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Prediction of remaining useful life of rolling element bearings based on LSTM and exponential model

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Abstract

Fault in rolling element bearings is a very common fault in mechanical systems. It may lead to abnormal operation of equipment, even to serious accidents or significant losses. Periodical monitoring of bearings plays a vital role in reducing unplanned maintenance and improving the reliability of machines. However, the existing methods for determining faults in rolling element bearings introduce too many artificial factors, and the results are often subjective. In order to solve this problem, the present paper proposes a hybrid real-time method for determining the starting time of a fault in a rolling element bearing. Based on the dynamic 3σ interval and voting mechanism, our method can adaptively predict the starting time. Firstly, the long short-term memory (LSTM) neural network is used to predict the trend of the future operation of the bearing. Then, an exponential model is used to estimate its remaining useful life (RUL). The obtained experimental results show that the proposed approach can significantly reduce artificial interference, adaptively divide the state of rolling element bearings, and accurately predict RUL.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Grants 61976141 and 51807124), in part by the State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures of Shijiazhuang Tiedao University (Grant KF2022-10).

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Authors and Affiliations

  1. State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang Tiedao University, Shijiazhuang, 050043, China

    Jingna Liu & Wenwu Guo

  2. College of Electrical and Electronic Engineering, Shijiazhuang Tiedao University, Shijiazhuang, 050043, China

    Jingna Liu

  3. School of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang, 050043, China

    Rujiang Hao

  4. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China

    Qiang Liu

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  1. Jingna Liu
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  2. Rujiang Hao
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Correspondence to Qiang Liu.

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Liu, J., Hao, R., Liu, Q. et al. Prediction of remaining useful life of rolling element bearings based on LSTM and exponential model. Int. J. Mach. Learn. & Cyber. 14, 1567–1578 (2023). https://doi.org/10.1007/s13042-023-01807-8

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  • DOI: https://doi.org/10.1007/s13042-023-01807-8

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