A review of fault diagnosis for train signal system based on multi-source information fusion

The train signal system is crucial to ensuring the safe operation of trains. However, with long-term operation, various signal system components degrade to different degrees, leading to potential faults that pose risks to railway operations. Among them, switch machine, track circuit, axle counter, signal lamp, and power equipment are the most common and critical signal devices. This paper systematically analyzes these key signal devices' fault causes and diagnosis methods, introducing the fundamental principles and applicable conditions of different diagnostic techniques. Furthermore, this paper explores the application of multi-source information fusion technology in train fault diagnosis, highlighting its advantages in integrating sensor data from multiple sources to enhance fault identification and localization. Finally, the study summarizes the challenges of current train signal system fault diagnosis. It outlines future research directions, emphasizing the need for more intelligent, automated, and data-driven diagnostic systems to ensure railway safety and reliability.