## 5 Experimental Tests of SOM Classifiers of IM Winding Faults
### Training Pattern Selection for Self-Organizing Kohonen Network
The Euclidean distance between neighbouring neurons as well as between the presented input vector and neurons is dependent on the course of the learning process. The effective selection of weights depends on the learning coefficient and the number of training epochs used in the learning process. As shown in Figure 10, an inadequate choice of the learning factor prevents us from separating the map into zones characteristic of all analysed damage. As observed in Figure 10, bands indicating a large distance between neighbouring neurons are noticeable. As noted in Figure 10a, [PERSON]'s map was divided into five areas constituting five separate fault categories. In the case of an incorrect value of learning factor (Figure 10b), for the same number of training epochs, only one area was identified on the map. Figure 8: Procedure of stator and rotor windings diagnosis using ISCA and Kohonen network (SOM): (**a**) flow chart of the signal processing; (**b**) scheme of the SOM network with the analysed inputs (in our case SOM size is \(10\times 10\)). The number of training epochs also has a significant influence on data classification efficiency. As observed in Figure 11, an adequate number of epochs provides a proper adaptation of the connections between the neurons. Figure 11a shows the areas characteristic of the discussed induction motor damage. The effect of an insufficient number of training epochs is shown in Figure 11b. In this case, the distances between the neurons are too small to determine the zones characteristic of each category. \begin{table}
\begin{tabular}{c c} \hline \hline
**Training Vector Size** & 104 \\ \hline
**Test Vector Size** & 150 \\ \hline
**Number of Faults Categories** &
\begin{tabular}{c} Shorted turns: 5 \\ Broken bars: 3 \\ \end{tabular} \\ \hline
**Neural Network Inputs** & \(I_{2}(^{6})\), \(I_{2}(^{5\text{ }s\text{ }-2\text{th}})\), \(I_{2}(^{5\text{ }s\text{ }-\text{th}})\) \\ \hline
**Training Method** & WTM (Winner Takes Most) \\ \hline
**Neural Network Structure** & \(20\times 20\) & \(10\times 10\) \\ \hline
**Number of Training Epochs** & 700 & 600 \\ \hline
**Neural Network Topology** & hexagonal \\ \hline
**Neighbourhood Function** & Gaussian \\ \hline
**Neighbourhood Radius** & 2 \\ \hline
**Learning Rate** & 0.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters of the self-organising neural network. Figure 10: Influence of the learning rate to the SOM neighbourhood distances: (**a**) \(\eta=0.8\); (**b**) \(\eta=0.3\). Figure 9: Illustration of the behaviour of the Kohonen Network (KN) under training. Based on the analysis of the network response to the learning and test data vectors, the number of learning epochs was selected. In the case of a 20 \(\times\) 20 network, the number of training epochs was 700, while for networks with a size of 10 \(\times\) 10, 600 epochs. The results obtained for the bigger network were comparable with a 10 \(\times\) 10 map, thus the results obtained with this less complicated map were shown in the next section. The response of the designed network to the given training vector allowed to highlight the Kohonen map zones characteristic of specific IM damage categories.