A systematic review on time-constrained ontology evolution in predictive maintenance

摘要

With the modernization of industry and introduction of IoT, maintenance practices have been moving from reactive to proactive and predictive approaches. The identification of faults often relies on the analysis of real-time data provided by streams and unstructured sources. Ontologies have been applied to the maintenance field in order to add a semantic layer to the data and facilitate interoperability, and combined with other approaches for explainability and fault diagnosis, among others. In such a time-sensitive domain, it is important that ontologies go beyond static representations of the domain and allow not only for the incorporation of time related knowledge, but must also be able to adapt to new knowledge and evolve. This systematic review presents four research questions to provide a general understanding of the state of the art of the representation of time and ontology evolution in the predictive maintenance field. The results have shown that there are several ways of representing the evolution of knowledge that are fairly established and several specific evolutionary actions are discriminated and analyzed. Similarly, there is a diverse group of metrics that can be exploited to measure change and to establish evolutionary trends and even predict future stages of the ontology. Studies on the representation of time show us that it can be done either quantitative or qualitatively, with some approaches combining the two. Applications of these to the problem of ontology evolution are still in the open. Finally, results show that while applications of ontologies to the field of predictive maintenance are plenty, there are not many studies focusing on their evolution or in the effective application of their ability to reason with time constraints. The results obtained in this systematic review are particularly relevant for devising solutions that make use of the ontology’s potential for time representation and evolution in the predictive maintenance field.