Compressed air leak detector could save fuel and labour costs for rail operators

This is the claim of the Texas-based Southwest Research Institute (SwRI), which has developed a proof-of-concept system to detect the air leaks and relay the location of them to personnel for repair.

Trains use compressed air for functions including air brakes, valve actuation, and radiator shutters. Each year it is estimated that the rail industry loses between 2-3 per cent vehicle efficiency due to air leaks, which can have a detrimental effect on train operability and safety.

“Air leaks significantly increase fuel consumption and reduce the effectiveness of a locomotive’s automatic engine stop-start (AESS) systems, which causes locomotives to run more often, burn more fuel and reduces the lifespan of parts such as starters, air compressors and batteries,” said SwRI lead engineer Christopher Stoos. “We are talking potentially saving millions of gallons of fuel and reducing carbon dioxide, oxides of nitrogen and particulate matter emissions.”

Finding air leaks requires a manual search, with staff going on, under or between railway vehicles to listen or feel for leaks. The practice is inefficient, time-consuming and introduces unnecessary risk to mechanical staff, prompting the US Federal Railroad Administration to define acceptable air leak rates for trains.

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To reduce these leaks, SwRI has created a system that uses audio detection technology, cameras and machine learning to autonomously detect, identify and report air leaks. The project is funded by the US Transportation Research Board’s (TRB) Rail Safety IDEA program and led by Stoos, senior research engineer Heath Spidle and research engineer Jake A. Janssen.

The system uses a Fluke SV600 fixed acoustic imager that uses a 64-microphone array and camera tuned to detect frequencies of 30-45kHz, the frequencies at which compressed air leaks best stand out from most background noise. This instrument works with a secondary visual spectrum camera. To automate the detection process, the team trained and implemented machine learning algorithms to identify air leaks from the sensor outputs while ignoring non-leak related outputs.

During testing, the prototype system is said to have detected a range of air leaks at various locations on locomotives with a false positive rate of 0.03 per cent. The system detected, on average, 11 out of every 13 leaks on a moving train. Once an air leak was identified, an alert with an accompanying image was shared electronically, showing the area in need of inspection and repairs.

"The system should reduce the burden on mechanical personnel and improve the compressed air system’s performance,” Stoos said in a statement. “Further field development and testing is still necessary, but this system could potentially save the locomotive industry millions in fuels savings and maintenance if implemented correctly. This technology could also greatly reduce greenhouse gas emissions by improving locomotive fuel efficiency."

The SwRI team shared a presentation detailing the development of the system and their findings, titled Autonomous Detection of Compressed Air Leaks on Trains, at the 2022 Rail Supply Institute’s Expo & Technical Conference in Fort Worth.