Testing and Evaluation of Robust Fault Detection and Identification for a Fault Tolerant Automated Highway System

This report concerns vehicle fault detection and identification. A vehicle health monitoring approach based on analytical redundancy is described. To detect and identify actuator and sensor faults, fault detection filters and parity equations are developed for the longitudinal dynamics of the PATH Buick LeSabre. Fault detection filters and parity equations use the control commands and sensor measurements to generate the residuals which have a unique static pattern in response to each fault. This allows the faults not only to be detected, but also identified. Fault detection filters and parity equations are .rst evaluated using simulated data generated by a high-fidelity vehicle simulation. Then, fault detection filters and parity equations are evaluated using empirical data recorded when driving a PATH Buick LeSabre at Crow's Landing. Finally, a real-time testing environment is developed using Linux operating system and C language. This allows the fault detection filters and parity equations to be evaluated in real-time on a PATH Buick LeSabre. The real-time evaluation at Crow's Landing demonstrates that the fault detection .lters can detect and identify actuator and sensor faults as expected even under various disturbances and uncertainties including sensor noise, road noise, system parameter variations, unmodeled dynamics and nonlinearities.