Roadmaps for AI integration in the raiL Sector

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Published June 12, 2023 | Version v1
Journal article Open

Towards AI-assisted Digital Twins for Smart Railways: Preliminary Guideline and Reference Architecture

Authors/Creators

  • 1. Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy
  • 2. Department of Computer Science and Media Technology, Linnaeus University, Växjö, Sweden
  • 3. Department of Computer Science and Media Technology, Linnaeus University, Växjö, Sweden; and School of Innovation, Design and Engineering, Mälardalen University, Eskilstuna, Sweden

Description

Abstract:

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.

 

Fundings and Disclaimer:

This research has received funding from the Shift2Rail Joint Undertaking (JU) under grant agreement No 881782 RAILS (Roadmaps for Artificial Intelligence (A.I.) integration in the raiL Sector). The JU receives support from the European Union’s Horizon 2020 research and innovation programme and the Shift2Rail JU members other than the Union.

The information and views set out in this document are those of the author(s) and do not necessarily reflect the official opinion of Shift2Rail Joint Undertaking. The JU does not guarantee the accuracy of the data included in this document. Neither the JU nor any person acting on the JU’s behalf may be held responsible for the use which may be made of the information contained therein.

 

Publication Notes:

This Journal Article is available in Open Access at: https://link.springer.com/article/10.1007/s40860-023-00208-6

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Additional details

Funding

European Commission
RAILS - Roadmaps for A.I. integration in the raiL Sector 881782