## 7 Conclusions  
This paper proposes a new method of ANFIS based on feature extraction for diagnosing ITSC and BRB faults in SC-IM. The motor operates with one BRB, two BRB and 2% ITSC, 5% ITSC, 7% ITSC, and 10% ITSC faults under different loading conditions. The feature extraction is obtained from the _qd_ signal using DWT. The experimental test is performed on SC-IM with combined BRB and 2% ITSC faults at No load using ANFIS based on DWT. The proposed method has been compared to ANFIS and ANFIS based on an auto-regressive model. The evaluation analysis of the results proves that the proposed method archives higher accuracy, the testing RMSE is 5.5547 \(\times\) 10\({}^{-7}\). While the weak output is given by ANFIS without data pre-processing, the testing RMSE is 2.1271. Many experimental tests are implemented on SC-IM with combined different ITSC and BRB faults under different loading conditions. The experimental tests verify the proposed technique's efficiency for detection and classification of fully different states of combined ITSC and BRB faults. In future research the following work may be considered: Open-phase, phase-to-phase, and phase-to-ground faults, as well as bearing failures, are examples of SC-IM faults; implementation of time-domain features for pre-processing of input data and comparing them to wavelet transform and autoregressive model.  
[PERSON] and [PERSON] designed the problem under study; [PERSON] performed the simulations and obtained the results; [PERSON] analyzed the obtained results; [PERSON] wrote the paper, which was further reviewed by [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
The authors appreciate the Taif University Researchers Supporting Project TURSP-2020/97 for funding this research.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Loading Conditions** & **RMSE of Training** & **RMSE of Testing** \\ \hline
10\% & 6.7152 \(\times\) 10\({}^{-5}\) & 6.7421 \(\times\) 10\({}^{-5}\) \\
20\% & 5.3322 \(\times\) 10\({}^{-4}\) & 5.3791 \(\times\) 10\({}^{-4}\) \\
30\% & 5.7229 \(\times\) 10\({}^{-5}\) & 5.7915 \(\times\) 10\({}^{-5}\) \\
40\% & 5.1395 \(\times\) 10\({}^{-5}\) & 5.1772 \(\times\) 10\({}^{-5}\) \\
50\% & 5.7158 \(\times\) 10\({}^{-4}\) & 5.7929 \(\times\) 10\({}^{-4}\) \\
60\% & 1.2401 \(\times\) 10\({}^{-3}\) & 1.2283 \(\times\) 10\({}^{-3}\) \\
70\% & 7.1056 \(\times\) 10\({}^{-5}\) & 7.0383 \(\times\) 10\({}^{-5}\) \\
80\% & 3.9184 \(\times\) 10\({}^{-5}\) & 3.8812 \(\times\) 10\({}^{-5}\) \\
90\% & 1.2478 \(\times\) 10\({}^{-5}\) & 1.2409 \(\times\) 10\({}^{-5}\) \\
100\% & 1.0214 \(\times\) 10\({}^{-4}\) & 1.0117 \(\times\) 10\({}^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: The ANFIS evaluation for diagnosis combined two BRB and different ITSC faults under different loading conditions.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to their large size.  
This research was funded by the TAIF UNIVERSITY RESEARCHERS SUPORTING PROJECT, grant number \"TURSP 2020/97\". The authors appreciate the Taif University Researchers Supporting Project TURSP 2020/97 for funding this research.  
The authors declare no conflict of interest.