Mutual information-based sparse multiblock dissimilarity method for incipient fault detection and diagnosis in plant-wide process

摘要

Multiblock methods have attracted much attention in monitoring of plant-wide processes. However, most recent works only provide rough division results and do not take the data distribution changes among the process into consideration. To solve these defects, a new multiblock monitoring scheme that integrates a mutual information (MI)-based sparsification method, dissimilarity analysis (DISSIM) method and support vector data description (SVDD) is proposed in this paper. This method makes use of the complex relations between variables and the connections between sub-blocks in process decomposition to produce easy-to-interpret sub-blocks related to the process mechanism. Then DISSIM method is applied in each sub-block for distribution monitoring. The multiblock DISSIM strategy can deal with the local behaviors and distribution changes, improving its sensitivity to incipient changes in plant-wide process. Finally, results in all blocks are combined with SVDD to provide a final decision, and a diagnosis method is proposed for fault diagnosis. Case studies upon a numerical case and Tennessee Eastman (TE) benchmark process demonstrate the effectiveness of our method. (C) 2019 Elsevier Ltd. All rights reserved.