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Related Experiment Video

Updated: Sep 27, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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Fault Detection for Nonlinear Dynamic Systems With Consideration of Modeling Errors: A Data-Driven Approach.

Hongtian Chen, Linlin Li, Chao Shang

    IEEE Transactions on Cybernetics
    |April 13, 2022
    PubMed
    Summary

    This study introduces a novel data-driven fault detection (FD) method for nonlinear systems. The approach effectively identifies system parameters and establishes a reliable FD threshold, even with limited data.

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    Area of Science:

    • Engineering
    • Control Systems
    • Machine Learning

    Background:

    • Nonlinear dynamic systems require robust fault detection (FD) methods.
    • Existing techniques often struggle with unknown system nonlinearities and limited data.
    • Data-driven approaches offer a promising alternative for system identification and FD.

    Purpose of the Study:

    • To propose a data-driven fault detection method for nonlinear dynamic systems.
    • To develop a novel parameterization strategy for nonlinear Hammerstein models.
    • To establish a reliable FD threshold considering various error sources.

    Main Methods:

    • A stacked neural network-aided canonical variate analysis (SNNCVA) was developed for nonlinear Hammerstein model identification.
    • A data-driven residual generator was formulated based on the SNNCVA.
    • Quantiles-based learning was employed to determine the FD threshold, accounting for estimation and approximation errors.

    Main Results:

    • The SNNCVA method successfully parameterized nonlinear Hammerstein systems using only input-output data.
    • The proposed residual generator ensured effective fault detection despite unknown system models and nonlinearities.
    • The quantiles-based threshold provided reliable FD performance even with limited available samples.

    Conclusions:

    • The developed data-driven fault detection method, utilizing SNNCVA and quantiles-based learning, is effective for nonlinear dynamic systems.
    • The method demonstrates robustness in the presence of unknown system dynamics and limited data.
    • Successful application to a nonlinear hot rolling mill process validates the proposed approach.