Rapid fault isolation for a class of nonlinear lipschitz systems via deterministic learning

Early isolation of small faults is an important issue in the literature of fault diagnosis. In this paper, for a class of nonlinear lipschitz systems with output measurements, an approach for rapid isolation of small oscillation faults is presented. By utilizing the knowledge obtained through the deterministic learning, a bank of estimators is constructed for the training normal mode and oscillation faults. The occurrence of a fault can be isolated if all the residual norms associated with the matched fault estimator become smaller than the ones associated with the other estimators in a finite time. Finally, based on the concept of fault mismatched function, rigorous analysis of the performance of the isolation schemes is given. The attractions of the paper lie in that the sensitivity to small faults is enhanced through using the learned knowledge of modeling uncertainty and nonlinear faults with output measurements.

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