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Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
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When analyzing a single line-to-ground fault from phase A to ground at a three-phase bus, it is important to consider the fault impedance. This impedance is zero for a bolted fault, equal to the arc impedance for an arcing fault, and represents the total fault impedance for a transmission-line insulator flashover. To derive sequence and phase currents, fault conditions are translated from the phase domain to the sequence domain.
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In a series resistor-inductor (R-L) circuit, closing the switch at the start of the time period simulates a three-phase short circuit, a fault condition where all three phases of an unloaded synchronous machine are short-circuited. When there is no fault impedance and no initial current, the initial voltage is determined by the phase angle of the source voltage.
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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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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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Stochastic Event-Triggered Fault Detection and Isolation Based on Kalman Filter.

Hamideh Jafari, Javad Poshtan, Saeed Shamaghdari

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    This study introduces an advanced fault detection and isolation (FDI) Kalman filter using stochastic event-triggered schedulers. The method effectively detects and isolates faults in induction motors while minimizing communication rates.

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    Area of Science:

    • Control Systems Engineering
    • Signal Processing
    • Electrical Engineering

    Background:

    • Discrete linear systems with unknown inputs and colored measurement noise present challenges for robust fault detection and isolation (FDI).
    • Traditional Kalman filters may require approximations when dealing with non-zero mean, colored noise, and unknown inputs, impacting performance.
    • Event-triggered control strategies offer potential for reducing communication load in FDI systems.

    Purpose of the Study:

    • To develop a robust FDI Kalman filter enhanced with stochastic event-triggered schedulers for discrete linear systems.
    • To address challenges posed by deterministic/stochastic unknown inputs with nonzero mean and colored measurement noise.
    • To optimize the trade-off between communication rate and FDI performance.

    Main Methods:

    • A subspace projection method is employed to attenuate disturbance effects.
    • A fusion method is utilized to manage colored measurement noise in Kalman filter design.
    • Stochastic event-triggered schedulers, modeled as Gaussian functions, are integrated to preserve innovation sequence properties.
    • Convex optimization is used to determine design parameters for minimizing communication and maximizing FDI performance.

    Main Results:

    • The proposed FDI method effectively detects and isolates faults, specifically stator interturn short circuits and broken rotor bars in three-phase induction motors.
    • The use of Gaussian-based stochastic event-triggered schedulers preserves the innovation sequence's Gaussian property, avoiding approximate recursive equations.
    • Convex optimization successfully achieved a low communication rate between sensor nodes and the FDI filter while ensuring high FDI performance.

    Conclusions:

    • The developed robust FDI Kalman filter with stochastic event-triggered schedulers provides an effective solution for fault diagnosis in discrete linear systems.
    • The method demonstrates superior performance in handling unknown inputs and colored measurement noise, validated through induction motor fault analysis.
    • The approach offers a promising strategy for efficient and reliable condition monitoring in industrial applications.