## 5 Experimental Tests of SOM Classifiers of IM Winding Faults
### Results of Stator and Rotor Fault Classification
Similarly as in the case of responses to the training data, smooth transitions between the zones characteristic of the stator winding faults are highlighted on the map. Due to the increase in the load torque, the activity of neurons in the higher layers of the Kohonen map increases. The changes in the frequency of the supply voltage do not affect the effectiveness of the classification of the stator winding faults. The analysis of KN responses from Figures 12a and 13a allowed to find out that during the course of the learning process, two extreme failures were identified in the Kohonen network. The map areas for \(N_{sh}\) = 5 and \(N_{bb}\) = 2 were also identified from the characteristic zone for the healthy motor. This fact allowed to analyse the behaviour of the SOM in the event of simultaneous faults to the rotor and stator windings of the IM (Figure 13b). As noted in Figure 13b, mixed failures increase the activity of neurons in a zone that does not belong to any of the previously discussed ones. By analysing the map and components of a mixed failures shown in Figure 13b, it was noted that the network response depends on the contribution of a single fault in a mixed defect. Short circuits of fewer than three shorted turns (\(N_{sh}\) < 3) cause slight changes in the network input vector compared to the changes caused by the rotor damage. Due to the above, such mixed defects (\(N_{sh}\) < 3 and \(N_{bb}\) > 0) were classified as rotor failures by the network. Therefore, as a result of the increase in the number of short-circuit turns in the mixed faults, neurons located above the zone assigned to the separate stator failures were active. The analysis of the received Kohonen maps allowed to assess the category and extent of damage. The map zones indicating the specific defects of the machine are clearly identified from the zone characteristic of the undamaged motor windings. This fact is an unquestionable advantage of using Kohonen self-organizing networks during the initial assessment of the motor's condition. The risk of error will then be minimized. Analysing the obtained maps, a gradual distancing of zones characteristic of the discussed winding defects was observed due to the increase in the damage degree. The zones characteristic of the different number of ITSCs are slightly overlapping, which may be related to small changes in the stator currents due to the short circuits in case of the fully loaded motor. During the analysis of the SOM responses in the case of the damage of the rotor bars, stator inter-turn short-circuits as well as mixed damage, it was observed that for small values of the load torque, neurons located in the lower zones of the Kohonen map are stimulated. This fact gave the basis  
Figure 14: Specification of the test data parameters for the classification of the stator winding faults with 10 \(\times\) 10 SOM; \(T_{L}\)—load torque, \(T_{LN}\)—rated motor torque, \(f_{s}\)—frequency of the supply voltage. for testing the classification performance of the SOMs for unloaded drive (\(T_{L}\)/\(T_{LN}\) = 0). The results of these test are presented in Figure 14. At this stage of research, the classification tests were made only for the rotor failures. This is related to the strong relationship between motor slip and the magnitude values of the diagnostic signals for this type of machine defect. As noted in Figure 15a, due to the lack of load torque, a part of the map that does not belong to any of the damage categories discussed earlier is separated. The Kohonen network did not allow for the classification of damage in the case of an unloaded motor, however, it provided information about an untypical situation for the tested machine. It can be used in a diagnostic procedure as a kind of alarm signal indicating the motor state. Finally, the classification success rate was evaluated and summarized in Table 3. The analysis of the effectiveness of the self-organizing neural network was divided into two categories.