A Robust Approach for Fault Diagnosis in Railway Suspension Systems

Due to the increased popularity of railway transportation, maximizing the availability of both vehicles and infrastructure is on high demand. To fulfill this requirement, the development of efficient maintenance strategies is in the focus of research in this area. Such strategies require a robust estimation and prediction of the vehicle’s component health states. In this chapter, a sliding-mode-based algorithm is proposed as a solution for the identification of suspension parameters, which are credible representatives of the operational status. The accuracy of the estimation, robustness to model uncertainties, and sensitivity to faults are shown through different test scenarios in a simulation environment.