IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v225y2022ics0951832022002277.html
   My bibliography  Save this article

The two-stage RUL prediction across operation conditions using deep transfer learning and insufficient degradation data

Author

Listed:
  • Cheng, Han
  • Kong, Xianguang
  • Wang, Qibin
  • Ma, Hongbo
  • Yang, Shengkang

Abstract

The remaining useful life (RUL) prediction provides an essential basis for improving mechanical equipment reliability. In practical application, the variant of working conditions and incomplete degradation data seriously deteriorate the performance of the prognostic models. In order to conquer this problem, a two-stage RUL prediction method is proposed for the cross-domain prognostic task with insufficient degradation data. At first, the two-level alarm mechanism is employed to detect the first predicting time (FPT) of each mechanical entity adaptively. Then, the deep separable convolutional network with the double transferable attention mechanism (DSCN-DTAM) is proposed to construct the cross-domain prognostic model. In DSCN-DTAM, multiple regularization strategies can guide the model to extract domain-invariant features, and the double transferable attention mechanism is designed to select the degradation information with high transferability. Finally, the proposed method is verified by multiple transfer prognostic tasks designed by two bearing datasets. Compared with other methods, the proposed method shows superior performance.

Suggested Citation

  • Cheng, Han & Kong, Xianguang & Wang, Qibin & Ma, Hongbo & Yang, Shengkang, 2022. "The two-stage RUL prediction across operation conditions using deep transfer learning and insufficient degradation data," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    • Handle: RePEc:eee:reensy:v:225:y:2022:i:c:s0951832022002277
    DOI: 10.1016/j.ress.2022.108581
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832022002277
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2022.108581?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. Ahmad, Wasim & Khan, Sheraz Ali & Islam, M M Manjurul & Kim, Jong-Myon, 2019. "A reliable technique for remaining useful life estimation of rolling element bearings using dynamic regression models," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 67-76.
    2. Li, Xiang & Zhang, Wei & Ding, Qian, 2019. "Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 208-218.
    3. Zhang, Wei & Li, Xiang & Ma, Hui & Luo, Zhong & Li, Xu, 2021. "Transfer learning using deep representation regularization in remaining useful life prediction across operating conditions," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    4. Shi, Zunya & Chehade, Abdallah, 2021. "A dual-LSTM framework combining change point detection and remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    5. 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).
    6. Ding, Yifei & Jia, Minping & Miao, Qiuhua & Huang, Peng, 2021. "Remaining useful life estimation using deep metric transfer learning for kernel regression," Reliability Engineering and System Safety, Elsevier, vol. 212(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. Ma, Shuaiyin & Huang, Yuming & Liu, Yang & Kong, Xianguang & Yin, Lei & Chen, Gaige, 2023. "Edge-cloud cooperation-driven smart and sustainable production for energy-intensive manufacturing industries," Applied Energy, Elsevier, vol. 337(C).
    2. Yang, Jing & Wang, Xiaomin, 2024. "Meta-learning with deep flow kernel network for few shot cross-domain remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    3. 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).
    4. Cheng, Han & Kong, Xianguang & Wang, Qibin & Ma, Hongbo & Yang, Shengkang & Xu, Kun, 2023. "Remaining useful life prediction combined dynamic model with transfer learning under insufficient degradation data," Reliability Engineering and System Safety, Elsevier, vol. 236(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. 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. Wang, Yuan & Lei, Yaguo & Li, Naipeng & Yan, Tao & Si, Xiaosheng, 2023. "Deep multisource parallel bilinear-fusion network for remaining useful life prediction of machinery," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    3. Xiang, Sheng & Qin, Yi & Luo, Jun & Pu, Huayan & Tang, Baoping, 2021. "Multicellular LSTM-based deep learning model for aero-engine remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    4. Xu, Danyang & Qiu, Haobo & Gao, Liang & Yang, Zan & Wang, Dapeng, 2022. "A novel dual-stream self-attention neural network for remaining useful life estimation of mechanical systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    5. Arias Chao, Manuel & Kulkarni, Chetan & Goebel, Kai & Fink, Olga, 2022. "Fusing physics-based and deep learning models for prognostics," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    6. Zio, Enrico, 2022. "Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    7. Xiao, Lei & Tang, Junxuan & Zhang, Xinghui & Bechhoefer, Eric & Ding, Siyi, 2021. "Remaining useful life prediction based on intentional noise injection and feature reconstruction," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    8. Wei, Yupeng & Wu, Dazhong & Terpenny, Janis, 2024. "Remaining useful life prediction using graph convolutional attention networks with temporal convolution-aware nested residual connections," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    9. Li, Yuanfu & Chen, Yao & Hu, Zhenchao & Zhang, Huisheng, 2023. "Remaining useful life prediction of aero-engine enabled by fusing knowledge and deep learning models," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    10. 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).
    11. 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.
    12. 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).
    13. 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).
    14. 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).
    15. Zhu, Yongmeng & Wu, Jiechang & Wu, Jun & Liu, Shuyong, 2022. "Dimensionality reduce-based for remaining useful life prediction of machining tools with multisensor fusion," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    16. Bae, Jinwoo & Xi, Zhimin, 2022. "Learning of physical health timestep using the LSTM network for remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    17. 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).
    18. 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).
    19. Cao, Yudong & Ding, Yifei & Jia, Minping & Tian, Rushuai, 2021. "A novel temporal convolutional network with residual self-attention mechanism for remaining useful life prediction of rolling bearings," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    20. 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).

    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:225:y:2022:i:c:s0951832022002277. 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.