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Bearing remaining useful life prediction with convolutional long short-term memory fusion networks

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  • Wan, Shaoke
  • Li, Xiaohu
  • Zhang, Yanfei
  • Liu, Shijie
  • Hong, Jun
  • Wang, Dongfeng

Abstract

Deep learning methods have improved the performance of RUL prediction, and multi-sensor data has also been found can significantly improve the fault diagnosis's accuracy. Hence, it is also highly motivated to integrate the deeply learned features from multi-sensor data for RUL prediction. In this paper, a novel deep learning framework with multi-branch networks, which is called convolutional long short-term memory fusion networks (CLSTMF), is proposed for RUL prediction with multi-sensor data. In each branch networks, shallow features of single sensor's data are extracted by convolutional layer of convolutional neural network (CNN), and then convolutional long short-term memory (CLSTM) network is employed to capture deep temporal features from these shallow features. Meanwhile, a novel information transfer layer (ITL) is developed to fuse the multi-sensor data's features captured with CLSTM in different branch networks. Experiments are also performed on two real run-to-failure datasets and the results indicates that the proposed approach performs well with respect to higher accuracy.

Suggested Citation

  • Wan, Shaoke & Li, Xiaohu & Zhang, Yanfei & Liu, Shijie & Hong, Jun & Wang, Dongfeng, 2022. "Bearing remaining useful life prediction with convolutional long short-term memory fusion networks," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    • Handle: RePEc:eee:reensy:v:224:y:2022:i:c:s095183202200182x
    DOI: 10.1016/j.ress.2022.108528
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    References listed on IDEAS

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    1. 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).
    2. Li, Naipeng & Gebraeel, Nagi & Lei, Yaguo & Fang, Xiaolei & Cai, Xiao & Yan, Tao, 2021. "Remaining useful life prediction based on a multi-sensor data fusion model," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
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    5. 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).
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    7. Liu, Junqiang & Lei, Fan & Pan, Chunlu & Hu, Dongbin & Zuo, Hongfu, 2021. "Prediction of remaining useful life of multi-stage aero-engine based on clustering and LSTM fusion," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
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    Cited by:

    1. Leoni, Leonardo & De Carlo, Filippo & Abaei, Mohammad Mahdi & BahooToroody, Ahmad & Tucci, Mario, 2023. "Failure diagnosis of a compressor subjected to surge events: A data-driven framework," Reliability Engineering and System Safety, Elsevier, vol. 233(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. Yang, Ningning & Wang, Zhijian & Cai, Wenan & Li, Yanfeng, 2023. "Data Regeneration Based on Multiple Degradation Processes for Remaining Useful Life Estimation," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    4. 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).
    5. 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).
    6. Chen, Xi & Wang, Hui & Lu, Siliang & Xu, Jiawen & Yan, Ruqiang, 2023. "Remaining useful life prediction of turbofan engine using global health degradation representation in federated learning," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    7. Abdullah Caliskan & Conor O’Brien & Krishna Panduru & Joseph Walsh & Daniel Riordan, 2023. "An Efficient Siamese Network and Transfer Learning-Based Predictive Maintenance System for More Sustainable Manufacturing," Sustainability, MDPI, vol. 15(12), pages 1-23, June.
    8. Cao, Lixiao & Zhang, Hongyu & Meng, Zong & Wang, Xueping, 2023. "A parallel GRU with dual-stage attention mechanism model integrating uncertainty quantification for probabilistic RUL prediction of wind turbine bearings," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    9. Ni, Qing & Ji, J.C. & Feng, Ke & Zhang, Yongchao & Lin, Dongdong & Zheng, Jinde, 2024. "Data-driven bearing health management using a novel multi-scale fused feature and gated recurrent unit," Reliability Engineering and System Safety, Elsevier, vol. 242(C).

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