Large-Scale Language Models for PHM in Railway Systems - Potential Applications, Limitations, and Solutions

Prognostics and health management (PHM) technology, by monitoring the faults and degradation of railway systems, predicting the remaining useful life of equipment, and providing maintenance recommendations, can effectively improve the safety and reliability of railway systems. In recent years, large-scale language models (LLMs) like ChatGPT have achieved groundbreaking accomplishments and led a new wave of innovation in various fields. Consequently, the potential impacts of LLMs on PHM applications in railway systems are worth researching and exploring. This paper first introduces the basic principles and technical characteristics of LLMs. Subsequently, it analyzes the potential impacts of these models in PHM applications within railway systems, exploring how they can be applied in various processes of PHM, including operations management, condition monitoring, maintenance recommendations, and knowledge management, to enhance the effectiveness of PHM. Moreover, this study analyzes the limitations of LLMs in PHM applications within railway systems from a practical perspective and discusses relevant solutions accordingly. Based on these solutions, these models are expected to become more specialized and intelligent, playing a crucial role in the maintenance and management of railway systems. Finally, this paper provides an outlook on LLMs’ prospects and research directions in PHM applications within railway systems.