Ontology for Strategies and Predictive Maintenance models

As of today, to cope with traditional maintenance policies such as reactive and preventive maintenance, the manufacturing companies need the deployment of adaptive and responsive maintenance strategies. Meanwhile, the advent of Industry 4.0 leads the maintenance paradigm shift facilitated by the efficient monitoring of physical assets and forecasting of the potential risks. As the advanced maintenance policies benefit in terms of cost-efficiency, inventory management and reliability management, most of the manufacturing companies are trying to make their own advanced maintenance strategies and to elaborate on the development of an innovative platform for it. However, since advanced enabling technologies collect a huge amount of data from different data sources such as machine, component, document, process and so on, data federation should necessarily be achieved for further discussion, but manufacturing companies are immature to address this issue. H2020 EU project Z-BRE4K, i.e., Strategies and predictive maintenance models wrapped around physical systems for zero unexpected-breakdowns and increased operating life of factories, deploys semantic technologies to address this issue. This paper deals with the debate on how to efficiently federate various data formats with the support of the semantic technologies in the context of maintenance. In addition, it proposes a maintenance ontology validated and implemented with an actor from European industry. Copyright (C) 2020 The Authors.