Using AI to Help Avoid Train Accidents

AI researchers at Stony Brook University have found a way to use artificial intelligence (AI) and guided ultrasonic waves for detecting faults inside switch rails. Their model is a significant advancement from the existing ones, and has the potential to be used for practical purposes, including preventing train accidents.

A recent survey by the International Union of Railways noted how high-speed railway networks are expanding rapidly, and the length of the worldwide high-speed railway network has reached nearly 59,000 km. With an increased demand for faster trains, switch rails (sections of track where trains switch from one direction to another) tend to get more easily damaged — especially in high-speed rail tracks — due to their special structures and heavy workload, increasing 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, discussed the problem: “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 — such as eddy currents, magnetic flux leakage and ultrasonic techniques — are all point-by-point inspection methods with low efficiency. While the first two methods can only detect surface and near surface damage, ultrasonic waves can only detect wide areas of switch rails.

“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.” Since railway tracks are usually only open for repairs at night, guided waves meet the requirements of fast, accurate and reliable scanning of damaged rails.

Read the full story at the AI Innovation Institute website.