Stochastic Analysis of Transit Wheel Wear and Optimized Forecasting of Wheel Maintenance Requirements

Abstract

As transit vehicle wheels accrue mileage, they experience flange and tread wear based on the contact between the railhead and wheel-running surface. When wheels wear excessively, the likelihood of accidents and derailments increases. Thus, regular maintenance is performed on the wheels, until they require replacement. One common maintenance practice is truing; using a specially designed cutting machine to bring a wheel back to an acceptable profile. This process removes metal from the wheel and is often based on wheel flange thickness standards (and sometimes wheel flange angle). Wheel replacement is usually driven by rim thickness, which is continually reduced by wear and metal removed by truing. This research study used wheel wear data provided by the New York City Transit Authority (NYCTA) to analyze wheel wear trends and forecast wheel maintenance (truing based on flange thickness) and wheel life (replacement based on rim thickness). Using automatic wheel-scanning technology, NYCTA was able to collect wheel profile measurements for nearly 4,000 wheels in its fleet over a six-month period, measured weekly. The resulting wheel measurement data was analyzed using advanced stochastic techniques to determine relationships for the changes in flange thickness over time for each wheel in the fleet. Flange thickness wear rate relationships for each wheel were then used to forecast the time it would take for a wheel to reach the flange thickness maintenance threshold as defined by NYCTA standards. Furthermore, a subpopulation of wheels that exhibited very high rates of wear were classified as “bad actors” and identified for further investigation to understand the cause of accelerated wear. This allows for identification and addressing of causal factors that relate to accelerated wear, such as angle of attack and L/V ratio. NYCTA has recently started capturing such data that relates truck performance, which can be related to rate of wear.