Model based Online Fault Diagnosis of Automotive Engines using Joint State and Parameter Estimation
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Abstract
In this work, an Extended Kalman Filter (EKF) based tunable diagnoser, which uses a minimal hybrid nonlinear state space model of a spark ignition (SI) four stroke engine, is used for the detection and isolation of a variety of engine system faults including intake manifold leak, injector fault and exhaust manifold leak. The state estimates and innovation sequences from the EKF based estimator are shown to be adequate for the detection and isolation of the faults under consideration. Once a fault is detected and isolated, the diagnoser could be tuned online to perform fault identification by redefining a model/fault parameter as an additional state to be estimated and then performing a joint state and parameter estimation. The engine model and diagnoser are implemented in SimulinkTM and are validated against an AMESimTM model of the engine. For the nominal engine model, the performance of the EKF estimator is compared with two other computationally more expensive nonlinear estimators, namely the Unscented Kalman Filter (UKF) and Rao-Blackwell Particle Filter (RBPF).
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