Hydrophobicity classification of polymeric insulators using a masked autoencoder model in vision transformer

Abstract

The loss of hydrophobicity on the polymeric insulator’s surface leads to a continuous water channel formation. This water channel formation can initiate dry band arcing and subsequent flashover. Therefore, accurately identifying the hydrophobicity class is crucial as it provides the surface ageing information of the polymeric insulators. This study proposed a novel approach utilizing a masked autoencoder-based vision transformer image classifier to classify the hydrophobic condition of polymeric insulators accurately. A comprehensive series of tests were conducted on deep learning image classifier models to assess robustness. The experimental findings demonstrated that the vision transformer model exhibited a significant recognition accuracy of 99.69%, surpassing the performance of the CNN, pre-trained CNN, and ViT models.