Ontology-Based Semantic Modeling for Automated Identification of Damage Mechanisms in Process Plants

Abstract

Damage mechanisms reduce the ability of equipment to deliver its intended function and, thus, increase the equipment’s probability of failure. Damage mechanism assessment is performed to identify the credible damage mechanisms of the equipment; thereby, appropriate measures can be applied to prevent failures. However, due to its high dependency on human cognition, damage mechanism assessment is error-prone and time-consuming. Additionally, due to its multi-disciplinary nature, the damage mechanism assessment process requires unambiguous communication and synchronization of perspectives among collaborating parties from different knowledge domains. Thus, the Damage Mechanism Identification Ontology (DMIO), supported by Web Ontology Language axioms and Semantic Web Rule Language rules, is proposed to conceptualize damage mechanism knowledge in both a human- and machine-interpretable manner and to enable automation of the damage mechanism identification task. The implementation of DMIO is expected to create a leaner damage mechanism assessment process by reducing the lead-time to perform the assessment, improving the quality of assessment results, and enabling more effective and efficient communication and collaboration among parties during the assessment process.