Bayesian-LSTM Neural Network-based Remaining Useful Life Prediction and Uncertainty Estimation of Rollers in A Hot Strip Mill

doi:10.3901/JME.2024.11.181

Abstract

Abstract: Roller’s performance directly affects the production efficiency and quality of the steel rolling process. Combined with the complex operating environment and fluctuating working conditions, it is especially important to accurately predict the time-series change characteristics of the roller’s operating status and the remaining useful life (RUL) for the refined, intelligent and efficient production process. Considering the dynamic uncertainty in operation, Bayesian neural network-long short term memory network (Bayesian-LSTM) method is proposed to extract the roller health index to evaluate the roller’s health status and intelligently predict the RUL of the roller and quantify interval uncertainty of the distribution feature. The influence of the constructed Bayesian-LSTM network structure parameters on the lifetime interval is further discussed. And the effectiveness of the method is verified by using the actual industrial data from a hot strip mill. Results show that compared with the traditional data-driven method, the proposed method can not only achieve the prediction accuracy of 96.90%, but also evaluate the uncertainty of roller RUL.

Key words: hot strip mill, rollers, remaining useful life prediction, uncertainty estimation, Bayesian-LSTM

CLC Number:

TH17

ZHU Ting, CHEN Zhaoxiang, ZHOU Di, CHEN Zhen, HU Bing, PAN Ershun. Bayesian-LSTM Neural Network-based Remaining Useful Life Prediction and Uncertainty Estimation of Rollers in A Hot Strip Mill[J]. Journal of Mechanical Engineering, 2024, 60(11): 181-190.

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