The Educational Railroading Conference Leader Since 1994


Brad Hopkins

Brad Hopkins
Amsted Rail


Wheel Health Monitoring Using Onboard Sensors: Slid Flat Detection and Profile Wear Estimation

Wheel flat spots, caused by a slid wheel from hand brakes being left on during motion or other operational conditions, can result in costly damage to the wheel and rail. A repeated high impact force concentrated in a small contact area during a short duration of time may result in the propagation of subsurface cracks in both the wheel and the rail, which may eventually lead to a catastrophic wheel or rail failure. Wheel Impact Load Detectors (WILDs) integrated into the track are valuable for detecting high-impact wheels, but are not always able to provide real-time insight into the health of the wheel due to incomplete rail network coverage. Onboard monitoring provides a possible solution to this problem by providing information that may be related to wheel health, either on demand or in frequent intervals, as well as presenting the status of the handbrake during motion so that action may be taken to avoid damage. This presentation covers a technique which utilizes low-cost, low-power microelectromechanical (MEM) sensors mounted to the truck system for monitoring wheel health. The results show that the technique adequately detects wheel impacts from a noisy signal.

Additionally, a technique for monitoring wheel profile wear is also presented. Various patterns and combinations of wheel profile wear can result in undesirable and potentially damaging dynamic behavior in the truck and car system. While Truck Hunting Detectors (THDs) and Truck Performance Detectors (TPDs) provide an assessment of truck dynamics, the once again infrequent placement throughout the North American rail network means that a long period of time may pass before a poorly performing truck is identified. An onboard system, however, has the capability of monitoring truck performance in near real-time. The technique presented utilizes onboard, truck-mounted MEMs accelerometers and relates truck acceleration behavior to wheel wear patterns. The results show that under certain operating conditions the technique can accurately estimate the wheel profile pattern.