A Dynamic Model-Level Method for Multimode Process Monitoring Using ω-Metric

A multimode process possesses dynamic behaviors, especially in between-mode transitions. Motivated by that dynamic models are well-suited for interpretation of process dynamics, we initially propose a dynamic model-level method for multimode process monitoring to detect abnormal operations quickly and accurately. It mainly includes three parts. First, a piecewise affine (PWA) model is identified offline to represent different dynamic patterns of a multimode process, which can avoid model redundancy compared with existing achievements. Owing to the utilization of the PWA model, the proposed method is still valid when unknown transitions occur. Second, in online monitoring, local autoregressive exogenous (ARX) models, obtained by just-in-time learning (JITL), can sufficiently extract local dynamic patterns so as to facilitate evaluating dynamic changes in the process. Finally, a novel measurement, ω -metric, is devised to analyze the dynamic variations in the process by directly measuring the similarity between local models and the PWA model, which is more beneficial for detecting abnormal operations than data distances. The superiority of the proposed method is validated by a continuous stirred tank reactor (CSTR) and a real air separation process compared with the existing multimode process monitoring approaches.