Semantics-driven Knowledge Representation for Decision Support and Status Awareness at Process Plant Floors
Authors: D. Arena, C. Zigou, S. Voutetakis and D. Kiritsis
The aim of this work is to demonstrate the potential of introducing ontologies for an appropriate visual status signaling at industrial process units. The need for safe operation and maintenance, either preventive or condition-based, drives the proposed approach, which supports operators and technical team utilizing knowledge from the workers, combined with reasoning techniques. As a consequence, the traditional Human Machine Interfaces (HMIs) are able to provide appropriate information to the workers about the status of a plant’s subsystems. As far as the overall proposed approach is concerned, the adoption of semantics in this framework is not to say that all of the data integration issues and increasingly less distributed ontologies, are solved. Here, semantics modelling and semantics-driven (or semantic rules-based) analysis methods are exploited to conceptualize a semantically-enriched platform, and test it in terms of feasibility at a chemical pilot plant of.
D. Arena, C. Zigou, S. Voutetakis, D. Kiritsis. Semantics-driven Knowledge Representation for Decision Support and Status Awareness at Process Plant Floors. ICE 2017