Thickness-Related Fault Diagnosis of Steel Strip Based on W-KPLS Method Considering Mechanism Weight Optimization

Abstract

Due to the lack of a reasonable mechanism explanation for the data model used in the process of quality-related fault diagnosis, the diagnosis model has insufficient ability to identify faults, resulting in the phenomenon of failure detection or false positive. Therefore, this paper adopted the method of mechanism and data model fusion to solve the problem of insufficient interpretation of the influence of existing diagnosis methods on rolling process variables. Firstly, the KPLS achieves strip quality-related fault detection for nonlinear processes. In order to find out the abnormal variables, a nonlinear contribution plot was introduced to calculate the contribution value of each variable to the monitoring index. Secondly, based on the bounce equation of the rolling process, the static comprehensive analysis of the steady rolling process was carried out to reveal the influence of various variables on strip thickness. Thirdly, based on the above analysis of the steady rolling process mechanism, the influence weight method and kernel function method were used to reconstruct and map the original input matrix. A kernel partial least squares method based on influence weight W optimization (W-KPLS) was proposed for quality-related fault monitoring and diagnosis. Finally, the model was applied in the cold rolling process of an aluminum alloy sheet, and the validity of the model was further verified by practical industrial data. The results show that the new method improves the fault detection rate by more than 20% compared with the traditional monitoring method, and the proportion of data points reaching the early warning limit was increased to more than 95%.