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

doi:10.1007/s11633-020-1276-6

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	A Regularized LSTM Method for Predicting Remaining Useful Life of Rolling Bearings
	Zhao-Hua Liu 1
发表期刊	International Journal of Automation and Computing
ISSN	1476-8186
	2021
卷号	18 期号:4 页码:581-593
摘要	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.
关键词	Deep learning fault diagnosis fault prognosis long and short time memory network (LSTM) rolling bearing rotating machinery regularization remaining useful life prediction (RUL) recurrent neural network (RNN)
DOI	10.1007/s11633-020-1276-6
引用统计	被引频次：25[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/45065
专题	学术期刊_Machine Intelligence Research
作者单位	1.School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan 411201, China 2.Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield S1 3JD, UK
推荐引用方式 GB/T 7714	Zhao-Hua Liu. A Regularized LSTM Method for Predicting Remaining Useful Life of Rolling Bearings[J]. International Journal of Automation and Computing,2021,18(4):581-593.
APA	Zhao-Hua Liu.(2021).A Regularized LSTM Method for Predicting Remaining Useful Life of Rolling Bearings.International Journal of Automation and Computing,18(4),581-593.
MLA	Zhao-Hua Liu."A Regularized LSTM Method for Predicting Remaining Useful Life of Rolling Bearings".International Journal of Automation and Computing 18.4(2021):581-593.

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