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In order to detect the defects of ceramic insulators, frequency response analysis (FRA) is used to identify the peak range of the main natural modes of ceramic insulators, and principal component analysis (PCA) based on time-frequency feature extraction is used to detect the defects of ceramic insulators. By selecting 67 samples of ceramic insulators made of cristobalite and alumina installed on 500 kV transmission towers, three typical types of ceramic insulator defects, namely porcelain body, porcelain cap and interior, were studied. PCA was used to analyze the extracted features of time data and frequency response data, and realized the distinction of integrity, porcelain body defects, porcelain cap defects, internal defects and materials. The experimental results show that the internal defects are manifested by the disappearance of natural modes or the generation of new modes. The vector with the largest contribution of PCA based on time data to the data variance reaches 99.74%, which can distinguish the defects and materials of porcelain body; PCA based on frequency response data has the largest contribution to the data variance vector of 96.70%, which can realize the internal defect detection of ceramic insulators.