Model-based fault detection and diagnosis with special input excitation applied to a modern diesel engine

Abstract Due to the rising complexity of many technical processes modern diagnosis systems have to supervise a multitude of hydraulic, mechanical, electromechanical and mechatronic components. Therefore model-based methods of fault-detection and diagnosis have been developed. These methods use mathematical process models to relate data of several measurable variables. Thus the diagnosis quality depends on the available sensor data. In order to obtain additional information with the given sensor configuration special input excitation signals can be used. This paper will describe a method to locate faults in multivariable systems using such input excitation and its application to the intake air system of a modern common rail Diesel engine. The presented method uses the knowledge of fault effects on the measured output, when the inputs are successively excited quasi-stationary, to determine the location of the fault. It has been applied successfully to differentiate air mass sensor faults from other process faults.

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