Currently, there are two types of defect detection systems used to monitor the health of freight railcar bearings in service: wayside hot-box detection systems and trackside acoustic detection systems. These systems have proven to be inefficient in accurately determining bearing health, especially in the early stages of defect development. To that end, a prototype onboard bearing condition monitoring system has been developed and validated through extensive laboratory testing and a designated field test in 2015 at the Transportation Technology Center, Inc. in Pueblo, CO. The devised system can accurately and reliably characterize the health of bearings based on developed vibration thresholds and can identify defective tapered-roller bearing components with defect areas smaller than 12.9 cm² while in service.

Wayside hot-box detectors (HBDs) are devices used to assess the health of railcar components including bearings, axles, and brakes by monitoring their temperatures. HBDs use infrared (IR) sensors to record the temperatures of railroad bearings. Bearings that trigger an alarm or exhibit warm trending are removed and sent for inspection. In many cases, no discernable defects were found in the flagged bearings. Motivated by this finding, an investigation was conducted which included performing a controlled field test as well as exhaustive laboratory testing utilizing an HBD simulator. Data acquired from field and laboratory testing was used to evaluate the accuracy and efficacy of wayside HBDs. The results suggest that the scanning location on the bearing cup significantly affects the temperature measurement. Different calibrations for the field- and laboratory-acquired data were also explored. An optimized calibration technique along with proper IR sensor alignment can markedly improve the accuracy of HBD measurements.

In the railroad industry, monitoring the condition of key components such as bearings and wheels is vital to ensure the safe transport of goods and commodities. Bearing seizures are amongst the most dangerous types of failures experienced by trains because they occur unexpectedly and may lead to costly derailments. Current bearing health monitoring techniques include tracking the temperature and acoustic emissions given by the bearings. Although temperature histories of railroad tapered roller bearings are readily available, the literature does not provide information relating the temperature profiles to the severity of the bearing defect. The study presented here investigates the correlation between temperature profiles and bearing defect severity measured by the size of spalls present on bearing outer (cup) and inner (cone) rings. The temperature data used for this study was acquired from defective and healthy bearings that were run at various operating load and speed conditions. The data presented here provides the railroad industry with a greater understanding of the thermal behavior of defective bearings, which can be used to assess the future needs of bearing condition monitoring systems.

A thermoelectric energy harvesting device is evaluated to power a bearing health monitoring system. Unlike wayside equipment, the new system is an onboard wireless solution utilizing accelerometer and temperature sensors to assess the bearing condition continuously. The harvesting system consists of two thermoelectric generator modules with aluminium heat sinks, a switching boost converter, a battery management circuit, and a lithium rechargeable battery. The performance of the harvester is validated on an AAR class bearing mounted on a laboratory tester, with load and speed simulating common freight routes of up to 896 miles. The energy harvested varies with operating conditions, and data is presented showing the effect of load and speed. Over a realistic route, the net energy harvested is more than double that needed to indefinitely power a Bluetooth Low Energy sensor. The critical design parameters are the ratio of open-circuit voltage to the temperature difference for the thermoelectric module, and the cold start voltage of the boost converter.

Rolling contact fatigue (RCF) is one of the major causes of failure in railroad bearings used in freight service. Subsurface inclusions resulting from impurities in the steel used to fabricate the bearings initiate subsurface fatigue cracks which propagate upwards and cause spalling of the rolling surfaces. These spalls start small and propagate as continued operation induces additional crack formation and spalling. Studies have shown that the bearing temperature is not a good indicator of spall initiation. In many instances, the temperature of the bearing increases markedly only when the spall has spread across major portions of the raceway. In contrast, vibration signatures can be used to accurately detect spall initiation within a bearing and can track spall deterioration. No monitoring technique can indicate the growth rate of a spall nor determine residual useful life. Hence, the main objective of this study is to develop reliable prognostic models for spall growth within railroad bearings that are based on actual service life testing rather than theoretical simulations. The data used to devise the models presented here has been acquired from laboratory and field testing that started in 2010. Growth models are provided for spalls initiating on the bearing inner (cone) and outer (cup) rings. Coupling these prognostic models with a previously developed vibration-based bearing condition-monitoring algorithm will provide the rail industry with an efficient tool that can be used to plan proactive maintenance schedules that will mitigate unnecessary and costly train stoppages and delays and will prevent catastrophic derailments.