## 1 Introduction
Therefore, more advanced signal processing methods are used, including time-frequency analysis, such as Short-Time Fourier Transform (STFT), Wavelet Transform (WT) or Higher Order Transform (HOT) [30; 31; 32]. Symptoms of damage to cage rotors can also be detected in the axial flux, measured by means of additional measuring coils installed in the axis of the motor shaft, outside or inside the machine [31]. The analysis of the symmetrical components of the stator current can also be used to detect the asymmetry of the rotor windings. Similarly to the MCSA analysis, characteristic frequencies associated with rotor damage appear in the spectra of the instantaneous components of the positive- and negative-sequence of the stator current [26]. Thus the diagnostics of IM winding faults constitutes a complex problem that requires the measurement data acquisition of chosen state variables of the drive system, signal pre-processing to extract the fault symptoms, which can help the diagnostic system to detect the fault and evaluate its level. The fault symptom classification is much more difficult when diagnostic signal changes are not unequivocal and the number of datasets is large. The classification of such data is very difficult but the application of artificial intelligence methods can help to solve this problem and enables the objectification and automation of this process [1; 2; 33; 34; 35; 36]. From early 2000, feedforward multilayer neural networks trained with the Levenberg-Marquardt algorithm have been applied for the detection of different IM faults: electrical and mechanical types (see [25; 26; 37; 38; 39; 33; 36] among many others available in technical literature). The Kohonen maps were also used for IM fault detection [33; 37; 38; 39], but literature is not as abundant as in the case of feedforward networks. For example, in [37] the Kohonen Network (KN) with a Winner Takes All (WTA) method was applied to the detection of broken bar faults of IM using classical FFT of stator phase current and slip frequencies as network inputs. The authors report an 98% effectiveness; however, they do not present detailed results. Similar research is presented in [38] for the IM with one or two broken rotor bars. Relatively good results were obtained in the classification of healthy and faulty motor cases, while the separation of faulted classes were much worse. In [39] a Self-Organizing Map (SOM) network trained with the supervised Learning Vector Quantization (LVQ) algorithm was successfully used for mechanical (damage bearings) and electrical (stator winding short-circuit and broken rotor bars) faults classification. The feature extraction was taken from the sound data and wavelet analysis. Only motors with single fault types were tested. In [33] KN was used for multiple faults classification in IM, namely for bearings failures and supply asymmetry recognition, based on the stator current and vibration spectra. It was found there that KN is able to classify different types of faults (electrical and mechanical). However, according to the best knowledge of the authors, the Kohonen self-organizing networks were not applied to the detection of incipient stator faults as well as for the multiple electrical faults classification of the induction motor windings, namely stator short-circuit turns and rotor bar faults simultaneously. Therefore, the authors are convinced that their work could be considered a novelty, which proves that simple SOMs can be effective in the early detection and classification of incipient electrical faults in IM drives, regardless of whether they occur independently or simultaneously. Moreover, in our work we have used an analysis of instantaneous symmetrical components, which is a rarely used method in IM fault diagnostics. In particular, this method has not yet been used in the diagnosis of rotor failures, neither was it applied to in the detection of stator and rotor windings simultaneously.