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Parameter Estimation for Fault Diagnosis of an Automotive Engine using Extended Kalman Filters

A nonlinear state space model of the DaimlerChrysler diesel engine OM611 is used. Three different faults in the air path have been taken under consideration: inlet manifold pressure sensor fault, air mass-flow sensor fault and leakage in the inlet manifold. These faults are modeled and added to the nonlinear state space model. The faults are assumed to be constant during estimation. An extended Kalman filter is used as an observer of the system in order to estimate the different fault-parameters. Only one fault-parameter is monitored at a time. Simulations with high inlet manifold pressure has turned out to give good results, the estimated fault-parameters are close to the true values. For simulations with low pressure in the inlet manifold are the results less good, probably due to model errors. The extended Kalman filter has proved to perform well in this type of application, as an observer for a diagnosis system of an automotive engine

Martin Gunnarsson


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