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
2001
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Last updated: 2021-11-10
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