IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v253y2025ics0951832024006069.html
   My bibliography  Save this article

Contrastive domain-invariant generalization for remaining useful life prediction under diverse conditions and fault modes

Author

Listed:
  • Xiao, Xiaoqi
  • Zhang, Jianguo
  • Xu, Dan

Abstract

As industrial equipment becomes increasingly complex, necessitating operation under varied conditions and often exhibiting diverse failure modes, traditional deep learning models built on data from the original environment become inapplicable. Moreover, in actual industrial scenarios, the generalization capability of Domain Adaptation and classic Domain Generalization methods is severely impacted when there is a lack of multiple source domain and target domain data, due to the cost or feasibility constraints associated with collecting extensive monitoring data. In this paper, a single domain Contrastive Domain-Invariant Generalization (CDIG) method for estimating the remaining useful life under different conditions and fault modes is proposed. This method first defines homologous signals as the foundational data. Subsequently, it learns domain-invariant features by encouraging two feature extraction processes to extract latent features of homologous signals as similarly as possible. Additionally, multiple condition-based attention, pooling, and a novel equalization loss function are utilized to regulate the generation of domain-invariant features. Ultimately, the RUL predictor is trained by source domain data, operational conditions, and temporal information to facilitate its applicability across diverse domains. Case studies demonstrate that CDIG achieves satisfactory predictive results under unseen conditions, highlighting the potential of the proposed method as an effective predictive tool.

Suggested Citation

  • Xiao, Xiaoqi & Zhang, Jianguo & Xu, Dan, 2025. "Contrastive domain-invariant generalization for remaining useful life prediction under diverse conditions and fault modes," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
  • Handle: RePEc:eee:reensy:v:253:y:2025:i:c:s0951832024006069
    DOI: 10.1016/j.ress.2024.110534
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832024006069
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2024.110534?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. Yan, Jianhai & He, Zhen & He, Shuguang, 2023. "Multitask learning of health state assessment and remaining useful life prediction for sensor-equipped machines," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    2. Huang, Xucong & Peng, Zhaoqin & Tang, Diyin & Chen, Juan & Zio, Enrico & Zheng, Zaiping, 2024. "A physics-informed autoencoder for system health state assessment based on energy-oriented system performance," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    3. Xu, Dan & Xiao, Xiaoqi & Liu, Jie & Sui, Shaobo, 2023. "Spatio-temporal degradation modeling and remaining useful life prediction under multiple operating conditions based on attention mechanism and deep learning," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    4. 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).
    5. 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).
    6. 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).
    7. Gao, Pengjie & Wang, Junliang & Shi, Ziqi & Ming, Weiwei & Chen, Ming, 2024. "Long-term temporal attention neural network with adaptive stage division for remaining useful life prediction of rolling bearings," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    8. Zhang, Yong & Xin, Yuqi & Liu, Zhi-wei & Chi, Ming & Ma, Guijun, 2022. "Health status assessment and remaining useful life prediction of aero-engine based on BiGRU and MMoE," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    9. 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).
    10. da Costa, Paulo Roberto de Oliveira & Akçay, Alp & Zhang, Yingqian & Kaymak, Uzay, 2020. "Remaining useful lifetime prediction via deep domain adaptation," Reliability Engineering and System Safety, Elsevier, vol. 195(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. Zhou, Liang & Wang, Huawei, 2024. "An adaptive multi-scale feature fusion and adaptive mixture-of-experts multi-task model for industrial equipment health status assessment and remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    2. Liu, Mingyuan & He, Wei & Ma, Ning & Zhu, Hailong & Zhou, Guohui, 2025. "A new reliability health status assessment model for complex systems based on belief rule base," Reliability Engineering and System Safety, Elsevier, vol. 254(PA).
    3. Guo, Chang & Shang, Zuogang & Ren, Jiaxin & Zhao, Zhibin & Ding, Baoqing & Wang, Shibin & Chen, Xuefeng, 2024. "CIS2N: Causal independence and sparse shift network for rotating machinery fault diagnosis in unseen domains," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    4. 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).
    5. Yan, Jianhai & Ye, Zhi-Sheng & He, Shuguang & He, Zhen, 2024. "A feature disentanglement and unsupervised domain adaptation of remaining useful life prediction for sensor-equipped machines," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    6. Yan, Jianhai & He, Zhen & He, Shuguang, 2023. "Multitask learning of health state assessment and remaining useful life prediction for sensor-equipped machines," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    7. Chen, Jiaxian & Li, Dongpeng & Huang, Ruyi & Chen, Zhuyun & Li, Weihua, 2023. "Aero-engine remaining useful life prediction method with self-adaptive multimodal data fusion and cluster-ensemble transfer regression," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    8. 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).
    9. Gao, Tianyu & Yang, Jingli & Wang, Wenmin & Fan, Xiaopeng, 2024. "A domain feature decoupling network for rotating machinery fault diagnosis under unseen operating conditions," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
    10. 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).
    11. 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).
    12. Azari, Mehdi Saman & Santini, Stefania & Edrisi, Farid & Flammini, Francesco, 2025. "Self-adaptive fault diagnosis for unseen working conditions based on digital twins and domain generalization," Reliability Engineering and System Safety, Elsevier, vol. 254(PA).
    13. Zhang, Zhiyao & Chen, Xiaohui & Zio, Enrico & Li, Longxiao, 2023. "Multi-task learning boosted predictions of the remaining useful life of aero-engines under scenarios of working-condition shift," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    14. Liang, Tao & Wang, Fuli & Wang, Shu & Li, Kang & Mo, Xuelei & Lu, Di, 2024. "Machinery health prognostic with uncertainty for mineral processing using TSC-TimeGAN," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    15. Zhang, Qing & Li, Shaochen & Chin-Hon, Tan & Liu, Xiaofei & Shen, Jingyuan & Shi, Tielin & Xuan, Jianping, 2025. "Fault Impulse Inference and Cyclostationary Approximation: A feature-interpretable intelligent fault detection method for few-shot unsupervised domain adaptation," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
    16. 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).
    17. Xiang, Sheng & Li, Penghua & Huang, Yi & Luo, Jun & Qin, Yi, 2024. "Single gated RNN with differential weighted information storage mechanism and its application to machine RUL prediction," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    18. Zhang, Yadong & Zhang, Chao & Wang, Shaoping & Dui, Hongyan & Chen, Rentong, 2024. "Health indicators for remaining useful life prediction of complex systems based on long short-term memory network and improved particle filter," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    19. Basora, Luis & Viens, Arthur & Chao, Manuel Arias & Olive, Xavier, 2025. "A benchmark on uncertainty quantification for deep learning prognostics," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
    20. Tong, Jianfeng & Liu, Zhenxing & Zhang, Yong & Zheng, Xiujuan & Jin, Junyang, 2023. "Improved multi-gate mixture-of-experts framework for multi-step prediction of gas load," Energy, Elsevier, vol. 282(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:s0951832024006069. 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.