A Failure Detection and Isolation system for an automotive power train is developed and implemented to detect and isolate faults in engine speed, torque converter turbine speed, and wheel speed. A bank of extended Kalman filters, with different observation combinations and specially formulated gains to maximize robustness, is used to generate residuals. This method of gain selection increases the robustness of the residuals to parameter variations and unmodeled dynamics. Results of this model based Failure Detection and Isolation method utilizing fully nonlinear state equations is experimentally validated using a research vehicle.
A robust failure detection and isolation method for automotive power train sensors
Robustes Fehlerdiagnosesystem für Sensoren im Antriebsstrang von Automobilen
1992
7 Seiten, 11 Bilder, 15 Quellen
Aufsatz (Konferenz)
Englisch
Fehlerortung , Fehlererkennung , Motorleistung , Diagnoseverfahren , Kalman-Filter , Verfahrensparameter , Nichtlinearität , Zustandsgleichung , Zustandsüberwachung , Kraftfahrzeug , Kraftübertragung , mathematisches Verfahren , Drehzahl , Sensor , dynamisches Verhalten , Simulationsmodell , Modellsimulation
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