Building an Integrated Simulation Model with Ontologies for Prescriptive Maintenance

Resumo

This article provides studies to support the creation of prescriptive maintenance frameworks, addressing the difficulty caused by the lack of formalized data that hinders the application of rule sets and the execution of data-driven maintenance measures. The absence of adequate data structuring makes it difficult to develop automated and efficient approaches to solve maintenance problems.

One adopted solution is the use of semantic approaches through ontologies. By modeling the domain at a conceptual level, ontologies make it possible to establish relationships and decision logic to drive prescriptive maintenance solutions in an automated way, providing a coherent and consistent structure for the data and facilitating its understanding and manipulation. The use of ontologies enables a formal representation of the data, allowing the creation of more efficient frameworks for prescriptive maintenance.

Using a solution that integrates a simulation model with an ontology simultaneously would bring significant gains to the academic community, since it is possible to obtain benefits in terms of in-depth data understanding, automation of prescriptive maintenance measures, and efficiency in creating frameworks to solve maintenance problems.