Nonlinear Degradation Modeling and Residual Life Prediction for Rollers Based on Kernel-based Wiener Process

Abstract

In the process of steel rolling, due to wear and other reasons, the working performance of the roll under long and complex working conditions has a gradual decline. Considering the characteristics of complex working conditions and strong random interference of the roll working environment, this paper proposed a kernel-based Wiener process (KWP) degradation model to characterize the strong randomness of the roll degradation trend by using the Wiener process, and to capture the nonlinear degradation path of the roll by using the kerna function. This paper derives the analytical expression of parameter estimation in the Bayesian framework, and constructs the health index of the roll working rotation, then predicts the remaining useful life (RUL) of the roll. In combination with the field data of 1580 hot rolling production line of an iron and steel company, the goodness of fit of the model built is 0.989, and the residual life prediction error is less than 4.7%. Compared with the common machine learning algorithm, it has achieved better results, which is helpful to improve the operating efficiency and safety of equipment and achieve maintenance as needed.

Keywords

• roller
• performance degradation
• nonlinear modeling
• kernel function
• wiener process
• remaining useful life (rul) prediction