A Regularized LSTM Method for Predicting Remaining Useful Life of Rolling Bearings

Springer Science and Business Media LLC - Tập 18 - Trang 581-593 - 2021
Zhao-Hua Liu1, Xu-Dong Meng1, Hua-Liang Wei2, Liang Chen1, Bi-Liang Lu1, Zhen-Heng Wang1, Lei Chen1
1School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan, China
2Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, UK

Tóm tắt

Rotating machinery is important to industrial production. Any failure of rotating machinery, especially the failure of rolling bearings, can lead to equipment shutdown and even more serious incidents. Therefore, accurate residual life prediction plays a crucial role in guaranteeing machine operation safety and reliability and reducing maintenance cost. In order to increase the forecasting precision of the remaining useful life (RUL) of the rolling bearing, an advanced approach combining elastic net with long short-time memory network (LSTM) is proposed, and the new approach is referred to as E-LSTM. The E-LSTM algorithm consists of an elastic mesh and LSTM, taking temporal-spatial correlation into consideration to forecast the RUL through the LSTM. To solve the over-fitting problem of the LSTM neural network during the training process, the elastic net based regularization term is introduced to the LSTM structure. In this way, the change of the output can be well characterized to express the bearing degradation mode. Experimental results from the real-world data demonstrate that the proposed E-LSTM method can obtain higher stability and relevant values that are useful for the RUL forecasting of bearing. Furthermore, these results also indicate that E-LSTM can achieve better performance.

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Tập 18

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