Abstract
This paper proposes a novel data-driven fault detection (FD) method for Lipschitz nonlinear systems. The proposed method is developed by considering that the sample size of training data is limited, while the global system nonlinearity is taken into account. It is a nonparametric approach and consists of two FD versions corresponding to open-loop and closed-loop systems, respectively. It achieves a tradeoff between approximation and estimation errors. By quantifying the unknown modeling error that is closely related to the threshold used in FD tasks, an upper bound is obtained so that trial-and-error for finding the threshold can be avoided. The effectiveness of the proposed data-driven schemes is illustrated by two simulation studies.
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