Integration of Fault Detection and Identification into a Fault Tolerant Automated Highway System: Final Report

This report is continuation of the work of (Douglas et al., 1996) and (Douglas et al., 1997) which concerns vehicle fault detection and identification.  A vehicle health monitoring approach based on analytical redundancy is described. 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. Sensor noise, process disturbances, system parameter variations, unmodeled dynamics and nonlinearities can distort these static patterns.   A Shiryayev sequential probability ratio test that has been extended to multiple hypotheses examines the fault detection filter and parity equation residuals and generates the probability of the presence of a fault.  A point design of fault detection filters and parity equations is developed for the longitudinal dynamics of the PATH Buick LeSabre. Fault detection filters are evaluated using empirical data obtained from U.C. Berkeley. The preliminary evaluation is promising in that the fault detection filters can detect and identify actuator and sensor faults as expected even under various disturbances and uncertainties.