiik Diew & Chen, Tianyi & Wang, Nanxi & Yuen, Kum Fai, 2024. "An ensemble method for investigating maritime casualties resulting in pollution occurrence: Data augmentation and feature analysis," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    10. Zhang, Jiusi & Tian, Jilun & Yan, Pengfei & Wu, Shimeng & Luo, Hao & Yin, Shen, 2024. "Multi-hop graph pooling adversarial network for cross-domain remaining useful life prediction: A distributed federated learning perspective," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    11. Zhao, Shuaiyu & Duan, Yiling & Roy, Nitin & Zhang, Bin, 2024. "A deep learning methodology based on adaptive multiscale CNN and enhanced highway LSTM for industrial process fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    12. Lin, Yanzhuo & Wang, Yu & Zhang, Mingquan & Zhao, Ming, 2025. "A robust source-free unsupervised domain adaptation method based on uncertainty measure and adaptive calibration for rotating machinery fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
    13. Wu, Zhangjun & Xu, Renli & Luo, Yuansheng & Shao, Haidong, 2024. "A holistic semi-supervised method for imbalanced fault diagnosis of rotational machinery with out-of-distribution samples," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    14. Fu, Xingchen & Jiao, Keming & Tao, Jianfeng & Liu, Chengliang, 2024. "Multi-stream domain adversarial prototype network for integrated smart roller TBM main bearing fault diagnosis across various low rotating speeds," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    15. Li, Gang & Hu, Jiayao & Ding, Yaping & Tang, Aimin & Ao, Jiaxing & Hu, Dalong & Liu, Yang, 2024. "A novel method for fault diagnosis of fluid end of drilling pump under complex working conditions," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    16. 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).
    17. Chen, Xirui & Liu, Hui, 2025. "Domain correction for hydraulic internal pump leakage detection considering multiclass aberrant flow data," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
    18. Liu, Mengyu & Cheng, Zhe & Yang, Yu & Hu, Niaoqing & Yang, Yi, 2024. "Multi-target domain adaptation intelligent diagnosis method for rotating machinery based on multi-source attention mechanism and mixup feature augmentation," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    19. Zhou, Tao & Yao, Dechen & Yang, Jianwei & Meng, Chang & Li, Ankang & Li, Xi, 2024. "DRSwin-ST: An intelligent fault diagnosis framework based on dynamic threshold noise reduction and sparse transformer with Shifted Windows," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    20. Su, Zhiheng & Lian, Penglong & Shang, Penghui & Zhang, Jiyang & Xu, Hongbing & Zou, Jianxiao & Fan, Shicai, 2024. "Semi-supervised source-free domain adaptation method via diffusive label propagation for rotating machinery fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 252(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:253:y:2025:i:c:s0951832024006434. 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.