Operating condition diagnosis based on HMM with adaptive transition probabilities in presence of missing observations

• Published in: AIChE journal Vol. 61; no. 2; pp. 477 - 493
• Main Authors: Sammaknejad, Nima, Huang, Biao, Xiong, Weili, Fatehi, Alireza, Xu, Fangwei, Espejo, Aris
• Format: Journal Article
• Language: English
• Published: New York Blackwell Publishing Ltd 01.02.2015; American Institute of Chemical Engineers
• Subjects: adaptive transition probabilities; Case studies; Chemical engineers; Computer simulation; Diagnosis; expectation maximization algorithm; fault diagnosis; hidden Markov models; Markov analysis; Markov chains; Mathematical models; Mathematical problems; Maximization; Monitoring; Multivariate analysis; Parameter estimation; Probability; Scheduling; Scheduling algorithms; Transition probabilities
• ISSN: 0001-1541; 1547-5905
• DOI: 10.1002/aic.14661

A new approach for modeling and monitoring of the multivariate processes in presence of faulty and missing observations is introduced. It is assumed that operating modes of the process can transit to each other following a Markov chain model. Transition probabilities of the Markov chain are time varying as a function of the scheduling variable. Therefore, the transition probabilities will be able to vary adaptively according to different operating modes. In order to handle the problem of missing observations and unknown operating regimes, the expectation maximization algorithm is used to estimate the parameters. The proposed method is tested on two simulations and one industrial case studies. The industrial case study is the abnormal operating condition diagnosis in the primary separation vessel of oil‐sand processes. In comparison to the conventional methods, the proposed method shows superior performance in detection of different operating conditions of the process. © 2014 American Institute of Chemical Engineers AIChE J, 61: 477–493, 2015