Multimode Process Monitoring Approach Based on Moving Window Hidden Markov Model

Due to the existence of multiple operating modes, traditional fault detection techniques are ill-suited for complex industrial processes. Although there are more and more literature studies concerning this problem, only a few of them are based on the hidden Markov model (HMM). However, there is no exploration to concern the unknown mode in the industrial process based on it. This article proposes a novel process monitoring approach based on moving window HMM (MVHMM) for real-time multimode process monitoring with unknown mode. First, a hidden Markov model is built by training set. Instead of just considering the posterior probability of one single sample, the moving window is introduced to utilize the independence of samples for improving the accuracy of online mode identification. In addition, an MVHMM-based threshold statistic is defined to identify the unknown mode. Also, various known modes which include stable modes and transitions are separated on the basis of the Viterbi algorithm. Second, a new monitoring scheme is developed for fault detection of each mode. The effectiveness of the proposed approach is validated by the Tennessee Eastman (TE) chemical process and a numerical simulation example.