## 4 Results  
This section presents the results of the proposed method. At first, the mean, minimum, and maximum accuracies of each SCT are presented by changing the number of window samples to check the method behavior. After, the general accuracy of the proposed method is compared changing the classification method by a Neural Network (NN) and a decision tree (DT). This additional step is carried out to check the QSA discrimination obtained and its adaptation to other classification methods. Finally, the proposed method is compared with the literature. Initially, the results of the accuracy analysis are presented. The classification accuracy results are presented in confusion matrices to obtain the system behavior. An example of a confusion matrix is shown in Table 2. The real SCT condition is presented in each column of the confusion matrix, and the classified/predicted SCT condition is presented in each row. The normalized classification accuracies are shown from 0 to 1.0, 0 being equal to \begin{table}
\begin{tabular}{c c} \hline
**Equipment** & **Specifications** \\ \hline Transformer & 1.5 kVA, 135 turns, voltage input 120 V \\ Autotransformer & Powerstat, type 116 CU-3, Input 240 V, Output 0–280 V \\ Current clamp & Fluke i200s, 10 mV/A \\ Triaxial accelerometer & KISTLER, model 8395\({}^{\circ}\), resolution = 400 mV/g, measurement range \(\pm\)10 g \\ Data acquisition system & NI-USB 6211, ADC of 16 bits with an input range of \(\pm\)10 V, sampling frequency used = 6000 samples/s \\ \hline \end{tabular}
\end{table} 
Table 1: Specifications of the equipment used. Figure 7: Graphical interface of the proposed methodology. 0% and 1.0 equal to 100%. The values of the diagonal cells correspond to the normalized percentage of the tests for each SCT condition that was correctly classified. In this regard, the accuracy values of 1 for the conditions of 0, 15, and 20 SCTs indicate that 100% of tests were correctly classified. The results of Table 2 belong to the first KNN training for a data window size of 100. To have a general statistical performance with different data window sizes. The classification process is repeated forty times using different file combinations and various data window sizes, i.e., 100, 500, 1000, 5000, and 8000 samples to test. From the obtained accuracy values, their mean, minimum, and maximum values are computed and presented in Table 3. For example, the cell for 0 SCTs and 100 samples as window size have the values 0.9889 (0.94, 1.0), indicating that the mean for the forty accuracy values is 0.9889, the minimum obtained value is 0.94, and the maximum obtained value is 1.0. The best classification results are obtained using 5000 and 8000 samples, where three classes present 100% accuracy and three classes are near 99%. The 8000 samples are obtained in 1.33 s, which represents 58% of the total acquisition time, and 5000 samples are obtained in 0.83 s, which represents 36% of the total acquisition time. Additionally, the windows with smaller sample number, such as 500 and 1000 present similar accuracies in these classes, except in 5 SCTs which is understandable since it is a low severity damage. They represent 3.62% and 7.25% of the total acquired samples, which means that high accuracies are obtained using a very low number of samples. The accuracy in using a window of 100 samples remains near 99%, being a good option in classification despite the reduction in the accuracy of some classes. Therefore, it is concluded that a reliable diagnosis can be obtained with a small number of samples, which is desirable for applications that require fast responses, as in the case of protections; in the same regard, a slight improvement in the accuracy values can be obtained, but an increase in the number of samples can be taken into account. The best option depends on the application.