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Remaining useful life prediction based on transfer multi-stage shrinkage attention temporal convolutional network under variable working conditions

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  • Li, Wanxiang
  • Shang, Zhiwu
  • Gao, Maosheng
  • Qian, Shiqi
  • Feng, Zehua

Abstract

Many data-driven remaining useful life (RUL) prediction methods usually assume that the training and test data are independent and identically distributed. However, the different degradation trends of machines under variable working conditions can lead to problems with disparate distribution of degradation features and difficulties in obtaining the corresponding labels. To address the above problems, this paper proposed a RUL prediction method based on a transfer multi-stage shrinkage attention temporal convolutional network under variable working conditions. Firstly, a shrinkage attention module is designed by using the attention mechanism and shrinkage operation to eliminate the interference of irrelevant information and increase the focus on critical features. Secondly, a multi-stage shrinkage attention temporal convolution block based on a hybrid attention subnetwork and soft thresholding subnetwork is designed to efficiently learn the manifold structure of the input data to capture the degenerate information-rich deep features. Finally, an unsupervised domain adaptation strategy based on representation subspace distance and bases mismatch penalization is proposed to enhance the learning of cross-domain invariant features. The proposed method is experimentally studied on XJTU-SY and FEMTO datasets. The experimental results demonstrate that the effectiveness and accuracy of the proposed method in RUL prediction are higher than other methods.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:reensy:v:226:y:2022:i:c:s0951832022003465
    DOI: 10.1016/j.ress.2022.108722
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    References listed on IDEAS

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    1. 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).
    2. 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).
    3. Li, Tianfu & Zhao, Zhibin & Sun, Chuang & Yan, Ruqiang & Chen, Xuefeng, 2021. "Hierarchical attention graph convolutional network to fuse multi-sensor signals for remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 215(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. Yan, Tao & Lei, Yaguo & Li, Naipeng & Wang, Biao & Wang, Wenting, 2021. "Degradation modeling and remaining useful life prediction for dependent competing failure processes," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    6. 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).
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    Citations

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    Cited by:

    1. 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).
    2. Yang, Shilong & Tang, Baoping & Wang, Weiying & Yang, Qichao & Hu, Cheng, 2024. "Physics-informed multi-state temporal frequency network for RUL prediction of rolling bearings," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    3. 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).
    4. Li, Yuan & Li, Jingwei & Wang, Huanjie & Liu, Chengbao & Tan, Jie, 2024. "Knowledge enhanced ensemble method for remaining useful life prediction under variable working conditions," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    5. 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).
    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).

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