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Intelligent decision support for maintenance: an overview and future trends

Turner, C., Emmanouilidis, C., Tomiyama, T., Tiwari, A. and Roy, R. ORCID: 0000-0001-5491-7437 (2019). Intelligent decision support for maintenance: an overview and future trends. International Journal of Computer Integrated Manufacturing, 32(10), pp. 936-959. doi: 10.1080/0951192X.2019.1667033

Abstract

The changing nature of manufacturing, in recent years, is evident in industry’s willingness to adopt network-connected intelligent machines in their factory development plans. A number of joint corporate/government initiatives also describe and encourage the adoption of Artificial Intelligence (AI) in the operation and management of production lines. Machine learning will have a significant role to play in the delivery of automated and intelligently supported maintenance decision-making systems. While e-maintenance practice provides aframework for internet-connected operation of maintenance practice the advent of IoT has changed the scale of internetworking and new architectures and tools are needed. While advances in sensors and sensor fusion techniques have been significant in recent years, the possibilities brought by IoT create new challenges in the scale of data and its analysis. The development of audit trail style practice for the collection of data and the provision of acomprehensive framework for its processing, analysis and use should be avaluable contribution in addressing the new data analytics challenges for maintenance created by internet connected devices. This paper proposes that further research should be conducted into audit trail collection of maintenance data, allowing future systems to enable ‘Human in the loop’ interactions.

Publication Type: Article
Additional Information: This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Computer Integrated Manufacturing on 3 Oct 2019, available online: https://doi.org/10.1080/0951192X.2019.1667033.
Publisher Keywords: Machine learning, industry 4.0, E-maintenance, intelligent maintenance
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TJ Mechanical engineering and machinery
Departments: School of Mathematics, Computer Science & Engineering
URI: https://openaccess.city.ac.uk/id/eprint/24149
[img] Text - Accepted Version
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