## 6 Discussion  
The paper proposed the use of ANN for the analysis of acoustic signals accompanying electrical treeing in epoxy resin. The recorded signals were divided into blocks used to identify the values of the teaching data. Then, the features of the reference signal were analyzed using a moving window of a set length. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Network** & **Block Length** & **Neurons** & **Efficiency (\%)** & **Network** & **Block Length** & **Efficiency (\%)** \\ \hline FFN & 10 & 10 & 97.2 & SVM & 10 & 90.4 \\  & 60 & 14 & 97.6 & & 60 & 87.8 \\  & 100 & 16 & 97.3 & & 100 & 88.5 \\  & 200 & 15 & 97.5 & & 200 & 95.6 \\  & 300 & 11 & 96.7 & & 300 & 96.3 \\ \hline WNN & 10 & 3 & 90.9 & RBF & 10 & 95.8 \\  & 60 & 3 & 88.2 & & 60 & 95.3 \\  & 100 & 3 & 86.2 & & 100 & 94.6 \\  & 200 & 2 & 94.7 & & 200 & 94.8 \\  & 300 & 3 & 95.7 & & 300 & 95.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classification efficiency test results for variable data block lengths. WNN, wavelet neural network. \begin{table}
\begin{tabular}{c c c} \hline \hline
**PC Number** & **Variance (-)** & **Variance Share (\%)** \\ \hline
1 st & 3.6092 & 98.17 \\
2 nd & 0.0606 & 1.65 \\
3 rd & 0.0066 & 0.18 \\
4 th & \(6.3\times 10^{-5}\) & 0 \\
5 th & \(8.7\times 10^{-11}\) & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification efficiency test results for variable data block lengths. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**PC Number**} & \multicolumn{3}{c}{**Efficiency (\%)**} \\ \cline{2-5}  & **FFN** & **WNN** & **RBF** & **SVM** \\ \hline
1 & 96.6 & 95.5 & 95.7 & 96.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification efficiency test results for variable data block lengths. The features were divided into two groups whose properties depended on window length and position versus the beginning of the AE pulse. Hence, kurtosis and skewness, due to the high dynamics of changes, can be good indicators for the identification of the beginnings of AE pulses related to treeing. The values of the parameters, however, did not reflect the duration of AE signals, and their use required the application of an analysis window with a minimum length. In the analyzed case, the results were regarded as satisfactory and repeatable for window lengths over 200 samples. The other group of analyzed parameters, which describes the signal by its power characteristics, i.e., band power and signal energy, demonstrated elevated values during the signal. The steepness of the signal energy increase did not depend on the analysis of the window length, but a relevant selection of the length helped to identify the place in the signal where the signal amplitude dropped significantly. This is seen in the diagram as an inflexion point. Since AEs in electrical treeing are related to the breaking of polymer chains or the presence of partial discharge in the existing channels, the value and dynamics of signal energy changes during a single pulse can be linked to treeing intensity. By analyzing the results for each neural network, one can see that FFN demonstrated the highest efficiency. Slightly poorer results were obtained for RBF, WNN, and SVM. Moreover, for FFN and RBF, the results for each window length applied to the signal were comparable. WNN and SVN rendered better results for the greater analysis of window lengths. Moreover, WNN in its realization used only two wavelet neurons, while the structure of FNN required at least 15 neurons. In the case of networks taught by means of the first primary component, one can state that the efficiency of each network dropped by a value that did not exceed 0.5%. Summing up, the high efficiency in detecting AE signals accompanying electrical treeing needs to be highlighted. In order, however, to obtain as much information as possible on the AE pulse course, it seems justified to use an algorithm based on the following three stages:  
* signal envelope analysis,
* AE signals' detection,
* analysis of individual signals.