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Related Concept Videos

Fault Types01:18

Fault Types

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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.
For line-to-line faults occurring between phases B and C, the...
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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Types of Errors: Detection and Minimization01:12

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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.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
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Three-Phase Short Circuit—Unloaded Synchronous Machine01:21

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Conducting a three-phase short circuit test on an unloaded synchronous machine helps understand its impact on the system. The AC fault current's oscillogram, with the DC offset removed, reveals that the waveform amplitude decreases from an initially high value to a steady-state level for one phase of the machine.
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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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CAFD: Context-Aware Fault Diagnostic Scheme towards Sensor Faults Utilizing Machine Learning.

Umer Saeed1, Young-Doo Lee1, Sana Ullah Jan2

  • 1School of Electrical Engineering, University of Ulsan, Ulsan 44610, Korea.

Sensors (Basel, Switzerland)
|January 22, 2021
PubMed
Summary

This study introduces a lightweight machine learning system for detecting sensor faults in Wireless Sensor Networks (WSNs). The Context-Aware Fault Diagnostic (CAFD) scheme accurately identifies subtle sensor failures, enhancing system reliability.

Keywords:
Extra-TreesWSNclassificationcontext-aware systemdata-drivenfault diagnosismachine learningsensor faults

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Area of Science:

  • Cyber-Physical Systems
  • Wireless Sensor Networks
  • Machine Learning

Background:

  • Sensors are critical in Cyber-Physical Systems but prone to failures from environmental factors, production quality, and aging.
  • Sensor malfunctions, including communication loss or incorrect data, compromise system safety, economy, and reliability.
  • Wireless Sensor Networks (WSNs) have limited resources, posing challenges for implementing fault detection systems.

Purpose of the Study:

  • To develop a lightweight, machine learning-based fault detection and diagnostic system for WSNs.
  • To address the constraints of limited energy, memory, and computation in WSNs.
  • To propose a Context-Aware Fault Diagnostic (CAFD) scheme.

Main Methods:

  • Proposed a Context-Aware Fault Diagnostic (CAFD) scheme utilizing the Extra-Trees ensemble learning algorithm.
  • Replicated a realistic WSN scenario with humidity and temperature sensors experiencing low-intensity faults.
  • Considered six common sensor fault types: drift, hard-over/bias, spike, erratic/precision degradation, stuck, and data-loss.

Main Results:

  • The CAFD scheme demonstrated accurate and timely detection and diagnosis of low-intensity sensor faults.
  • The Extra-Trees algorithm showed superior performance in diagnostic accuracy, F1-score, and ROC-AUC compared to Support Vector Machines and Neural Networks.
  • Efficient training time was observed for the Extra-Trees algorithm.

Conclusions:

  • The proposed CAFD scheme is effective for detecting and diagnosing sensor faults in resource-constrained WSNs.
  • The Extra-Trees algorithm offers an efficient and accurate solution for sensor fault diagnostics in WSNs.
  • This approach enhances the overall safety, economy, and reliability of WSNs.