With the need for rail systems to provide long distance and highly reliable operations, problems that could arise should be quickly detected and addressed such that they do not become major issues. If undetected, sudden component faults can result in costly and difficult repair scenarios, and in the worst case, derailments, which can be catastrophic if they occur in populated areas. One such problem, Bearing Burn-off, can result in significant damage to the cars as well as to the rail infrastructure, often before the symptoms can be detected by rail crews and in most situations by wayside mounted infrastructure. A system developed by IONX for railroad operations is designed to monitor rail car bearings to detect potential burn-off conditions before they arise and before they become destructive. This system is comprised of a Central Monitoring Unit (CMU) and Wireless Sensor Nodes (WSNs) which continuously monitor bearing temperatures as well as the current ambient temperature. The application of various algorithms not only conducts trend analysis to anticipate burn-off events, but also reports events at predetermined temperature limits to provide early warning and immediately actionable feedback to locomotive engineers. With these alerts, the driver or train controller can then decide on the appropriate action to be taken and destructive consequences can be avoided saving operational and infrastructure costs. For this pilot study, a total of ten freight cars and fifteen locomotives were equipped, operated, and monitored for an initial three month period. This paper presents preliminary operational results for these tests demonstrating the benefits of predictive condition monitoring systems in real-world applications.

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