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.

中文翻译：

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人工智能与数字技术的融合：铁路维护应用及其他领域的挑战与机遇

在过去的几年里，人们对新兴的数字孪生（DT）概念越来越感兴趣，因为它代表了一种有前途的范例，可以持续监控网络物理系统，以及测试和验证可预测性、安全性和可靠性。与此同时，在对实物资产的行为进行建模时，人工智能 (AI) 被认为是一种极其强大的工具，事实上，它可以预测其潜在的演变。然而，尽管数字孪生和人工智能（及其组合）可以在不同领域（包括铁路）充当改变游戏规则的技术，但要确保其有效性，尤其是在处理安全关键系统时，必须面对一些挑战。本文对铁路维护应用 DT 的科学文献进行了叙述性回顾，特别关注它们与人工智能的关系。目的是讨论这两种技术的集成在铁路维护应用（及其他应用）中可能带来的机遇，同时强调其有效实施应克服的主要挑战。