Hierarchical Monitoring and Root-Cause Diagnosis Framework for Key Performance Indicator-Related Multiple Faults in Process Industries

Abstract

In actual production processes, the occurrence probability of multiple faults is much higher than that of a single fault, which will affect the process industry operating performance and final products quality. This paper is concerned with industrial practices and theoretical approaches for detection and location of key performance indicator (KPI) related multiple faults in process industries. First, a new KPI-related multiple fault monitoring scheme is addressed from the subprocess level based on the developed correlation-based canonical variable analysis model. Then, Bayesian fusion is implemented to form the final monitoring decisions from the plantwide level. After that, a tensor subspace analysis-based discriminant analysis method is proposed for locating the root causes, which will help field engineers to take correction actions and recover the process operations. Finally, the application to a typical industry process, i.e., hot strip mill process, is given to demonstrate the performance and effectiveness of the proposed methods with real industrial data.