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Deep transfer learning based on Bi-LSTM and attention for remaining useful life prediction of rolling bearing

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  • Dong, Shaojiang
  • Xiao, Jiafeng
  • Hu, Xiaolin
  • Fang, Nengwei
  • Liu, Lanhui
  • Yao, Jinbao

Abstract

Many transfer learning methods focus on training models between domains with large differences. However, the data feature distribution varies greatly in different bearing degradation processes, which affects the prediction accuracy of Remaining useful life (RUL). To solve this problem, a novel method for RUL prediction with more refined transfer is proposed, which added failure behavior judgment. Firstly, a failure behavior judgment method is proposed by using the convolutional autoencoder (CAE) and Pearson correlation coefficient to determine whether the bearing fails gradually or suddenly. Then, a multi-channel transfer network is proposed for extracting multi-scale features of bearing degradation. Each channel uses convolutional network and bidirectional long short-term memory (Bi-LSTM) to extract global and temporal information. To reduce the difference in feature distribution between the training and test bearing data, a domain adaptive structure is added after feature fusion in each channel to enable the model to learn domain invariant features. By applying this method to experimental data and comparing it with other methods, the superiority and effectiveness of the proposed method are verified.

Suggested Citation

  • Dong, Shaojiang & Xiao, Jiafeng & Hu, Xiaolin & Fang, Nengwei & Liu, Lanhui & Yao, Jinbao, 2023. "Deep transfer learning based on Bi-LSTM and attention for remaining useful life prediction of rolling bearing," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
  • Handle: RePEc:eee:reensy:v:230:y:2023:i:c:s0951832022005294
    DOI: 10.1016/j.ress.2022.108914
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    References listed on IDEAS

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    Cited by:

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    4. Naheliya, Bharti & Redhu, Poonam & Kumar, Kranti, 2024. "MFOA-Bi-LSTM: An optimized bidirectional long short-term memory model for short-term traffic flow prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 634(C).
    5. Xu, Xiaodong & Tang, Shengjin & Han, Xuebing & Lu, Languang & Wu, Yu & Yu, Chuanqiang & Sun, Xiaoyan & Xie, Jian & Feng, Xuning & Ouyang, Minggao, 2023. "Fast capacity prediction of lithium-ion batteries using aging mechanism-informed bidirectional long short-term memory network," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    6. 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).
    7. Zhu, Ting & Chen, Zhen & Zhou, Di & Xia, Tangbin & Pan, Ershun, 2024. "Adaptive staged remaining useful life prediction of roller in a hot strip mill based on multi-scale LSTM with multi-head attention," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    8. 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).

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