Suffolk Reporter

AI researchers at Stony Brook University have developed a model using artificial intelligence and guided ultrasonic waves to detect faults in switch rails. This advancement could play a crucial role in preventing train accidents.

A survey by the International Union of Railways highlights the rapid expansion of high-speed railway networks, now reaching nearly 59,000 km worldwide. The demand for faster trains has led to increased damage risk to switch rails, especially on high-speed tracks, due to their unique structures and heavy usage.

Zhaozheng Yin, an associate professor in biomedical informatics at Stony Brook’s AI Innovation Institute, explained the challenge: “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 methods like eddy currents, magnetic flux leakage, and ultrasonic techniques inspect point-by-point with low efficiency. While some methods only detect surface damage, ultrasonic waves cover wide areas but lack precision.

“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.” Guided waves offer fast, accurate scanning during limited repair windows when tracks are available only at night.

Further details can be found on the AI Innovation Institute website.