Train failures have hit the media cycles recently with devastating crashes being reported in the Northeast. Fortunately, the Internet of Things is bringing layers of safety and predictive maintenance to the industry. A recent IoT use case provided by BNSF, which owns 32,500 miles of track and 8,000 locomotives, and moves 1,400 trains a day around the West, shows impressive innovation.
BNSF is using IoT applications to monitor heat and environmental factors that can break or pull rails apart or deform wheels. In addition, it is employing a number of IoT sensors to specifically monitor the mechanical performance of wheels on trains. ONe specific situation comes when a wheel’s brakes fail to release as a train starts to move which causes a defective flat spot. A flat spot can lead to dangerous situations. In order to quickly assess when these flat spots occur, BNSF is employing multiple IOT sensors including:
- Force detectors placed on a track to watch for flat spots as a train passes overhead
- Thermal detectors to spot a hot wheel caused by friction from brakes not releasing and dragging
- Video footage to take thousands of images every time a train passes to identify flat spots on wheels
- Microphones to listen to the wheels’ “tone” as they move pass since a bad note could mean a crack or other problem in a wheel
Even further, the company is utilizing drones to survey two to three hundred miles of track.