A Transfer Learning Approach to Sense the Degree of Surface Pollution for Metal Oxide Surge Arrester Employing Infrared Thermal Imaging

Deposition of environmental pollutants like dust, salt, alkali etc. on the housing of metal oxide surge arrester can induces premature failure of it in long run. Therefore, reliability of an electrical system can be affected. Hence, accurate sensing of surface pollution severity of Metal oxide surge arrester is very much important. Considering the issue, this paper proposes an innovative approach to sense the pollution severity on metal oxide surge arrester housing using infrared thermal imaging technique. For this purpose, infrared thermal images of metal oxide surge arrester at different pollution severity have been captured. After suitable preprocessing, the captured infrared thermal images are fed to pretrained convolutional neural network architecture ResNet50 for automatic feature extraction. The extracted deep features have been fed to 4 machine learning classifiers i.e., k-nearest neighbor, support vector machine, naive Bayes and random forest for classification purpose. According to the result, the best performance has been achieved with the random forest classifier. It is also observed that proposed framework is very much capable to sense surface pollution severity of Metal oxide surge arrester with higher degree of accuracy. Therefore, proposed framework can be practically implemented to monitor surface condition of Metal oxide surge arrester.

Automated summary

The paper proposes an innovative approach to sense the pollution severity on the housing of metal oxide surge arrester using infrared thermal imaging technique. By capturing infrared thermal images, extracting deep features and utilizing machine learning classifiers, the framework is capable to accurately sense the surface pollution severity of the surge arrester.