1 Engine Air Path Fault Diagnosis Using Adaptive Neural Classifier
نویسندگان
چکیده
This paper presents a new method for on-board fault diagnosis for the air-path of spark ignition (SI) engines. The method uses an adaptive radial basis function (RBF) neural network to classify pre-defined possible faults from engine measurements to report the type and size of the fault. The RBF fault classifier adapts its widths and weights to model the time-varying dynamics of the engine and disturbance so that the false alarm rate is greatly reduced. The developed scheme is assessed with various faults simulated to a benchmark model and promising results are obtained. Copyright © 2006 USTARTH
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