Discovering Data-Aware Mode-Switching Constraints to Monitor Mode-Switching Decisions in Supervisory Control

• Published in: IEEE transactions on industrial informatics Vol. 18; no. 6; pp. 3734 - 3743
• Main Authors: Hussain, Mukhtar, Fidge, Colin, Foo, Ernest, Jadidi, Zahra
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-06-2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Anomalies; Control systems; Decision trees; Hidden Markov models; Industrial control systems; Industrial electronics; Informatics; Integrated circuits; mode-switching constraints; Monitoring; multimode processing; Paints; Power generation; Sensors; Supervisory control; Switches; Switching
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2021.3120020

In a multimode industrial control system, mode switching decisions have to follow standard operating procedures which are set for the safety of the system based on the operating limitations of equipment. A rich literature can be found on monitoring multimode systems. However, that work is mainly focused on mode identification and monitoring anomalies in the process running under each mode. Instead, we present a data-driven method for monitoring the modes' switching constraints. This article is based on state-transition matrix and decision-tree methods to discover data-driven mode switching conditions. Moreover, our approach is not limited to only threshold based condition learning. To capture data trajectory-based conditions, we adopt a functional data descriptors method. In practical experiments, we showed that our approach can discover anomalous mode-switching decisions which cannot be discovered by previous multimode process-monitoring methods.