In the last years, there has been a growing interest in the emerging concept of digital twins (DTs) among software engineers and researchers. DTs not only represent a promising paradigm to improve product quality and optimize production processes, but they also may help enhance the predictability and resilience of cyber-physical systems operating in critical contexts. In this work, we investigate the adoption of DTs in the railway sector, focusing in particular on the role of artificial intelligence (AI) technologies as key enablers for building added-value services and applications related to smart decision-making. In this paper, in particular, we address predictive maintenance which represents one of the most promising services benefiting from the combination of DT and AI. To cope with the lack of mature DT development methodologies and standardized frameworks, we detail a workflow for DT design and development specifically tailored to a predictive maintenance scenario and propose a high-level architecture for AI-enabled DTs supporting such workflow.
In the last years, there has been a growing interest in the emerging concept of Digital Twins (DTs) among soJware engineers and researchers. DTs represent a promising paradigm to enhance the predictability, safety, and reliability of cyber-physical systems. They can play a key role in different domains, as it is also witnessed by several ongoing standardisa2on ac2vi2es. However, several challenging issues have to be faced in order to effec2vely adopt DTs, in par2cular when dealing with cri2cal systems. This work provides a review of the scien2fic literature on DTs in the railway sector, with a special focus on their rela2onship with Ar2ficial Intelligence. Challenges and opportuni2es for the usage of DTs in railways have been iden2fied, with interoperability being the most discussed challenge. One difficulty is to transmit opera2onal data in real-2me from edge systems to the cloud in order to achieve 2mely decision making. We also provide some guidelines to support the design of DTs with a focus on machine learning for railway maintenance.
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