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An Axiomatic Categorisation Framework for the Dynamic Alignment of Disparate Functions in Cyber-physical Systems

Published online by Cambridge University Press:  26 July 2019

Thomas J Byrne
Affiliation:
University of Bradford, Faculty of Engineering and Informatics; Advanced Automotive Analytics Research Centre
Aleksandr Doikin
Affiliation:
University of Bradford, Faculty of Engineering and Informatics; Advanced Automotive Analytics Research Centre
Felician Campean
Affiliation:
University of Bradford, Faculty of Engineering and Informatics; Advanced Automotive Analytics Research Centre
Daniel Neagu
Affiliation:
University of Bradford, Faculty of Engineering and Informatics; Advanced Automotive Analytics Research Centre
Corresponding
E-mail address:

Abstract

Advancing Industry 4.0 concepts by mapping the product of the automotive industry on the spectrum of Cyber Physical Systems, we immediately recognise the convoluted processes involved in the design of new generation vehicles. New technologies developed around the communication core (IoT) enable novel interactions with data. Our framework employs previously untapped data from vehicles in the field for intelligent vehicle health management and knowledge integration into design. Firstly, the concept of an inter-disciplinary artefact is introduced to support the dynamic alignment of disparate functions, so that cyber variables change when physical variables change. Secondly, the axiomatic categorisation (AC) framework simulates functional transformations from artefact to artefact, to monitor and control automotive systems rather than components. Herein, an artefact is defined as a triad of the physical and engineered component, the information processing entity, and communication devices at their interface. Variable changes are modelled using AC, in conjunction with the artefacts, to aggregate functional transformations within the conceptual boundary of a physical system of systems.

Type
Article
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
© The Author(s) 2019

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