Combining Evidential Clustering and Ontology Reasoning for Failure Prediction in Predictive Maintenance
Résumé
In smart factories, machinery faults and failures are detrimental to the efficiency and reliability of production systems. To ensure the smooth operation of production systems, predictive maintenance techniques have been widely used in a variety of contexts. In this paper, we tackle the machinery failure prediction task by introducing a novel hybrid ontology-based approach. The proposed approach is based on the combined use of evidential theory tools and semantic technologies. Among evidential theory tools, the Evidential C-means (ECM) algorithm is used to assess the criticality of failures according to two main parameters (time constraints and maintenance cost). In addition, domain ontologies with their rule-based extensions are used to formalize the domain knowledge and predict the time and criticality of future failures. Case studies on synthetic data sets and a real-world data set are used to validate the proposed approach.
