Hidden Markov model-based approach for multimode process monitoring

• A probability ratio strategy based on HMM is proposed to identify stable modes and transitional modes.
• A new monitoring indication based on HMM is developed for fault detection.
• Hidden Markov estimation accounts for process dynamics and uncertainty.
• The Viterbi algorithm classifies stable modes data into different operating modes.

Many industrial processes have multiple operating modes due to various factors, such as alterations of feedstock and compositions, different manufacturing strategies, fluctuations in the external environment, and various product specifications. There are just a few literatures concerning hidden Markov model in multimode process monitoring. And HMM has not been explored to deal with transitional modes. Besides, those monitoring methods fail to take advantage of internal elements of HMM. In this article, a novel monitoring scheme based on hidden Markov mode (HMM) is proposed for multimode process with transitions. To begin with, a hidden Markov model is trained on the basis of the measurement data. Then a probability ratio strategy based on HMM is developed to identify stable modes and transitional modes. Further, the Viterbi algorithm classifies samples into various modes and a new monitoring indication is built based on the elements of HMM in each mode for fault detection. At last, the effectiveness of the proposed method is demonstrated through a numerical simulation and the Tennessee Eastman process.