Intelligent decision support for maintenance: An overview and future trends

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dc.contributor.authorTurner, Christopher J.
dc.contributor.authorEmmanouilidis, Christos
dc.contributor.authorTetsuo, Tomiyama
dc.date.accessioned2019-10-03T19:03:31Z
dc.date.available2019-10-03T19:03:31Z
dc.date.issued2019-10-03
dc.description.abstractThe 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 a framework 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 a comprehensive framework for its processing, analysis and use should be a valuable 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 and the provision of comprehensive framework for its processing analysis and use, allowing future systems to enable ‘Human in the loop’ interactions.en_UK
dc.identifier.citationTurner CJ, Emmanouilidis C, Tomiyama T, et al., (2019) Intelligent decision support for maintenance: an overview and future trends. International Journal of Computer Integrated Manufacturing, Volume 32, Issue 10, 2019, pp. 936-959en_UK
dc.identifier.issn0951-192X
dc.identifier.urihttps://doi.org/10.1080/0951192X.2019.1667033
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/14589
dc.language.isoenen_UK
dc.publisherTaylor and Francisen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectMachine learningen_UK
dc.subjectIndustry 4.0en_UK
dc.subjectE-Maintenanceen_UK
dc.subjectIntelligent Maintenanceen_UK
dc.titleIntelligent decision support for maintenance: An overview and future trendsen_UK
dc.typeArticleen_UK

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