Hidden Markov models combining local and global information for nonlinear and multimodal process monitoring

Multivariate statistical process monitoring techniques have been successfully used to detect and identify deviation from normal operation within industrial processes. However, the nonlinear and multimodal characteristics in some processes have posed difficulties to the conventional approaches, because a fundamental assumption is often that the operating data is unimodal and Gaussian distributed. To explicitly address these important characteristics in processes, hidden Markov models (HMM)-based process monitoring models have been developed in this paper. A novel quantification indication for process state is proposed, which effectively combines local information (Mahalanobis distance) and global information (negative log likelihood probability) in HMM. In addition, a Bayesian inference-based process failure probability indication is developed, where the posterior probabilities of each new sample belonging to each Gaussian component in a hidden state of HMM are calculated. HMM is capable to estimate data distribution from normal operation with nonlinear and multimodal characteristics, under the assumption that predictable fault patterns are not available. Thus, the HMM-based monitoring models can be used for online process monitoring without too much human intervention. The validity and effectiveness of the proposed models are illustrated through simulated and real-world processes. The experimental results clearly demonstrate that the proposed approaches effectively captured the nonlinear and multimodal relationship in process variables and showed superior process monitoring performance compared to those conventional process monitoring approaches. (C) 2009 Elsevier Ltd. All rights reserved.