A review of digital twin for vehicle predictive maintenance system

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dc.contributor.authorWang, Chengwei
dc.contributor.authorFan, Ip-Shing
dc.contributor.authorKing, Stephen
dc.date.accessioned2023-07-10T15:48:58Z
dc.date.available2023-07-10T15:48:58Z
dc.date.issued2023-03-07
dc.description.abstractThe development of Digital Twin (DT) has become popular. A dominant description of DT is that it is a software representation that mimics a physical object to portray its real-world performance and operating conditions of an asset. It uses near real-time data captured from the asset and enables proactive optimal operation decisions. There are many other definitions of DT, but not many explicit evaluations of DT performance found in literature. The authors have an interest to investigate and evaluate the quality and stability of appropriate DT techniques in real world aircraft Maintenance, Repair, and overhaul (MRO) activities. This paper reviews the origin of DT concept, the evolution and development of recent DT technologies. Examples of DTs in aircraft systems and transferable knowledge in related vehicle industries are collated. The paper contrasts the benefits and bottlenecks of the two categories of DT methods, Data-Driven (DDDT) and Model-Based (MBDT) models. The paper evaluates the applicability of the two models to represent vehicle system management. The authors present their methodological approach on Predictive Maintenance (PM) development basing on reliable DT models for vehicle systems. This paper contributes to design, operation, and support of aircraft/vehicle systems.en_UK
dc.identifier.citationWang C, Fan IS, King S. (2023) A review of digital twin for vehicle predictive maintenance system. In: 2023 AeroTech, 14-16 March 2023, Fort Worth, USA. Paper number SAE 2023-01-1024en_UK
dc.identifier.eissn2688-3627
dc.identifier.issn0148-7191
dc.identifier.urihttps://doi.org/10.4271/2023-01-1024
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/19950
dc.language.isoenen_UK
dc.publisherSociety of Automotive Engineersen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectDigital Twinen_UK
dc.subjectPredictive Maintenanceen_UK
dc.subjectAircraft Inspectionen_UK
dc.subjectPrognosticsen_UK
dc.titleA review of digital twin for vehicle predictive maintenance systemen_UK
dc.typeConference paperen_UK

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