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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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    This study introduces a novel framework for industrial process fault detection. It effectively separates temporal and spatial information for accurate anomaly identification and isolation.

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    Area of Science:

    • Process Engineering
    • Data Science
    • Control Systems

    Background:

    • Industrial processes exhibit complex temporal and spatial dependencies.
    • Existing fault detection methods struggle to effectively separate these dependencies, leading to inaccuracies.
    • Inappropriate representation of process characteristics hinders precise fault isolation.

    Purpose of the Study:

    • To propose an explicit representation and customized fault isolation framework.
    • To accurately identify and locate anomalies based on their temporal and spatial characteristics.
    • To improve fault detection and isolation in industrial processes.

    Main Methods:

    • A double-level separation method for temporal and spatial information using independent auto-encoding modules.
    • An information aliasing loss function to enhance the distinction between temporal and spatial characteristics.
    • A customized isolation strategy quantifying intravariable temporal dynamics and intervariable spatial graph structure.

    Main Results:

    • Successfully separated temporal and spatial information, enabling explicit statistics monitoring.
    • Achieved accurate determination of spatiotemporal dependencies for anomaly isolation.
    • Demonstrated effective characterization and isolation of temporal impact and spatial propagation of faults.
    • Validated the framework on numerical, thermal power plant, and Tennessee Eastman benchmark processes.

    Conclusions:

    • The proposed framework effectively tackles temporal and spatial characteristics in industrial processes.
    • Explicit representation and customized isolation enhance fault detection and localization accuracy.
    • The method provides a robust solution for complex industrial process monitoring and control.