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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
Neural Circuits01:25

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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
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Integrated Damage Location Diagnosis of Frame Structure Based on Convolutional Neural Network with Inception Module.

Sensors (Basel, Switzerland)·2023
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Frame Structure Fault Diagnosis Based on a High-Precision Convolution Neural Network.

Yingfang Xue1, Chaozhi Cai1, Yaolei Chi1

  • 1School of Mechanical and Equipment Engineering, Hebei University of Engineering, Handan 056038, China.

Sensors (Basel, Switzerland)
|December 11, 2022
PubMed
Summary

An improved Convolutional Neural Network with Training Interference (TICNN) model enhances structural fault diagnosis accuracy under noisy conditions. This robust model demonstrates superior anti-noise capabilities for critical infrastructure safety.

Keywords:
anti-noise capabilityconvolution neural networkfault diagnosisstructural health monitoringvibration signal

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Area of Science:

  • Mechanical Engineering
  • Civil Engineering
  • Artificial Intelligence

Background:

  • Accurate structural fault diagnosis is crucial for mechanical and civil engineering.
  • Ensuring structural integrity safeguards equipment, infrastructure, and human lives.
  • Existing methods face challenges in noisy environments.

Purpose of the Study:

  • To improve the accuracy of fault diagnosis in frame structures under noise conditions.
  • To develop a novel convolutional neural network model with enhanced noise resistance.
  • To validate the proposed model's superiority against existing methods.

Main Methods:

  • An existing Convolutional Neural Network with Training Interference (TICNN) model was improved.
  • Comparative experiments were conducted using TICNN, 1DCNN, and WDCNN.
  • Fault diagnosis experiments were performed on a four-story steel structure model.

Main Results:

  • The improved TICNN demonstrated superior anti-noise ability compared to TICNN, 1DCNN, and WDCNN.
  • High diagnostic accuracy was achieved by the improved TICNN even under strong noise conditions.
  • The model's effectiveness was validated on a practical structural model.

Conclusions:

  • The improved TICNN offers significant advantages for structural fault diagnosis in noisy environments.
  • This enhanced model contributes to safer operation of mechanical equipment and civil structures.
  • The research validates the practical applicability and robustness of the proposed deep learning approach.