Efficient Process Monitoring via the Integrated Use of Markov Random Fields Learning and the Graphical Lasso

Process monitoring is an important aspect of safe operation of process plants. Various methods exist that monitor the process using data-driven methods, but they all have certain limitations. For instance, most of the fault detection methods are not able to detect the fault propagation path, and some methods require a priori knowledge on the faults, or the relationships between the monitored variables. In this study, a monitoring method for accurately detecting the faults and analyzing the fault propagation path is proposed. Named the Glasso-MRF monitoring framework, this method integrates the use of the graphical lasso algorithm (G-lasso) and the Markov random field (MRF) modeling framework to divide the monitored variables into relevant groups and then detect the faults separately for each of the groups. Graphical lasso uses the lasso constraint on the inverse covariance matrix of vari…