AI researchers at Stony Brook University have developed a new model using artificial intelligence and guided ultrasonic waves to detect faults in switch rails. This advancement could be instrumental in preventing train accidents, offering practical applications for railway safety.
The International Union of Railways recently highlighted the rapid expansion of high-speed railway networks, which now span nearly 59,000 kilometers worldwide. With the growing demand for faster trains, switch rails—critical sections where trains change direction—are more susceptible to damage due to their unique structures and heavy usage. This increases the risk of train accidents.
Zhaozheng Yin, SUNY Empire Innovation Associate Professor in Biomedical Informatics and a member of Stony Brook’s AI Innovation Institute, emphasized the importance of maintaining switch rail integrity: “It is important to ensure the switch rails are working perfectly in a high-speed rail system, and so we wanted to look for methods that would not destroy these structures while we were looking for damage.”
Traditional nondestructive testing techniques like eddy currents, magnetic flux leakage, and ultrasonic methods are point-by-point inspections with limited efficiency. While eddy currents and magnetic flux leakage can only identify surface or near-surface damage, ultrasonic waves cover broader areas but still fall short.
“The solution was to use guided waves. These waves propagate over relatively long distances and are sensitive to defects. They also allow us to inspect large areas in a short amount of time,” explained Yin. Given that railway tracks are typically available for repairs only at night, guided waves offer a fast, accurate, and reliable method for scanning damaged rails.
For further details on this research development, visit the AI Innovation Institute website.