Root cause identification of fault in hot-rolling process by causal plot

Abstract

In the steel manufacturing industry, a hot-rolling process produces a thick steel plate from a slab as a batch operation; however, off-spec steel plates are sometimes produced when abnormalities occur during rolling operations. To improve the product yield, it is necessary to appropriately ascertain the root cause of a fault. Because the physicochemical behaviors of the slab during hot-rolling are complicated and yet to be fully understood, we adopted a data-driven approach to identify the cause of the fault in the hot-rolling process. We previously proposed a data-driven fault diagnosis method, referred to as a causal plot, that considers the causal relationships between process variables and monitoring indexes for process monitoring. In the causal plot, monitoring indexes were calculated using existing process monitoring methods, and the causal relationships between the process variables and the calculated monitoring indices were estimated. A linear non-Gaussian acyclic model (LiNGAM) can be adopted for causal inferences between the process variables and calculated monitoring indexes. In this study, we propose a new fault diagnosis method for a batch process, referred to as a b-causal plot, utilizing the causal plot and dynamic time warping (DTW). We analyzed real operation data when defective coils were produced in the hot-rolling process with the proposed b-causal plot and confirmed that the identified root cause was consistent with process engineers’ knowledge, which is typically a low-importance variable that operators do not constantly monitor in daily operation. Because the root cause identification of faults is crucial for maintaining product quality and efficiency in batch processes, the proposed b-causal plot contributes to improving productivity across industries, as demonstrated in this work.