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Sequence Networks of Rotating Machines01:24

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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
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Improved Variational Mode Decomposition and CNN for Intelligent Rotating Machinery Fault Diagnosis.

Qiyang Xiao1, Sen Li1, Lin Zhou1

  • 1School of Artificial Intelligence, Henan University, Zhengzhou 450046, China.

Entropy (Basel, Switzerland)
|July 27, 2022
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Summary
This summary is machine-generated.

This study introduces an intelligent method for diagnosing rotating machinery faults using improved variational mode decomposition (IVMD) and convolutional neural networks (CNNs). The approach enhances fault diagnosis accuracy in complex environments.

Keywords:
continuous wavelet transform (CWT)deep learningimproved variational mode decompositionintelligent fault diagnosis

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Area of Science:

  • Mechanical Engineering
  • Signal Processing
  • Artificial Intelligence

Background:

  • Rotating machinery is crucial in industrial applications.
  • Early fault diagnosis is essential for preventing catastrophic failures and minimizing downtime.
  • Traditional methods struggle with non-stationary signals common in fault diagnosis.

Purpose of the Study:

  • To develop an intelligent fault diagnosis method for rotating machinery.
  • To address limitations in traditional variational mode decomposition (VMD) parameter setting.
  • To improve the accuracy and robustness of fault identification in complex operational conditions.

Main Methods:

  • Proposed an improved variational mode decomposition (IVMD) with automatic mode number optimization.
  • Reconstructed the signal using highly correlated decomposed components.
  • Applied Continuous Wavelet Transform (CWT) for extracting 2D time-frequency features.
  • Utilized a Convolutional Neural Network (CNN) for fault classification.

Main Results:

  • The IVMD method overcomes subjective parameter setting issues of traditional VMD.
  • The combined IVMD-CWT-CNN approach effectively extracts fault features from non-stationary signals.
  • Experimental results demonstrate high recognition rates for rotating machinery faults.

Conclusions:

  • The proposed intelligent diagnosis method is effective for rotating machinery fault identification.
  • The method shows adaptability in complex environments.
  • This approach offers a promising solution for reliable condition monitoring.