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Updated: Sep 13, 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 Estimation for Nonlinear Distributed Parameter Systems With External Disturbances Based on Full Iterative

Shuiqing Xu, Li Feng, Lejing Wang

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    Summary
    This summary is machine-generated.

    This study presents a new method for fault estimation in nonlinear systems. The approach accurately identifies faults in both time and space, even with external disturbances.

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    Area of Science:

    • Control Systems Engineering
    • Nonlinear Dynamics
    • Fault Diagnosis

    Background:

    • Nonlinear distributed parameter systems (NDPSs) are complex and prone to faults.
    • Accurate fault estimation is crucial for system reliability and safety.
    • External disturbances complicate fault detection and estimation.

    Purpose of the Study:

    • To develop an innovative approach for simultaneous fault estimation in NDPSs.
    • To address both time-domain and spatiotemporal fault characteristics.
    • To mitigate the impact of external disturbances on fault estimation accuracy.

    Main Methods:

    • An iterative learning observer was designed to capture temporal and spatial variations.
    • A full iterative learning (FIL) technique was employed for fault estimation law development.
    • The λ-norm method was utilized to simplify convergence analysis and gain calculations.

    Main Results:

    • The proposed method enables rapid and precise estimation of fault signals.
    • The approach effectively mitigates the influence of external disturbances.
    • Simulation results confirm the method's efficiency in estimating faults across time and spatiotemporal domains.

    Conclusions:

    • The developed iterative learning observer and FIL technique offer an effective solution for fault estimation in NDPSs.
    • The λ-norm method simplifies the theoretical analysis and practical implementation.
    • The approach demonstrates robust performance in the presence of external disturbances.