Abstract
Many industrial processes have multiple operation modes and Gaussian mixture model (GMM) is used to describe multimode processes. This paper uses principal component analysis (PCA) and parameter estimation method of GMM for fault detection of multimode processes monitoring. First, by combining the Shannon entropy with the Deterministic Annealing EM (DAEM) algorithm, an entropy penalized maximum likelihood objective function is constructed to simultaneously perform parameter estimation and model selection of GMM. Then the PCA method is used to avoid the singular covariance matrix caused by the correlation of variables. Finally, with the obtained GMM, multimode process monitoring scheme is applied. A numerical example and the Tennessee Eastman (TE) benchmark case studies are provided to verify the effectiveness of the proposed method.
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