## 3 Results
### Network Training  
To obtain the best weights for ANN, several training were performed with different combinations of variables, which are:  
* Training/Test Ratio: Proportion of the available samples from the database to be used for training and testing, in this case, the samples that will be used for training will not be used for testing. * Intermediate Neurons: Number of intermediate neurons in the neural network. Figure 7: Extracted features for ANN training: (**A**) maximum value; (**B**) minimum value; (**C**) mean value; (**D**) variance; (**E**) root mean square. Base Multiplier: Number of times that samples from the database will be trained, the repetition of training can lead to better results, at the expense of a longer processing time. Table 2 shows the best network accuracy achieved. The proportions of the database that were used for tests are presented in the columns, varying between [0.5, 0.95], for each neural network (i.e., the network that detects perforation and the network that detects contamination). The number of neurons present in the intermediate layer is highlighted in the lines, followed by the base multiplier, these numbers being separated by a bar. First, we cannotice that changing the number of intermediate neurons does not consistently improve accuracy. On the other hand, it is clear that there is a tendency to increase accuracy as more samples are made available, either through the proportion of samples used for training, or by reusing the same samples with the base multiplier. The convergence process of the algorithm for the network training is shown in Figure 8. It can be seen that in Figure 8B for the contaminated insulator, there was an oscillation before the convergence. As can be seen in the cost function relative to the error (see Equation (7)), the error reduction occurs more quickly in the perforated insulator, this possibly occurs because there is a greater difference in this signal in relation to the others and thus a faster convergence occurs. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{\
\begin{tabular}{c} **Neurons/** \\ **Multiplier** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Training/** \\ **Test (\%)** \\ \end{tabular} } & \multicolumn{2}{c}{**Accuracy**} \\  & & **Perforated/** & **Containated/** \\  & & **Non-Perforated** & **Non-Containated** \\ \hline \multirow{4}{*}{6/1} & 50/50 & 80.85\% & 50.03\% \\  & 80/20 & 81.92\% & 51.92\% \\  & 90/10 & 80.83\% & 51.50\% \\  & 95/05 & 82.00\% & 50.00\% \\ \hline \multirow{4}{*}{8/1} & 50/50 & 80.87\% & 50.27\% \\  & 80/20 & 81.92\% & 52.00\% \\  & 90/10 & 80.67\% & 51.00\% \\  & 95/05 & 82.00\% & 49.67\% \\ \hline \multirow{4}{*}{12/1} & 50/50 & 80.93\% & 50.37\% \\  & 80/20 & 81.92\% & 51.75\% \\  & 90/10 & 80.83\% & 51.33\% \\  & 95/05 & 82.00\% & 49.67\% \\ \hline \multirow{4}{*}{16/1} & 50/50 & 79.67\% & 50.23\% \\  & 80/20 & 81.92\% & 53.25\% \\  & 90/10 & 80.83\% & 50.50\% \\  & 95/05 & 82.00\% & 53.33\% \\ \hline \multirow{4}{*}{16/2} & 50/50 & 81.20\% & 54.17\% \\  & 80/20 & 82.17\% & 68.25\% \\  & 90/10 & 80.83\% & 66.50\% \\  & 95/05 & 82.00\% & 64.00\% \\ \hline \multirow{4}{*}{16/4} & 50/50 & 81.26\% & 67.77\% \\  & 80/20 & 82.00\% & 63.58\% \\ \cline{1-1}  & 90/10 & 80.83\% & 66.33\% \\ \cline{1-1}  & 95/05 & 82.00\% & 64.66\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: ANN results. In order to seek even better results, accuracy was separated by type of case. The results presented in Table 3 use the weights obtained in training with ninety-five percent of the training database, sixteen intermediate neurons and a four-fold base multiplier. In this assessment, each case is compared to the classification of perforation and contamination separately to assess the accuracy of the algorithm in identifying this condition. The discrepancy of results for Case 4 (Perforated and Uncontaminated Insulator) with the others is evident by isolating the accuracy by type of case.