Rapid isolation of small oscillation faults via deterministic learning

In this paper, we investigate the small fault isolation problem for a class of nonlinear uncertain systems. First, by utilizing the learned knowledge obtained through a recently proposed deterministic learning (DL) approach, a bank of estimators is constructed to represent the training normal mode and oscillation faults. Second, two isolation schemes based on the norms of residuals are provided. The occurrence of a fault can be isolated according to smallest residual principle. Rigorous analysis of the performance of the both isolation schemes is also given. The attraction of the paper lies in that an approach for fault isolation is proposed, in which the knowledge of modeling uncertainty and nonlinear faults obtained through DL is utilized to enhance the sensitivity of the isolation scheme. Simulation studies are included to demonstrate the effectiveness of the approach.