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Sökning: WFRF:(Somma Alessandra)

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1.
  • De Donato, Lorenzo, et al. (författare)
  • Towards AI-assisted digital twins for smart railways : preliminary guideline and reference architecture
  • 2023
  • Ingår i: Journal of Reliable Intelligent Environments. - : Springer Science and Business Media Deutschland GmbH. - 2199-4668 .- 2199-4676.
  • Tidskriftsartikel (refereegranskat)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.
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  • Dirnfeld, Ruth, et al. (författare)
  • Integrating AI and DTs : challenges and opportunities in railway maintenance application and beyond
  • 2024
  • Ingår i: Simulation (San Diego, Calif.). - : Sage Publications. - 0037-5497 .- 1741-3133.
  • Tidskriftsartikel (refereegranskat)abstract
    • In the last years, there has been a growing interest in the emerging concept of digital twin (DT) as it represents a promising paradigm to continuously monitor cyber-physical systems, as well as to test and validate predictability, safety, and reliability aspects. At the same time, artificial intelligence (AI) is exponentially affirming as an extremely powerful tool when it comes to modeling the behavior of physical assets allowing, de facto, the possibility of making predictions on their potential evolution. However, despite the fact that DTs and AI (and their combination) can act as game-changing technologies in different domains (including the railways), several challenges have to be faced to ensure their effectiveness, especially when dealing with safety-critical systems. This paper provides a narrative review of the scientific literature on DTs for railway maintenance applications, with a special focus on their relationship with AI. The aim is to discuss the opportunities the integration of these two technologies could open in railway maintenance applications (and beyond), while highlighting the main challenges that should be overcome for its effective implementation.
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  • Resultat 1-4 av 4

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