## 1 Introduction
A frequency response analysis (FRA) using an FRF has been widely used to identify mechanical damage for various targets such as civil infrastructure [13], automobiles [14], and electric facilities [15; 16; 17; 18]. However, because the insulator of the transmission line cannot be measured in close proximity, it is necessary to develop equipment with high field applicability for measuring the FRF of string-type porcelain insulators. In equipment development, convenience and weight of the equipment should be considered through the application of the latest technology. In addition, in most studies, energy and frequency of peaks are simply analyzed via fast fourier transform (FFT) and FRF graphs using frequency response data [18]. However, currently installed porcelain insulators were made mostly by hand, so there is a little difference in dimensions and mass, and it is difficult to judge damage only by analyzing the dynamic characteristics of the FRF data. Furthermore, boundary conditions in the field may vary depending on the installation environment. To minimize their effects, it is necessary to apply additional analysis methods to improve the reliability of the analysis. Additional analysis methods exist for extracting various features from the FRF to increase the reliability and diversity of the analysis, rather than the peak analysis of the FRF waveform. In this case, a number of quantitative values, such as area, moment, etc., that can be considered in the FRF waveform can be extracted. Moreover, because a large data set can be constructed according to the extracted features, it is necessary to apply a method for reducing the data size and maintaining the primary characteristics. Neighborhood component analysis (NCA), a major dimension reduction method, is applied to test multiple objects or to identify biases of numerous results from different locations. NCA has been found to be effective in reducing dimensions by identifying trends according to which large amounts of data are contained [19]. NCA is a method for finding feature spaces such that the probabilistic nearest neighbor algorithm provides the best accuracy. In this study, NCA was employed to find the space of features and visualize the division by reflecting the class of porcelain insulators that was known. In addition, to improve the reliability of the analysis, the machine learning (ML) method was applied to provide a diagnostic method that ensured consistent and accurate judgment. For example, the support vector machine (SVM), one of the most widely used supervisory algorithms for binary classification, employs kernel functions, such as linear, polynomial, and radial functions, to distinguish objects belonging to different classes [20; 21]. One of the ensemble classification methods, the bagging method, is another name for bootstrap aggregation. It is generally classified based on a decision tree, and it is a method for changing the number of boosts to improve classification accuracy [22; 23]. The k-nearest neighbor (kNN) method is a nonparametric method that was used in statistical applications in the early 1970s. It finds the k sample group closest to an unknown sample in the data set, and is a method for finding the optimal classification value by changing the response variable k, which plays an important role in classifier performance [24]. Discriminant analysis (DA) is a method for classifying data by making decision boundaries through learning about data distribution. The goal is to project the data onto a particular axis and to find a straight line that can distinguish both classes [25]. As such, various ML methods are widely used for cancer prediction in medical fields, determination of normal states and abnormalities in civil structures, and classification of cracks in mechanical fields. This classification method can be used to improve the reliability of the analysis and the ease of judgment.