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

نویسندگان

چکیده

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

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ژورنال

عنوان ژورنال: IEEE Transactions on Industrial Informatics

سال: 2022

ISSN: ['1551-3203', '1941-0050']

DOI: https://doi.org/10.1109/tii.2021.3120020