Decentralized PCA modeling based on relevance and redundancy variable selection and its application to large-scale dynamic process monitoring

Abstract

In order to ensure the long-term stable operation of a large-scale industrial process, it is necessary to detect and solve the minor abnormal conditions in time. However, the large-scale industrial process contains a large number of complex related process variables, some of which are redundant for abnormal condition detection. To solve this problem, a new decentralized PCA modeling method based on relevance and redundancy variable selection (RRVS-DPCA) is presented. First, considering the complex dynamic relation of process variables, a variable selection strategy based on relevance and redundancy (RRVS) is designed to select variables that carried the most profitable information from different temporal dimensions for each key process variables, so the optimal variable sub-block for each individual key process variables can be obtained. Then, for each sub-block, a corresponding sub-PCA monitoring model is established. The sub-blocks’ monitoring results are combined to form a probability statistical indicator through a Bayesian inference. Finally, the weighed contribution plot method is proposed to find the root cause of a fault. The proposed method is compared with several state-of-the-art process monitoring methods on a numerical example and the Tennessee Eastman benchmark process. The comparison results illustrate the feasibility and effectiveness of the proposed monitoring scheme.