A Systematic Mapping Study of Digital Twins for Diagnosis in Transportation
This work identifies a research gap in diagnostic reasoning for transportation systems, which is incremental as it builds on existing digital twin literature.
The study conducted a systematic mapping of digital twins for diagnosis in transportation, finding that few papers describe diagnostic processes and most approaches are limited to monitoring or fault detection.
In recent years, digital twins have been proposed and implemented in various fields with potential applications ranging from prototyping to maintenance. Going forward, they are to enable numerous efficient and sustainable technologies, among them autonomous cars. However, despite a large body of research in many fields, academics have yet to agree on what exactly a digital twin is -- and as a result, what its capabilities and limitations might be. To further our understanding, we explore the capabilities of digital twins concerning diagnosis in the field of transportation. We conduct a systematic mapping study including digital twins of vehicles and their components, as well as transportation infrastructure. We discovered that few papers on digital twins describe any diagnostic process. Furthermore, most existing approaches appear limited to system monitoring or fault detection. These findings suggest that we need more research for diagnostic reasoning utilizing digital twins.