Quality‐relevant dynamic process monitoring based on mutual information multiblock slow feature analysis

Abstract

Abstract Slow feature analysis (SFA) is an efficient technique in exploring process dynamic information and is suitable for quality‐relevant process monitoring. However, involving quality‐irrelevant variables or features may introduce redundant information and degrade the monitoring performance. A novel multiblock monitoring scheme based on mutual information (MI) and SFA is proposed to detect an efficient quality‐relevant fault for dynamic processes. First, all process variables are divided into two blocks in accordance with their MI values with quality variables. Second, slow features (SFs) of two blocks are extracted via the SFA. The SFs, which are extracted from the quality‐relevant variables, are not all related to the quality variable. Thus, these SFs are further partitioned into two blocks in accordance with their MI values with quality variables. The SFs from three blocks are obtained, and monitoring statistics are constructed. Two simulation studies, including a numerical example and Tennessee Eastman process, demonstrate that the proposed method outperforms conventional methods.