Deep learning method for remaining useful life prediction of the rolling bearing of high-speed train

Runze Wang; Tiantian Liang

doi:10.1117/12.2681562

1 June 2023 Deep learning method for remaining useful life prediction of the rolling bearing of high-speed train

Runze Wang, Tiantian Liang

Proceedings Volume 12718, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023); 1271822 (2023) https://doi.org/10.1117/12.2681562
Event: International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023), 2023, Nanjing, China

Abstract

In this paper, an optimized long short-term memory (LSTM) network is proposed for the remaining useful life (RUL) prediction of the rolling bearings based on whale optimized algorithm (WOA). The multi-domain features are extracted to construct the feature dataset as the single domain features are difficult to characterize the performance degeneration of the rolling bearing. Considering the possible gradient explosion by training of the rolling bearing lifetime data and the difficulties in selecting the key network parameters, an optimized LSTM network, namely, WOA-LSTM network is proposed. Experiment results show that, compared with the LSTM network, the RUL prediction accuracy of the rolling bearing are improved by the proposed WOA-LSTM network.

Citation Download Citation

Runze Wang and Tiantian Liang "Deep learning method for remaining useful life prediction of the rolling bearing of high-speed train", Proc. SPIE 12718, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023), 1271822 (1 June 2023); https://doi.org/10.1117/12.2681562

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