## 5 Experimental Tests of SOM Classifiers of IM Winding Faults  
### Training Pattern Selection for Self-Organizing Kohonen Network  
The learning process of [PERSON]'s self-organizing network was carried out based on the data from 104 measurements acquired on a real drive system. The training vector of the neural network contained amplitudes of the negative-sequence instantaneous symmetrical component \(i_{2}\) of the stator current with frequencies \(f_{s}\), \(5f_{s}-2 sf_{s}\) and \(5f_{s}-4 sf_{s}\). The diagram of the diagnostic procedure using ISCA and the Kohonen Network (KN) is shown in Figure 8.  
The KN training vector included the above mentioned magnitudes of the instantaneous negative-sequence symmetrical component of the motor current calculated for different values of the supply voltage frequency in the range of (20 \(\div\) 50) Hz. The network learning process for the classification of winding faults contained information on the amplitudes of the spectrum for no load (\(T_{L}\) = 0), half of the nominal value (\(T_{L}\) = 0.5\(T_{LN}\)) and the fully loaded motor (\(T_{L}\) = \(T_{LN}\)). In the case of the classification of the rotor bar damage, the training data included measurements for load torque changes \(T_{L}\) = 0.5\(T_{LN}\) and \(T_{L}\) = \(T_{LN}\). The ability to distinguish clusters is strongly dependent on topology, the number of neurons and the proximity of the Kohonen network. In practical applications, the selection of these parameters is to ensure the correct operation of the network, as well as the legibility of the solution found. In the case of a large number of patterns, and many factors affecting the values of the elements of the network input vector, it is required to use a large number of neurons in the output layer (network diagram, presented in Figure 8b). The number of neurons should allow for a clear identification of areas characteristic of particular categories. The network topology determines the type of connections between neurons. Analysing the impact of changes in the load torque, frequency of supply voltage and the number of analysed failure categories, in the conducted research it was decided to use a 20 \(\times\) 20 and 10 \(\times\) 10 network with a hexagonal topology. The network learning process was carried out in accordance with the WTM method, with the Gaussian neighbourhood function. After the training process, a test data vector was given to the  
Figure 7: Changes of the amplitude value of negative-sequence instantaneous symmetrical component \(i_{2}\) of the stator current with frequencies: \(f_{\rm s}\) (**a**), \(5f_{s}-2 sf_{s}\) (**b**), and \(5f_{s}-4 sf_{s}\) (**c**), for different number of shorted turns \(N_{sh}\) (**a**), different number of broken bars \(N_{bb}\) (**b**,**c**), and different load torque \(T_{L}\) and supplied frequency \(f_{\rm s}\). network input in order to evaluate the effectiveness of the classification. The test data vector contained data from 150 measurements for different values of load torque \(T_{L}\), the frequency of supply voltage \(f_{s}\), as well as for different numbers of faulted rotor bars \(N_{bb}\) and stator short-circuits turns \(N_{sh}\) of the induction motor. The summary of the self-organising network parameters used in this research is given in the Table 2.  
After the training process, the learning vector was given to the input of neural network. The neural network response contained information about the number of the winning neurons for each sample presented. Neurons closest to a given sample form an area characteristic of this sample. As observed in Figure 9, the reduced value of the Euclidean distance is noticeable for the winning neuron as well as neighbouring neurons (according to the WTM method, [45]). The presentation of a large amount of training data belonging to the same fault category allows us to create zones of active neurons for a given category.