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Small Fault Detection for a Class of Closed-Loop Systems via Deterministic Learning | IEEE Journals & Magazine | IEEE Xplore

Small Fault Detection for a Class of Closed-Loop Systems via Deterministic Learning


Abstract:

In this paper, based on the deterministic learning (DL) theory, an approach for detection for small faults in a class of nonlinear closed-loop systems is proposed. First,...Show More

Abstract:

In this paper, based on the deterministic learning (DL) theory, an approach for detection for small faults in a class of nonlinear closed-loop systems is proposed. First, the DL-based neural control approach and identification approach are employed to extract the knowledge of the control effort that compensates the fault dynamics (change of the control effort) and the fault dynamics (the change of system dynamics due to fault). Second, two types of residuals are constructed. One is to measure the change of system dynamics, another one is to measure change of the control effort. By combining these residuals, an enhanced residual is generated, in which the fault dynamics and the control effort are combined to diagnose the fault. It is shown that the major fault information is compensated by the control, and the major fault information is double in the enhanced residual. Therefore, the fault information in the diagnosis residual is enhanced. Finally, an analysis of the fault detectability condition of the diagnosis scheme is given. Simulation studies are included to demonstrate the effectiveness of the approach.
Published in: IEEE Transactions on Cybernetics ( Volume: 49, Issue: 3, March 2019)
Page(s): 897 - 906
Date of Publication: 27 February 2018

ISSN Information:

PubMed ID: 29994593

Funding Agency:


References

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