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A Review of Digital Twin for Vehicle Predictive Maintenance System
Technical Paper
2023-01-1024
ISSN: 0148-7191, e-ISSN: 2688-3627
Annotation ability available
Sector:
Event:
2023 AeroTech
Language:
English
Abstract
The 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.
Authors
Topic
Citation
Wang, C., Fan, I., and King, S., "A Review of Digital Twin for Vehicle Predictive Maintenance System," SAE Technical Paper 2023-01-1024, 2023, https://doi.org/10.4271/2023-01-1024.Also In
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