IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v239y2023ics0951832023004040.html
   My bibliography  Save this article

Machinery cross domain degradation prognostics considering compound domain shifts

Author

Listed:
  • Ding, Peng
  • Zhao, Xiaoli
  • Shao, Haidong
  • Jia, Minping

Abstract

Nowadays, data-driven based decision-making mode significantly promotes machinery prognostics and health management (PHM), but are also profoundly affected by domain shift problems. As a promising transfer learning methodology, domain adaptation has matured in machinery fault classifications under variable operating conditions and achieved tremendous success in aligning data distribution discrepancies among multiple domains, called inter-domain shifts in this study. However the monitoring signal presents nonlinear and non-stationary characteristics when the machine is degraded, its period-level and chronological discrepancy within the given set of monitoring time series is often ignored. This intra discrepancy is not conducive to maintaining the time series consistency and may fail to build an accurate prediction model. Therefore, this study, for the first time, formulas this kind of shift and comprehensively resolves the compound shifts originating from inter and intra domains. Firstly, the domain-invariant degradation indicator is constructed through the designed double adversarial learning based multi-source domain adaptation module. Then the obtained indicators are segmented into distinct degradation periods according to outlier detections for subsequent intra-domain level alignments, quantifying as the regularization term for matching the discrepancies between degradation periods. Finally, real-measured run-to-failed experiments are applied to test the effectiveness of our proposed method.

Suggested Citation

  • Ding, Peng & Zhao, Xiaoli & Shao, Haidong & Jia, Minping, 2023. "Machinery cross domain degradation prognostics considering compound domain shifts," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
  • Handle: RePEc:eee:reensy:v:239:y:2023:i:c:s0951832023004040
    DOI: 10.1016/j.ress.2023.109490
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832023004040
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2023.109490?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. Zhang, Wei & Wang, Ziwei & Li, Xiang, 2023. "Blockchain-based decentralized federated transfer learning methodology for collaborative machinery fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    2. Fu, Song & Zhang, Yongjian & Lin, Lin & Zhao, Minghang & Zhong, Shi-sheng, 2021. "Deep residual LSTM with domain-invariance for remaining useful life prediction across domains," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    3. Fallahdizcheh, Amirhossein & Wang, Chao, 2022. "Transfer learning of degradation modeling and prognosis based on multivariate functional analysis with heterogeneous sampling rates," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    4. 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).
    5. Zhang, Jiusi & Li, Xiang & Tian, Jilun & Jiang, Yuchen & Luo, Hao & Yin, Shen, 2023. "A variational local weighted deep sub-domain adaptation network for remaining useful life prediction facing cross-domain condition," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    6. Zhuang, Jichao & Jia, Minping & Ding, Yifei & Ding, Peng, 2021. "Temporal convolution-based transferable cross-domain adaptation approach for remaining useful life estimation under variable failure behaviors," Reliability Engineering and System Safety, Elsevier, vol. 216(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. Xiong, Jiawei & Zhou, Jian & Ma, Yizhong & Zhang, Fengxia & Lin, Chenglong, 2023. "Adaptive deep learning-based remaining useful life prediction framework for systems with multiple failure patterns," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    2. 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).
    3. 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).
    4. 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).
    5. Nejjar, Ismail & Geissmann, Fabian & Zhao, Mengjie & Taal, Cees & Fink, Olga, 2024. "Domain adaptation via alignment of operation profile for Remaining Useful Lifetime prediction," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    6. Chen, Dingliang & Qin, Yi & Qian, Quan & Wang, Yi & Liu, Fuqiang, 2023. "Transfer life prediction of gears by cross-domain health indicator construction and multi-hierarchical long-term memory augmented network," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    7. Zhuang, Jichao & Jia, Minping & Zhao, Xiaoli, 2022. "An adversarial transfer network with supervised metric for remaining useful life prediction of rolling bearing under multiple working conditions," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    8. 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).
    9. Lin Lin & Jie Liu & Feng Guo & Changsheng Tong & Lizheng Zu & Hao Guo, 2022. "ERDERP: Entity and Relation Double Embedding on Relation Hyperplanes and Relation Projection Hyperplanes," Mathematics, MDPI, vol. 10(22), pages 1-19, November.
    10. Yi Lyu & Qichen Zhang & Zhenfei Wen & Aiguo Chen, 2022. "Remaining Useful Life Prediction Based on Multi-Representation Domain Adaptation," Mathematics, MDPI, vol. 10(24), pages 1-18, December.
    11. Li, Wanxiang & Shang, Zhiwu & Gao, Maosheng & Qian, Shiqi & Feng, Zehua, 2022. "Remaining useful life prediction based on transfer multi-stage shrinkage attention temporal convolutional network under variable working conditions," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    12. Hyunsoo Kim & Jiseok Jeong & Changwan Kim, 2022. "Daily Peak-Electricity-Demand Forecasting Based on Residual Long Short-Term Network," Mathematics, MDPI, vol. 10(23), pages 1-17, November.
    13. Wang, Chu & Dou, Manfeng & Li, Zhongliang & Outbib, Rachid & Zhao, Dongdong & Zuo, Jian & Wang, Yuanlin & Liang, Bin & Wang, Peng, 2023. "Data-driven prognostics based on time-frequency analysis and symbolic recurrent neural network for fuel cells under dynamic load," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    14. Fu, Song & Lin, Lin & Wang, Yue & Guo, Feng & Zhao, Minghang & Zhong, Baihong & Zhong, Shisheng, 2024. "MCA-DTCN: A novel dual-task temporal convolutional network with multi-channel attention for first prediction time detection and remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    15. 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).
    16. Hyewon Lee & Izaz Raouf & Jinwoo Song & Heung Soo Kim & Soobum Lee, 2023. "Prognostics and Health Management of the Robotic Servo-Motor under Variable Operating Conditions," Mathematics, MDPI, vol. 11(2), pages 1-17, January.
    17. Fan, Linchuan & Chai, Yi & Chen, Xiaolong, 2022. "Trend attention fully convolutional network for remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    18. Lyu, Dongzhen & Niu, Guangxing & Liu, Enhui & Zhang, Bin & Chen, Gang & Yang, Tao & Zio, Enrico, 2022. "Time space modelling for fault diagnosis and prognosis with uncertainty management: A general theoretical formulation," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    19. Liu, Yuanhong & Shi, Baoxin & Lu, Shixiang & Gao, Zhi-Wei & Zhang, Fangfang, 2024. "A novel local linear embedding algorithm via local mutual representation for bearing fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    20. Song, Wanqing & Duan, Shouwu & Zio, Enrico & Kudreyko, Aleksey, 2022. "Multifractional and long-range dependent characteristics for remaining useful life prediction of cracking gas compressor," Reliability Engineering and System Safety, Elsevier, vol. 225(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:239:y:2023:i:c:s0951832023004040. 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.