A Probabilistic Approach for Fault Detection of Railway Suspensions

Abstract

Vehicle dynamics and safety against derailment are directly influenced by the primary and secondary suspension of a railway vehicle. During the operation faults of components like broken springs or dampers can occur. To prevent a complete system failure, the early detection of faults in the suspension of trains is thus of high importance. For the application of a vibration-based fault detection system several acceleration sensors can be mounted on the frame of the bogie. The signals of these sensors are collected during operation and are available for the application of online monitoring methods. In this publication, we present a probabilistic method to distinguish the faulty and the fault-free cases. The utilization of multiple sensor signals from one bogie for the fault detection and diagnosis yields a performance increase. This is conducted by the application of a subspace-based system identification algorithm. Afterwards, identified mode-shapes, damping factors and eigenfrequencies are considered in a probabilistic approach to distinguish the different failure causes. In this methodically new approach probability density plots are used to describe the most likely values of the eigenfrequencies and other characteristic parameters in the faultfree and faulty case. Comparing new data to the density plots determined in a previous training phase allows to assess if a failure has occurred or is likely to occur and what type of fault is the most likely one.