## 6 Conclusions  
Summing up the research aimed at assessing the efficiency of the damage classification of electric circuits of an induction motor, it was found that the cooperation of SOM together with the ISCA of stator currents is an excellent diagnostic tool. The ISCA analysis makes it possible to identify symptoms of both rotor and stator damage in the spectrum of instantaneous negative-sequence component \(i_{2}\) of the stator current, contrary to the direct analysis of the stator current spectra. The information contained in the analysed spectrum enabled a short and effective process of learning in the Kohonen network. Due to the simplicity of implementation, and most importantly, the relatively small number of training samples necessary to ensure high reliability of SOM, this solution can be quickly introduced into automated diagnostic systems. The tests presented in the paper confirm the effectiveness of the ISCA analysis of stator currents along with the Kohonen self-organizing network in the fault detection process of the converter-fed induction motor.  
We would like to emphasize that our intention was to show the possibility of categorizing two different faults using one network (classification problem). We considered three cases:  
* separate faults of stator winding (incipient short-turns; 1-5 turns),
* separate faults of rotor winding (incipient rotor bar brakes; 1-2 bars),
* simultaneous occurrence of stator and rotor incipient faults.  
And it was shown in the paper that the proposed SOM network with properly pre-processed stator current signals (using the ISCA method) fulfilled our requirements. We proved that even one or two shorted turns or one broken bar (very incipient IM faults) can be recognized by our system as a faulted motor case, and the decision to disable it can be made by the service (motor operation is stopped automatically).  
Despite the low power of the tested motor and the relatively weak influence of the analysed incipient electrical faults to the magnitudes of the chosen harmonics of the instantaneous symmetrical components (negative-sequence magnitude), we proved that it is possible to classify and detect those winding failures at their initial stage and to design a ready-for-use diagnostic system using AI tools, which can help end users in proper monitoring and diagnostic procedures of industrial drives.  
In our opinion the AI based automatic diagnostic systems can help the end user not only in on-line diagnostics but also in preventing the faults (by trend prediction). However, to design such systems, deep expert knowledge is necessary.  
All the authors contributed equally to the concept of the paper. [PERSON] and [PERSON], proposed the methodology and paper organization; [PERSON] and [PERSON] carried out the measurements and analysed the experimental data (ISCA and SOM); [PERSON] prepared the original draft and supervised the project; [PERSON] validated the obtained results; [PERSON] revised and edited the final paper.  
This research was partly supported by the National Science Centre Poland under grant number 2017/27/B/ST7/00816 and by statutory funds of the Faculty of Electrical Engineering of the Wroclaw University of Science and Technology (2018-2019).  
The authors declare no conflict of interest.