Resumen
More than 55% of porcelain insulators installed throughout Korea have exceeded their service life. Hence, utilities are extremely interested in determining the robustness of insulators in their systems. In this study, the identification of the peak ranges in the main natural modes by frequency response analysis, the principal component analysis (PCA) method by feature extraction in the time and frequency domains for the damage detection of porcelain insulators are investigated; among these, the PCA method, which utilizes frequency response data, is proposed for defect classification. The 67 porcelain insulators are secured as specimens from 154 kV transmission towers installed in various parts of Korea; their main materials are cristobalite and alumina. In these specimens, it is observed that the three types of damage, such as porcelain damage, cap damage, and internal damage, are those that are typically found in actual sites. Accordingly, the use of two eigenvectors (moments of real value and moments of imaginary value) considerably aids in the analysis of principal components. With the frequency response data, the material and damage types are found to be distinguishable. The classification accuracy is increased by including the third largest eigenvector (area of real value) in three-dimensional analysis. By employing frequency response data, the PCA method provides useful information for assessing the integrity of porcelain insulators; it may be used as basis for future machine learning applications.