Integrating AI and DTs: challenges and opportunities in railway maintenance application and beyondShow others and affiliations
2024 (English)In: Simulation (San Diego, Calif.), ISSN 0037-5497, E-ISSN 1741-3133Article in journal (Refereed) Epub ahead of print
Abstract [en]
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.
Place, publisher, year, edition, pages
SAGE PUBLICATIONS LTD , 2024.
Keywords [en]
Digital twin, railway, artificial intelligence, machine learning, cyber-physical system, Internet of things
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-66178DOI: 10.1177/00375497241229756ISI: 001163924700001Scopus ID: 2-s2.0-85185910530OAI: oai:DiVA.org:mdh-66178DiVA, id: diva2:1842855
2024-03-062024-03-062024-03-06Bibliographically approved