Towards an Ontology-Based Fault Detection and Diagnosis Framework - A Semantic Approach

Fault prediction methods aim for the realtime monitoring and detection of equipment failures in Industry 4.0 environments, with the integration of the internet of things and advanced analytics. The utilization of advanced, data-driven methods such as semantic technologies and ontology-based reasoning can enhance the efficiency of the development, operation, and integration of fault diagnostic systems and the management of related information. The main contribution of this paper is to present an ontology-based fault detection and diagnosis framework to improve the efficiency of fault analysis and detection processes in industrial systems. The framework uses ontologies to represent the complex relationships between different types of data, maintenance events, or equipment failures, and not only relays on standard failure analysis methods but adapts fault tree analysis for semantic reasoning. Additionally, a conceptual domain ontology is proposed, which provides efficient data interoperability in the complex system. An illustrative, wire harness manufacturing-based example presents, that the framework can identify potential failure modes and their causes, and perform anomaly detection and root cause analysis, using the domain ontology with reasoning techniques.