Impact of automated condition monitoring technologies on railroad safety and efficiency

The effectiveness and efficiency of railcar inspection is critical to freight railroad operations, as it directly affects safety, reliability, mainline efficiency, and terminal performance. Current railcar inspection practices are intended to identify defects prior to failure, but they generally do not provide predictive maintenance capabilities due to the limitations of manual, visual inspection. As a result, automated condition monitoring technologies (ACMT) have been developed to monitor rolling stock condition and facilitate predictive maintenance strategies. Through improved railcar condition monitoring, these technologies have the potential to reduce: equipment-caused derailments and in-service failures (ISFs), train delays and variability in mainline operations, and operational waste in railroad terminals. In this thesis, I applied lean manufacturing methods to the railcar maintenance process, quantifying both direct and indirect waste. To assess benefits to mainline efficiency, I used dispatch simulation software to quantify the length and variability of equipment-caused ISFs as a function of traffic level. To assess benefits to terminal efficiency, I compared the railroad terminal to a manufacturing production system and used lean production techniques to identify sources of waste and variability. The results of these analyses indicate that the cost-savings from improved mainline and terminal efficiency through the use of ACMT are comparable in scale to the cost-savings from the potential reduction in equipment-caused derailments. I also present the results of a study investigating the feasibility of a machine vision system to automatically inspect freight car structural underframe components. This research provides a basis for evaluating strategies for cost-effective integration of railcar inspection technology, improved railcar maintenance, and future analyses pertaining to of implemention of ACMT