Abstract
Predictive maintenance often relies on the continuous monitorization of equipment behavior, generally provided by sensors or by the very equipment. Additional data from management software, including which materials are being used and what processes are executed on the equipment can be used to enrich the data streams and ontologies can be used to bridge the gap between these different domains, while also facilitating the comprehension of the results obtained by the analytic methods applied to the data. Existing ontologies model these problems independently, and a holistic view that takes in consideration the temporal requirements of predictive maintenance is not yet available. This paper analysis existing ontologies and proposes a number of extensions that bridge the gaps between them, while meeting the time-sensitive requirements of the problem.
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Acknowledgements
The present work has been developed under the EUREKA - ITEA3 Project PIANISM (Itea-17008), PIANISM (ANI|P2020 40125). Alda Canito is supported by national funds through FCT – Fundação para a Ciência e Tecnologia through project UIDB/00760/2020 and Ph.D scholarship with reference SFRH/BD/147386/2019.
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Canito, A., Corchado, J., Marreiros, G. (2021). Bridging the Gap Between Domain Ontologies for Predictive Maintenance with Machine Learning. In: Rocha, Á., Adeli, H., Dzemyda, G., Moreira, F., Ramalho Correia, A.M. (eds) Trends and Applications in Information Systems and Technologies . WorldCIST 2021. Advances in Intelligent Systems and Computing, vol 1366. Springer, Cham. https://doi.org/10.1007/978-3-030-72651-5_51
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