HMM-driven Robust Probabilistic Principal Component Analyzer for Dynamic Process Fault Classification

In this paper, a novel hidden Markov model (HMM)-driven robust latent variable model (LVM) is proposed for fault classification in dynamic industrial processes. A robust probabilistic model with Student's t mixture output is designed for tolerating outliers. Based on the robust LVM, the probabilistic structure is further developed into a classifier form so as to incorporate various types of process information during model acquisition. After that, the robust probabilistic classifier is extended within the HMM framework so as to characterize the time-domain stochastic uncertainties. The model parameters are derived through the expectation-maximization algorithm. For performance validation, the developed model is tested on the Tennessee Eastman benchmark process.