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 2
刊名	International Journal of Automation and Computing
	2021
卷号	18 期号:4 页码:581-593
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)
ISSN号	1476-8186
DOI	10.1007/s11633-020-1276-6
英文摘要	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.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/45065]
专题	自动化研究所_学术期刊_International Journal of Automation and Computing
作者单位	1.Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield S1 3JD, UK 2.School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
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.