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This study introduces a new method using Fourier Transform Infrared (FTIR) spectroscopy and ensemble learning to quantitatively assess silicone rubber aging in composite insulators. The developed framework offers a reliable tool for condition-based maintenance in power systems.

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Area of Science:

  • Electrical Engineering
  • Materials Science
  • Data Science

Background:

  • Composite insulators are crucial for high-voltage transmission lines.
  • Material aging in silicone rubber hinders condition-based maintenance due to a lack of quantitative assessment methods.

Purpose of the Study:

  • To develop a novel, quantitative framework for assessing silicone rubber degradation in composite insulators.
  • To address the limitations in current condition-based maintenance strategies for electrical insulation systems.

Main Methods:

  • Integration of Fourier Transform Infrared (FTIR) spectroscopy for quantitative spectral analysis.
  • Application of a measurement-oriented ensemble learning model (KNN, SVM, RF, GBDT) with dynamic weight allocation.
  • Utilized information-theoretic feature selection and data augmentation to enhance model performance and generalization.

Main Results:

  • Achieved 96.17% accuracy in silicone rubber aging assessment.
  • Demonstrated strong model robustness against noise and anomalies, confirmed by SHAP analysis for overfitting resistance.
  • Established a scalable and reliable quantitative aging assessment framework.

Conclusions:

  • The proposed FTIR spectroscopy and ensemble learning framework provides a reliable method for silicone rubber aging assessment.
  • This contributes to the development of intelligent, data-driven diagnostic tools for electrical insulation systems.
  • Enables improved condition-based maintenance strategies for power infrastructure.