Hidden Markov Model-Based Fault Detection Approach for a Multimode Process

Many industrial processes possess multiple operational modes and transitions because of various production factors, which pose a challenge to conventional fault detection methods. In this article, a novel fault detection scheme based on a hidden Markov model (HMM) is presented for multimode processes with transitions. To begin with, measurement data of stable modes and transitional modes are separated. Then, hidden state probability integration strategy is developed to combine local monitoring results into two global indices in a probabilistic manner. These two indications work together for stable mode process monitoring. Further a new HMM is built for transition process modeling. The Bayesian information criterion (BIC) is responsible for model evaluation. After an appropriate model is acquired, an index named negative log likelihood probability is employed for transition process fault detection. In the end, a numerical simulation example and the Tennessee Eastman Chemical process is utilized to show that our proposed approach is effective.