A framework for constructing a common knowledge base for human-machine system to perform maintenance tasks

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dc.contributor.authorDeng, Haoxuan
dc.contributor.authorKhan, Samir
dc.contributor.authorErkoyuncu, John Ahmet
dc.date.accessioned2022-11-08T22:57:50Z
dc.date.available2022-11-08T22:57:50Z
dc.date.issued2022-11-08
dc.description11th International Conference on Through-life Engineering Services - TESConf 2022, 8-9 November 2022, Cranfield UKen_UK
dc.description.abstractA reliable and comprehensive maintenance is important to promise the system running in a normal state, but it is skill-intensive and heavily dependent on human labor. With the development of predictive maintenance in industry, an optimized solution can be posed for maintaining assets with less downtime and cost. However, most of current research on this topic is limited on a top-level algorithm design for prediction, but few consider how to perform the maintenance tasks according to the prediction results at a particular occasion and condition. Besides, the complexity of system is exploded, and it may take people much effort to cover every detail to achieve a credible maintenance result. Thus, machine is introduced to collaborate with human by undertaking some work and suggesting actions to take in order to reduce human physical and mental workload. This paper aims to present a framework to integrate human knowledge and machine learning into a common knowledge base to enable human and machine can contribute to shift the final maintenance decision from planning to performing. The proposed framework is based on a knowledge graph generated by ontology and machine learning, which can be conveniently retrieved by human via questions answering system or visualization platform and efficiently computed by machine via graph representation learning. Consequently, domain knowledge can be formally represented, systematically managed and easily reused by human-machine teaming to attack domain-specific problems. In a long term, the evolving knowledge based, with an accumulation on samples and information, can guide the team to draw a reasonable and delicate strategy for overhaul and recondition, moreover, ensure the next generation of maintenance: prescriptive maintenance.en_UK
dc.description.sponsorshipDMG Morien_UK
dc.identifier.citationDeng H. Khan S, Erkoyuncu JA. (2022) A framework for constructing a common knowledge base for human-machine system to perform maintenance tasks. In:11th International Conference on Through-life Engineering Services - TESConf 2022, 8-9 November 2022, Cranfield UK. Paper number 8350en_UK
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/18685
dc.language.isoenen_UK
dc.publisherCranfield Universityen_UK
dc.relation.ispartofseriesTESConf2022;8350
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectHuman-machine collaborationen_UK
dc.subjectKnowledge graphen_UK
dc.subjectPresecriptive maintenanceen_UK
dc.titleA framework for constructing a common knowledge base for human-machine system to perform maintenance tasksen_UK
dc.typeConference paperen_UK

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11th International Conference on Through-life Engineering Services 2022