Weighted part mutual information related component analysis for quality-related process monitoring

Industrial products have become the core of today's highly competitive international society, but quality-related faults happened in practical industrial processes heavily affect product quality. In this paper, we will consider the problem of the detection of quality-related faults. Inspired by part mutual information (PMI), we develop a process monitoring method called weighted PMI based related component analysis (WPMI-RCA). Firstly, combining PMI and Bayesian weighted fusion, process variables strongly related to quality are selected with the supervision of multi-quality indicators. Then, the selected variables are modeled by related component analysis (RCA) and thus orthogonal related components (RCs) containing the main information of quality variations can be obtained. The process data space can be divided into two subspaces and the monitoring statistics are developed for the quality-related fault detection. Finally, the validity of WPMI-RCA is demonstrated by a numerical example and the benchmark Tennessee Eastman process (TEP). The proposed method can improve the detection rates of quality-related faults and significantly reduce the nuisance detections. It may be helpful to improve the management efficiency for practical industrial processes. (C) 2020 Elsevier Ltd. All rights reserved.