Sensor Fault Detection for Rail Vehicle Suspension Systems With Disturbances and Stochastic Noises

This paper develops a sensor fault detection scheme for rail vehicle passive suspension systems, using a fault detection observer, in the presence of uncertain track regularity and vehicle noises that are modeled as external disturbances and stochastic process signals. To design the fault detection observer, the suspension system states are augmented with the disturbances treated as new states, leading to an augmented and singular system with stochastic noises. Using system output measurements, the observer is designed to generate the needed residual signal for fault detection. Existence conditions for observer design are analyzed and illustrated. In term of the residual signal, both fault detection threshold and fault detectability condition are obtained, to form a systematic detection algorithm. Simulation results on a realistic vehicle system model are presented to illustrate the observer behavior and fault detection performance.