Statistical method based on dissimilarity of variable correlations for multimode chemical process monitoring with transitions

Abstract

Chemical industrial processes are always accompanied by multiple operating conditions, which brings great challenges for multivariate statistical process monitoring methods to extract general characteristics from multimode data, especially for time-varying characteristics in transitions between two modes. In this work, a novel statistical process monitoring method based on the dissimilarity of process variable correlation (DISS-PVC) is proposed. The proposed method aims to monitor multiple stable modes and between-mode transitions simultaneously with no prior knowledge of the number of operating modes. Unlike traditional methods oriented to monitoring process variables, the proposed method is applied to monitor the correlation of process variables based on the idea that variable correlation should always conform to a certain process internal mechanism, no matter in which stable or transition mode. Mutual information is first employed to quantitate variable correlation with a moving-window approach. Cosine similarity between eigenvalues of mutual information matrices is selected as a dissimilarity index to evaluate the difference in variable correlation between two data sets and perform fault detection. The effectiveness of the proposed method is verified on the benchmark Tennessee Eastman (TE) process and an industrial continuous catalytic reforming heat exchange unit.