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    Area of Science:

    • Control Systems Engineering
    • Nonlinear System Analysis
    • Fault Detection and Diagnosis

    Background:

    • Nonlinear closed-loop systems are susceptible to small faults that can compromise performance.
    • Existing fault detection methods may struggle with the complexities of nonlinear dynamics and control efforts.

    Purpose of the Study:

    • To propose a novel fault detection approach for nonlinear closed-loop systems using deterministic learning (DL).
    • To enhance the sensitivity and accuracy of small fault detection by leveraging control effort knowledge.

    Main Methods:

    • Utilizing deterministic learning (DL) based neural control and identification to extract fault dynamics and control effort knowledge.
    • Constructing two types of residuals: one for system dynamics change and one for control effort change.
    • Generating an enhanced residual by combining the two types for improved fault diagnosis.

    Main Results:

    • The proposed method effectively compensates for fault dynamics through the control effort.
    • Major fault information is amplified ('doubled') in the enhanced residual, improving detectability.
    • Simulation studies validate the effectiveness of the enhanced fault diagnosis scheme.

    Conclusions:

    • The deterministic learning-based approach significantly enhances fault information in the diagnosis residual.
    • The method offers improved detection capabilities for small faults in nonlinear closed-loop systems.
    • Analysis of fault detectability conditions provides theoretical grounding for the proposed scheme.