Probabilistic monitoring of chemical processes using adaptively weighted factor analysis and its application

As a probabilistic statistical method, factor analysis (FA) has recently been introduced into process monitoring for the probabilistic interpretation and performance enhancement of noisy processes. Generally, FA methods employ the first several factors that are regarded as the dominant motivation of the process for process monitoring; however, fault information has no definite mapping relationship to a certain factor, and useful information might be suppressed by useless factors or submerged under retained factors, leading to poor monitoring performance. Weighted FA (WFA) for process monitoring is proposed to solve the problem of useful information being submerged and to improve the monitoring performance of the GT(2) statistic. The main idea of WFA is firstly building a conventional FA model and then using the change rate of the GT(2) statistics (RGT(2)) to evaluate the importance of each factor. The important factors tend to have larger RGT(2) values, and the larger weighting values are then adaptively assigned to these factors to highlight useful fault information. Case studies on both a numerical process and the Tennessee Eastman process demonstrate the effectiveness of the WFA method. Monitoring results indicate that the performance of the GT(2) statistic is improved significantly compared with the conventional FA method. (C) 2013 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.