The Electronic Return-less Fuel System (ERFS) manages the delivery of fuel from the fuel tank to the engine. The pressure in the fuel line is electronically controlled by the fuel system control module by speeding up or slowing down the fuel pump. This allows the system to efficiently control the amount of fuel provided to the engine when compared to vehicles equipped with a standard fuel system wherein the fuel pump continuously runs at full speed. A failure in the fuel system that impacts the ability to deliver fuel to the engine will have an immediate effect on system performance. Consequently, improved reliability and availability, and reduction in the number of walk-home situations require efficient fault detection, isolation and prognosis of the ERFS system. This paper develops and implements data-driven fault detection, isolation and severity estimation algorithms for the ERFS. The HIL Fuel System Rig and a Chevrolet Silverado truck were used to collect and analyze the fuel system behavior under different fault conditions. Several data-driven classifiers, such as support vector machines, K- nearest Neighbor, Discriminant analysis, Bayes classifier, Partial- least squares, Quadratic and Linear classifiers, were implemented on a limited set of data for both training and testing. Regression techniques, such as Partial least squares regression and Principle component regression, are used to estimate the severity of faults. The resulting solution approach has the potential to be applicable to a wide variety of systems, ranging from automobiles to aerospace systems.
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