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Related Concept Videos

Fault Types01:18

Fault Types

117
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...
117
Three-Phase Short Circuit—Unloaded Synchronous Machine01:21

Three-Phase Short Circuit—Unloaded Synchronous Machine

199
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.
This behavior occurs due to the magnetic flux produced by the short-circuit armature currents. Initially, these currents follow high-reluctance paths but eventually shift to...
199
Power System Three-Phase Short Circuits01:21

Power System Three-Phase Short Circuits

135
Determining the subtransient fault current in a power system involves representing transformers by their leakage reactances, transmission lines by their equivalent series reactances, and synchronous machines as constant voltage sources behind their subtransient reactances. In this analysis, certain elements are excluded, such as winding resistances, series resistances, shunt admittances, delta-Y phase shifts, armature resistance, saturation, saliency, non-rotating impedance loads, and small...
135

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Semi-Supervised Framework with Autoencoder-Based Neural Networks for Fault Prognosis.

Tiago Gaspar da Rosa1, Arthur Henrique de Andrade Melani1, Fabio Henrique Pereira2

  • 1Department of Mechatronics and Mechanical Systems Engineering, Polytechnic School, University of São Paulo, São Paulo 05508-010, SP, Brazil.

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Summary

This study introduces a novel deep learning framework for fault prognosis, enhancing system safety and reliability. The autoencoder-based method effectively identifies and tracks faults even with imbalanced industrial data.

Keywords:
autoencoderdeep neural networksfault prognosisremaining useful life (RUL)

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Deep Learning

Background:

  • Industrial systems face challenges with safety and reliability due to data imbalance between normal and faulty states.
  • Traditional supervised methods require extensive manual data labeling, which is often impractical.
  • Detecting and predicting faults in complex systems remains a critical research area.

Purpose of the Study:

  • To present a generic framework for fault prognosis utilizing autoencoder-based deep learning.
  • To address the issue of unbalanced data in industrial fault detection.
  • To improve system safety and reliability through advanced prognosis.

Main Methods:

  • A semi-supervised extrapolation of autoencoder reconstruction errors is employed.
  • The framework focuses on detecting measurement divergences and tracking their growth.
  • Individual variable evaluation is performed for fault detection and prognosis.

Main Results:

  • The proposed approach effectively handles unbalanced datasets, outperforming traditional methods.
  • It requires less manual data labeling and can uncover hidden data patterns.
  • Demonstrated effectiveness using Commercial Modular Aero Propulsion System Simulation (CMAPSS) data.

Conclusions:

  • The developed framework offers a robust and efficient solution for fault prognosis in industrial settings.
  • It enhances system safety and reliability by providing early fault detection and prediction capabilities.
  • The method's ability to work with limited labeled data makes it highly applicable.