Non-Gaussian chemical process monitoring with adaptively weighted independent component analysis and its applications

Abstract

Chemical process monitoring based on independent component analysis (ICA) is among the most widely used multivariate statistical process monitoring methods and has progressed very quickly in recent years. Generally, ICA methods initially employ several independent components (ICs) that are ordered according to certain criteria for process monitoring. However, fault information has no definite mapping relationship to a certain IC, and useful information might be submerged under the retained ICs. Thus, weighted independent component analysis (WICA) for fault detection and identification is proposed to process useful submerged information and reduce missed detection rates of I2 statistics. The main idea of WICA is to initially build the conventional ICA model and then use the change rate of the I2 statistic (RI2) to evaluate the importance of each IC. The important ICs tend to have higher RI2; thus, higher weighting values are then adaptively set for these ICs to highlight the useful fault information. Case studies on both simple simulated and Tennessee Eastman processes demonstrate the effectiveness of the WICA method. Monitoring results indicate that the performance of I2 statistics improved significantly compared with principal component analysis and conventional ICA methods.