Dynamic mixture probabilistic PCA classifier modeling and application for fault classification

A dynamic classifier based on the mixture probabilistic principal component analyzer (MPPCA) is proposed for fault classification. Compared with traditional methods, both fault detection and diagnosis are combined into a single classification task. By introducing a state indicator, the conventional MPPCA model is first designed as a standard classifier. Then, the static MPPCA model based classifier is temporally extended to the dynamic form within the hidden Markov model framework. Both static and dynamic MPPCA classifiers are obtained by using the Expectation‐Maximization algorithm. For performance evaluation, case studies of the continuous stirred tank heater process and the Tennessee Eastman process are carried out. Results indicate that the dynamic MPPCA classifier performs better compared with the static MPPCA classifier and the hidden Markov model based classifier.