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Rapid isolation of small oscillation faults via deterministic learning
Author(s) -
Chen Tianrui,
Wang Cong
Publication year - 2012
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.2326
Subject(s) - fault detection and isolation , estimator , isolation (microbiology) , nonlinear system , fault (geology) , control theory (sociology) , sensitivity (control systems) , computer science , scheme (mathematics) , engineering , mathematics , artificial intelligence , statistics , electronic engineering , control (management) , actuator , physics , quantum mechanics , seismology , microbiology and biotechnology , biology , geology , mathematical analysis
SUMMARY 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 the residuals are provided. The occurrence of a fault can be isolated if all the norms of the residuals associated with the matched fault estimator become smaller than the ones of the residuals associated with the other estimators in a finite time. Rigorous analysis of the performance of the both isolation schemes is also given, which includes the fault isolability condition and isolation time. 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. Copyright © 2012 John Wiley & Sons, Ltd.