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In this paper, an approach for rapid fault detection for a class of nonlinear sampled-data systems is proposed. Firstly, a learning estimator is constructed to capture the unknown system dynamics effects in sampled-data systems. The key issue in the learning process is that partial neural weights will converge into their optimal values based on the deterministic learning theory. Then a knowledge bank...
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...
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...
In this paper, we propose an approach for rapid detection of nonlinear oscillation systems. The approach consists of two phases: the training (learning) phase and the test (diagnosis) phase. In the training or learning phase, for an oscillation system in both normal and fault modes, a partial persistence of excitation (PE) condition is guaranteed to be satisfied by using localized radial basis function...
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