## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Peak Number**} & \multicolumn{2}{c}{**Actual Value Range**} & \multicolumn{2}{c}{**Chosen Value Range**} & \multicolumn{2}{c}{**Number of Values in the**} \\ \cline{2-6}  & **Min** & **Max** & **Min** & **Max** & **Chosen Range (\%)** \\ \hline P1 & 3 & 9 & 4 & 6 & 84.58 \\ P2 & 10 & 32 & 27 & 32 & 97.51 \\ P3 & 30 & 60 & 52 & 60 & 94.53 \\ P4 & 56 & 90 & 77 & 87 & 88.56 \\ P5 & 71 & 154 & 90 & 113 & 86.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Distribution the peak value by frequency range. Figure 5: Confusion matrix for ANN-classifier. cases that exist are divided into four classes, instead of nine. Under each class, we should refer to the pile numbers, or more precisely, the type of pile defect. Figure 6 shows the result of training another ANN classifier (Confusion matrix) on pre-processed Quadratic SVM data. The present ANN classifier was trained on the same data as the classifier with the results shown in Figure 5. The classes were defined by pile defects. The first class is pile defect \(\mathcal{N}\)1; the second class is pile defect \(\mathcal{N}\)2; the third class is a defect-free pile; and the fourth class is pile defect \(\mathcal{N}\)3. The results demonstrate 100% identification of a defective pile, and rather high overall accuracy (accuracy = 91.9%). At the same time, the highest percentage of errors was found between the classification of piles with defect numbers 1 and 2. Table 2 provides full details of the ANN classifiers trained during this study. Table 2 shows the results for the four experiments. In experiments \(\mathcal{N}\)1 and \(\mathcal{N}\)2, each sensor taking measurements was used as classes. The first five peak points of the spectrum were used as input data in experiment \(\mathcal{N}\)1, and the average values of the spectrum amplitudes over the frequency range for each of the five intervals shown in Table 1 were used in experiment \(\mathcal{N}\)2. Experiments \(\mathcal{N}\)3 and \(\mathcal{N}\)4 used pile defect numbers as classes (defect type 1, 2, 3 and defect-free type). The input data were used similarly to experiments \(\mathcal{N}\)1 and \(\mathcal{N}\)2. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Experiment Number**} & \multirow{2}{*}{**Brief Description**} & \multicolumn{5}{c}{**Accuracy for Different Types of Learning Algorithm, \%**} \\ \cline{3-6}  & & **Fine Tree** & **Liner SVM** & **Quadratic SVM** & **Fine KNN** & **Medium KNN** \\ \hline
1 & 9 classes 5 peaks & 72.5 & **85.9** & 81.9 & 82.6 & 61.7 \\
2 & 9 classes, range & 67.1 & 87.2 & **87.9** & 85.2 & 73.2 \\
3 & 4 classes 5 peaks & 73.2 & 71.1 & **91.9** & 87.9 & 71.1 \\
4 & 4 classes, range & 75.8 & 83.2 & **93.3** & **93.3** & 84.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Training results of ANS classifiers. Figure 6: Confusion matrix for ANN-classifier. As is clear from Table 2, the best result was shown by the classifier type defining four defect types, where the input data were the averaged spectrum values in the pre-determined ranges. It is worth emphasizing, however, that the results in all cases were quite high (accuracy \(>\) 80%). Consequently, the ANN classifier was demonstrated to be valid. It should be noted that, in all cases, the defective pile was identified 100% accurately. The result shows the validity of two approaches: the use of an ANN classifier to determine the pile defect at once, and to determine the defect with reference to the operating conditions of a particular sensor. However, this solution is intermediate and requires interpretation of sensor and pile defect conditions. Table 3 provides training data for the ANN classifier on data with different numbers of peaks. By analyzing the results of the experiments detailed in Table 3, we can conclude that reducing the number of peaks to use them as input data is not feasible, as the accuracy of the ANN classifier is reduced.