Abstract
This paper deals with the fault detection problem in uncertain linear/nonlinear systems having both model mismatch and noise. A robust fault detection method is presented which accounts for the effects of the variance and bias errors caused by the noise and the model mismatch, respectively. The model mismatch includes here linearization error as well as undermodelling. Comparisons are made with alternative fault-detection methods which do not account for model mismatch or linearization errors. The new method is shown to have good performance on a number of simulated systems. These results give support to the potential of this method and suggest it may be worthy of consideration in practical situations.
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