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Remaining Life Prediction Method for Rolling Bearing Based on the Long Short-Term Memory Network

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Abstract

A residual life prediction method based on the long short-term memory (LSTM) was proposed for remaining useful life (RUL) prediction in this paper. Firstly, feature parameters were extracted from time domain, frequency domain, time–frequency domain and related-similarity features; then three feature evaluation indicators were defined to select feature parameters that could better represent the degradation process of bearings and constructed the feature set with the time factor. The data of the feature set was used to train the LSTM network prediction model, and then the RUL was predicted by the trained neural network. The full life test of rolling bearing was provided to demonstrate that this method could accurately predict the remaining life of the rolling bearing, and the result was compared with the prediction results of BP neural network and support vector regression machine to verify the effectiveness.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 51375067 and 51875075).

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Authors and Affiliations

  1. Institute of Vibration Engineering, Dalian University of Technology, Dalian, 116024, China

    Fengtao Wang, Xiaofei Liu, Gang Deng, Hongkun Li & Qingkai Han

  2. School of Mechanical Engineering and Automation, University of Science and Technology LiaoNing, Anshan, 114051, China

    Xiaoguang Yu

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  1. Fengtao Wang
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  2. Xiaofei Liu
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  3. Gang Deng
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  4. Xiaoguang Yu
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  5. Hongkun Li
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  6. Qingkai Han
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Correspondence to Fengtao Wang.

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Wang, F., Liu, X., Deng, G. et al. Remaining Life Prediction Method for Rolling Bearing Based on the Long Short-Term Memory Network. Neural Process Lett 50, 2437–2454 (2019). https://doi.org/10.1007/s11063-019-10016-w

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  • DOI: https://doi.org/10.1007/s11063-019-10016-w

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