Fault detection and isolation for an experimental internal combustion engine via fuzzy identification

Certain engine faults can be detected and isolated by examining the pattern of deviations of engine signals from their nominal unfailed values. In this brief paper, we show how to construct a fuzzy identifier to estimate the engine signals necessary to calculate the deviation from nominal engine behavior, so that we may determine if the engine has certain actuator and sensor "calibration faults". We compare the fuzzy identifier to a nonlinear ARMAX technique and provide experimental results showing the effectiveness of our fuzzy identification based failure detection and identification strategy.<>