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The traditional dissimilarity (DISSIM) method explores the underlying fault characteristic based on the data distribution and is sensitive to the structure change of the process. However, it fails to explore which variables are significant to the concerned faults and its monitoring performance, including fault detection and diagnosis performance, is seriously decreased by the noise brought by non-informative variables, especially in plant-wide process. Since mutual information (MI) can explore both the linear and nonlinear dependencies of variables, a recursive MI-based variable selection algorithm is proposed in this paper. It can efficiently extract the most informative variables to the faults online and reduce the computational complexity. Then based on the variables selected online, the dissimilarity index is calculated to detect the distribution changes from normal to a fault condition and an MI-based diagnosis method is developed to further investigate the responsible variables to the fault. With the variable selection, not only the local characteristic of the process can be highlighted by the informative variables but also the influence of the non-informative variables can be eliminated, thus the control limit can be adaptively updated and the sensitivity and accuracy of the monitoring performance can be significantly improved. Moreover, the MI-based diagnosis method explores the contribution of selected variables with a high-order statistic and overcomes shortage brought by variable variance. Case study on Tennessee Eastman (TE) benchmark process demonstrates the feasibility and efficiency of our method.

Online Process Monitoring Using Recursive Mutual Information-Based Variable Selection and Dissimilarity Analysis With No Prior Information