Abstract
Wheel wear is of major interest in railways due to the high cost of wheel reshaping, rail wear, and safety issues in operation. Recent researches employ onboard sensors to evaluate the dynamic behavior of the wagon and determine railway running conditions. However, sensors and computing units are expensive and some maintenance scenarios are impossible to be deliberately generated. In this work, several maintenance parameters for primary suspension, friction wedge, and flange backspacing are evaluated within operational ranges in a Box-Behnken design of experiment (DoE), set in a quadratic surface model. Such a modeling approach uses few experimental points and a significantly small computational cost when compared to a full factorial method, which uses multibody dynamic models on a simulating platform, like Simpack, to determine which parameters are more influential in safety against derailment (Y/Q) and wheel and flange wear (Tγ). The present work evaluates such parameters for heavy-haul gondola rail cars, with meter and broad gauge, running on tangent track and curves, with and without FRA irregularities. The analysis makes it possible to identify non-uniformities in wheel wear and to set different inputs for condition-based maintenance for freight rail wagons. Results show that the primary suspension longitudinal clearance and stiffness affect Y/Q and wear by over 100%, increasing the operational risk and maintenance cost because of wheel wear.
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