Fault Detection and Isolation for a Railway Vehicle by Evaluating Estimation Residuals

The capacity of transport as well as the number of passengers is growing in the railway industry. At the same time, the pressure to reduce service costs rises. Reliability and dependability in complex mechanical systems can be improved by fault detection and isolation methods (FDI). These techniques are key elements for maintenance on demand, which could decrease service cost and time significantly. This work addresses FDI for a railway vehicle: The mechanical model is described as a multibody system, which is excited randomly due to track irregularities. The aim of this work is to detect faults in the suspension system of the vehicle. A Kalman filter is used to estimate the states. In order to detect and isolate faults, the detection error is minimized with multiple Kalman filters. A full scale train model with nonlinear wheel rail contact and nonlinear suspension forces serves as an example for the described techniques. Numerical results for different test cases are presented. For the analysed system it is possible not only to detect a failure of the suspension system from the system's dynamic response, but also to distinguish clearly between different possible causes for the changes in the dynamical behavior.