Digital Twins in Railways : State of the Art, Opportunities, and Guidelines

There is growing interest in the concept of digital twins (DTs) among software engineers and researchers. As an emerging topic, DTs are a promising paradigm to enhance the predictability, safety, and reliability of cyber-physical systems in different domains. Despite the increasing trend, it is continually challenging to decide the best approach to implement DTs. Moreover, to the best of the author's knowledge, it was found that there is a lack of conducted research and no systematic reviews on DTs in the transportation industry, especially in the area of railway systems. Therefore, following the systematic literature review method, this thesis work has identified 363 articles in four digital libraries, of which 60 primary articles were included to address three research questions. The review shows that most of the reviewed articles focus on the railway subarea maintenance and inspection, the DT enabling technology artificial intelligence is the most coupled technology. An in-depth analysis found that most of the articles apply machine learning algorithms and techniques in DTs to detect faults, predict failures, make automated decisions, and monitor health status to optimize railway systems. It was also found that interoperability is the most discussed challenge, where the difficulty is to transmit operational data in real-time and also achieve real-time decision making. Furthermore, the analysis shows several opportunities and advantages of DTs, such as reducing maintenance costs and the positive contribution to a reduction in freight transport by road. Finally, based on the findings of the conducted review, a guideline to support the design of a DT for predictive maintenance in railways in the form of a flowchart is presented and explained