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Abstract From 2013 to 2022, 1671 derailments have been reported by the Federal Railroad Administration (FRA), 8.2% of which were due to journal bearing defects. The University Transportation Center for Railway Safety (UTCRS) designed an onboard monitoring system that tracks vibration waveforms over time to assess bearing health through three analysis levels. However, the speed of the bearing, a fundamental parameter for these analyses, is often acquired from Global Positioning System (GPS) data, which is typically not available at the sensor location. To solve this issue, this paper proposes to employ Machine Learning (ML) algorithms to extract the speed and other essential features from existing vibration data, eliminating the need for additional speed sensors. Specifically, the proposed method tries to extract the speed information from the signatures that are embedded in the Power Spectral Density (PSD) plot, which enables rapid real-time analysis of bearings while the train is in motion. The rapid extraction of data could be sent to a cloud accessible by train dispatchers and railcar owners for assessment of bearings and scheduling of replacements before defects reach a dangerous size. Eventually, the developed algorithm will reduce derailments and unplanned field replacements and afford rail stakeholders more cost-effective preventive maintenance. 

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.