Remaining Useful Life Prediction for a Roller in a Hot Strip Mill Based on Deep Recurrent Neural Networks

doi:10.1109/JAS.2021.1004051

CORC > 自动化研究所 > 中国科学院自动化研究所 > 学术期刊 > IEEE/CAA Journal of Automatica Sinica

	Remaining Useful Life Prediction for a Roller in a Hot Strip Mill Based on Deep Recurrent Neural Networks
	Ruihua Jiao; Kaixiang Peng; Jie Dong
刊名	IEEE/CAA Journal of Automatica Sinica
	2021
卷号	8 期号:7 页码:1345-1354
关键词	Hot strip mill prognostics and health management (PHM) recurrent neural network (RNN) remaining useful life (RUL) roller management
ISSN号	2329-9266
DOI	10.1109/JAS.2021.1004051
英文摘要	Accurate estimation of the remaining useful life (RUL) and health state for rollers is of great significance to hot rolling production. It can provide decision support for roller management so as to improve the productivity of the hot rolling process. In addition, the RUL prediction for rollers is helpful in transitioning from the current regular maintenance strategy to conditional-based maintenance. Therefore, a new method that can extract coarse-grained and fine-grained features from batch data to predict the RUL of the rollers is proposed in this paper. Firstly, a new deep learning network architecture based on recurrent neural networks that can make full use of the extracted coarsegrained fine-grained features to estimate the heath indicator (HI) is developed, where the HI is able to indicate the health state of the roller. Following that, a state-space model is constructed to describe the HI, and the probabilistic distribution of RUL can be estimated by extrapolating the HI degradation model to a predefined failure threshold. Finally, application to a hot strip mill is given to verify the effectiveness of the proposed methods using data collected from an industrial site, and the relatively low RMSE and MAE values demonstrate its advantages compared with some other popular deep learning methods.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/44587]
专题	自动化研究所_学术期刊_IEEE/CAA Journal of Automatica Sinica
推荐引用方式 GB/T 7714	Ruihua Jiao,Kaixiang Peng,Jie Dong. Remaining Useful Life Prediction for a Roller in a Hot Strip Mill Based on Deep Recurrent Neural Networks[J]. IEEE/CAA Journal of Automatica Sinica,2021,8(7):1345-1354.
APA	Ruihua Jiao,Kaixiang Peng,&Jie Dong.(2021).Remaining Useful Life Prediction for a Roller in a Hot Strip Mill Based on Deep Recurrent Neural Networks.IEEE/CAA Journal of Automatica Sinica,8(7),1345-1354.
MLA	Ruihua Jiao,et al."Remaining Useful Life Prediction for a Roller in a Hot Strip Mill Based on Deep Recurrent Neural Networks".IEEE/CAA Journal of Automatica Sinica 8.7(2021):1345-1354.