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Convolutional Neural Network-Based Pattern Recognition of Partial Discharge in High-Speed Electric-Multiple-Unit

Chuanming Sun1,2, Guangning Wu2, Guixiang Pan1

  • 1CRRC Qingdao Sifang Co., Ltd., Qingdao 266000, China.

Sensors (Basel, Switzerland)
|April 27, 2024
PubMed
Summary

Convolutional Neural Networks (CNNs) accurately classify partial discharge signals in high-speed electric multiple unit (EMU) cable terminals. This advanced method surpasses traditional neural network models for defect detection.

Keywords:
cable terminalconvolutional neural networkhigh-speed electric multiple unitspartial dischargepattern recognition

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Area of Science:

  • Electrical Engineering
  • Materials Science
  • Artificial Intelligence

Background:

  • Partial discharge detection is vital for assessing insulation integrity in high-speed electric multiple unit (EMU) cable terminals.
  • Identifying specific defect types is crucial for preventing failures and ensuring operational safety.
  • Existing neural network (NN) models have limitations in accurately classifying diverse discharge signals.

Purpose of the Study:

  • To develop and evaluate a Convolutional Neural Network (CNN) model for classifying partial discharge signals from defective EMU cable terminals.
  • To compare the performance of the CNN model against traditional back-propagation NN and radial basis function NN models.
  • To establish a reliable method for identifying four typical defects in high-speed EMU cable terminals.

Main Methods:

  • Preparation of cable terminal samples with four distinct, typical defects from high-speed EMUs.
  • Development of a cable discharge testing system employing high-frequency current sensing to capture discharge signals.
  • Creation of comprehensive datasets for each defect type.
  • Implementation and comparison of CNN, back-propagation NN, and radial basis function NN for signal classification.

Main Results:

  • The CNN-based model demonstrated superior accuracy in classifying partial discharge signals corresponding to specific defects.
  • The CNN model significantly outperformed both the back-propagation NN and radial basis function NN models in defect identification.
  • Established datasets provide a valuable resource for further research in partial discharge analysis.

Conclusions:

  • Convolutional Neural Networks (CNNs) offer a highly effective solution for the accurate classification of partial discharge signals in high-speed EMU cable terminals.
  • The proposed CNN approach provides a significant advancement over existing neural network methods for defect diagnosis in critical rail infrastructure.
  • This study validates the potential of AI-driven techniques for enhancing the safety and reliability of high-speed rail systems.