A Three-Step Framework for Multimodal Industrial Process Monitoring Based on DLAN, TSQTA, and FSBN

The process monitoring method for industrial production can technically achieve early warning of abnormal situations and help operators make timely reliable response decisions. Because practical processes have multimodal operating conditions, the data distributions variables are different. different may cause fault detection model to be invalid. In addition, diagnosis cannot find correct root variable system failure by only identifying variables. There correlations between trend states If we do not consider these correlations, this result in an incorrect cause. Therefore, is a tough issue. paper, propose three-step framework monitoring. aims detect faulty status then that causes failure. We present deep local adaptive network (DLAN), two-stage qualitative analysis (TSQTA), five-state Bayesian (FSBN) implement detection, identification, step step. This timely, identify variables, propagation path. case studies on Tennessee Eastman simulation chlorobenzene provided verify effectiveness proposed