Design of a Knowledge-Enabled Supervisory Framework for the Detection of Abnormal Conditions at Process Pilot Plants

Abstract

This work presents a knowledge-enabled supervisory monitoring (KSM) framework which is used for the enhanced operational analysis of processes that are controlled by industrial automation systems. The objective of the knowledge-enabled platform is to provide notifications to the operators about potential abnormal behaviour of the underlying control equipment or subsystem. A set of rules designate the predefined conditions that are considered nominal which are described by a Finite State Machine. The systems is represented by an ontology integrated into a Common Information Data Exchange Model (CIDEM) using existing standards (ISA-95, implemented by a B2MML (Business To Manufacturing Markup Language) XML schema) for the information modelling while the decisions are provided in an informative manner to the operator. The functionalities of the KSM and its potential are exemplified to a continuous process at CERTH which is monitored by a Supervisory Control and Data Acquisition System (SCADA).