A fault-tolerant sensory diagnostic system for intelligent vehicle application

A properly designed automotive sensor monitoring and diagnostic system must be capable of detecting and distinguishing sensor and component malfunctions in the presence of signal noise, varying vehicle operating conditions and multiple faults. The technique presented in this paper addresses these objectives through the implementation of a multivariate state estimation algorithm based upon pattern recognition methodology coupled with a statistically-based hypothesis test. Utilizing a residual signal vector generated from the difference between the estimated and measured current states of a system, disturbances are detected and identified with statistical hypothesis testing. Since the hypothesis testing utilizes the inherent noise on the signals to obtain a conclusion and the state estimation algorithm requires only a majority of the sensors to be functioning to ascertain the current state, this technique has proven to be quite robust and fault-tolerant. Several examples of its application are presented.