Applying Time-Constraints Using Ontologies To Sensor Data For Predictive Maintenance

Predictive maintenance depends on sources of data analysed through ma-chine learning and data mining algorithms to identify and predict potential fault states. However, sources of raw data lack a layer of semantic abstrac-tion providing a consistent way of interpreting data. The use of ontologies to describe data from structured and unstructured sources has been employed to provide a semantic layer delivering a consistent interpretation and meaning to data that can be exchanged between different entities. Data supplied by sensors is time-sensitive, as variations and fluctuations occur over periods and must be analysed regarding the period they occur. Transforming raw da-ta and applying time constraints to fit the data to the semantic concepts is a process not frequently documented. In this paper, we present an architecture for the transformation of data acquired through different sources – from sensors to contextual information supplied by management software – from JSON to ontology instances that can be used in a predictive maintenance scenario, alongside with machine learning and data mining solutions. Here, the different datasources are trans-formed semantic time-sensitive data, represented through means of an on-tology and stored in a triple store.