Semantically Enriched Industry Data & Information Modelling: A feasibility study on Shop-floor Incident Recognition

Authors: A. Tsolakis, D. Arena, S. Krinidis, A. Perdikakis, D. Ioannidis, D. Kyritsis, D. Tzovaras

Abstract

Knowledge modelling at industrial level consists an importunate activity nowadays due to the ceaseless advances in technologies and standards applied as well as the extensive amount of unrelated real-time and historical data at shop-floor level. A Common Interface Data Exchange Model (CIDEM) is hereby introduced towards unifying continuously produced data from heterogeneous and distributed information sources – on different levels and granularities – into a shared vocabulary that can unobtrusively communicate with industrial standards and protocols (i.e. B2MML, gbXML, MIMOSA). For further enhancing the information model a conceptual definition is employed leading to a semantically enriched model which enables more understandable high level knowledge diffusion. This model has been applied to various industrial applications, one of the most important being industrial safety, through incident recognition.

Citation

A. Tsolakis, D. Arena, S. Krinidis, A. Perdikakis, D. Ioannidis, D. Kyritsis, D. Tzovaras, “Semantically Enriched Industry Data & Information Modelling: A feasibility study on Shop-floor Incident Recognition”, 14th IEEE International Conference on Industrial Informatics (INDIN’16), Futurescope, Poitiers, France, 18-21 July 2016

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