## 1 Introduction
The frequency response analysis (FRA) using the FRF is utilized in various fields: continued research is being conducted to assess the soundness of the target in various areas, including the diagnosis of transformers [14; 15; 16; 17] in the field of electricity, the setting of resonant frequency of car frames in the field of machinery [18], and the estimation of the location and extent of damage for monitoring structural conditions in the field of construction and civil engineering [19]. However, it is not sufficient to simply analyze the energy and natural frequencies of peaks of fast Fourier transform (FFT) and FRF to analyze objects that may cause errors in various usage environments and manufacturing processes [20]. Therefore, it is necessary to apply additional methods for improving the convenience and reliability. Figure 1: Sketch of porcelain insulator showing possible defects. To improve the reliability of the FRF analysis, quantitative values can be extracted by considering various features such as area and moment. In this case, because the dataset may grow depending on the number of extracted features, it is necessary to apply a method for reducing the amount of data, while maintaining the principal characteristics. Principal component analysis (PCA) and neighborhood component analysis (NCA) can be used to reduce dimensions while maintaining key features. They have been found to be effective in classifying and reducing dimensions by identifying trends in analyses that contain large amounts of data [21; 22; 23]. The PCA identifies the attribute combinations that account for the largest difference in data, and the NCA finds feature spaces so that the proximate nearest neighbor algorithm provides the best accuracy. The main difference between PCA and NCA is whether known classes are used to separate data. In this study, because the class of the data is known, we used PCA for a more accurate analysis. Previously, PCA was only used to identify and classify data trends. However, the application of machine learning methods, which is presently being actively researched, improves the accuracy of data classification and makes it easier to determine additional data through model development. Among the many machine learning methods, support vector machine (SVM) and ensemble methods are widely used for improving the classification accuracy and visualization of data. SVM classifies datasets linearly or nonlinearly by creating a maximum margin classifier between them. It is used for data classification in various fields besides the field of remote sensing and image analysis [24; 25]. The ensemble method is used in many fields such as cancer diagnosis, computer intelligence, statistics, and machine learning. It is a meta-algorithm that combines a single predictive model using several single machine learning algorithms and returns a learning model optimized for complex data classification. [26; 27]. Recently, it proved to be the best machine learning method for classifying data in kaggle, which provides a solution for managing big data. In this study, an artificial intelligence-based approach for SVM and ensemble models for the damage assessment of porcelain insulators with 154 kV transmission lines is proposed. Many varied porcelain insulator samples were used to increase the reliability of the analysis. Most previous studies have used fewer than 50 samples; the present study used 117 samples collected from transmission towers under various environmental conditions. Based on the FRF data with PCA, SVM and ensemble models were developed using MATLAB software.