## 6 Results and Discussions
### Diagnosis of Combined BRB and 2% of ITSC Faults Using ANFIS Based on DWT  
The test is implemented on SC-IM with one BRB, two BRB, and 2% of ITSC faults at No load. The training data set is 500 samples, and the testing data set 500 is samples. The ANFIS program is designed in MATLAB software. The sub-clustering is developed. The ANFIS output of training is shown in Figure 7. The solid line is the target values: '0' for healthy condition, '1' for one BRB fault, '2' for two BRB faults, '3'for 2% of ITSC fault, '4' for combined two BRB, and 2% of ITSC faults. The circles are the index of faults, which is the ANFIS output.  
The testing data set, which is different to the holdout dataset from the training data set, is applied to ANFIS to evaluate the performance of ANFIS. Figure 8 presents the testing ANFIS output. The solid line represents the target values, and the circles represent the index of faults. The testing ANFIS output is \"0\" from 501 to 600 samples for healthy condition, \"1\" from 601 to 700 samples for one BRB fault, \"2\" from 701 to 800 samples for two BRB faults, \"3\" from 801 to 900 samples for 2% of ITSC fault and \"4\" from 901 to 1000 samples for combined two BRB and 2% of ITSC faults with good accuracy.  
The RMSE is calculated to evaluate the performance of ANFIS based on DWT. The training RMSE is 5.6073 \(\times\) 10\({}^{-7}\) and the testing RMSE is 5.5547 \(\times\) 10\({}^{-7}\). ANFIS achieved high performance and learned the test data for all the samples with reasonable accuracy.  
Figure 8: The testing ANFIS output for combined BRB and 2% of ITSC faults.  
Figure 7: The training ANFIS output for combined BRB and 2% of ITSC faults.  
As a result, ANFIS based on DWT features is a well-structured tool for diagnosing the combined BRB and 2% of ITSC faults.  
The FLC model is implemented on MATLAB Simulink for diagnosing combined BRB and 2% of ITSC faults of SC-IM. Figure 9 shows the FLC Model to diagnose combined BRB and 2% of ITSC faults. Five signals are the input of the multiport switch.  
These signals are the _lqd_h_ of a healthy motor, _lqd_ubr1_ of one BRB fault, _lqd_ubr2_ of two BRB faults, _lqd_ubr2_ of a 2% of ITSC fault, and _lqd_ubr2_sh2_ for a combined two BRB and 2% of ITSC faults condition.  
The fuzzy logic controller is connected directly to the output multiport switch. The designed ANFIS program is stored in FLC. FLC output gives the index of faults. The duration of the operating process of FLC is 0.1 s. In order for the multiport switch to select the input signals, the sample time of the port index is adjusted to 0.021 s. At the start, the multiport switch selects 25,000 samples for the healthy condition. After 0.021 s, 25,000 samples for one BRB fault are selected. After 0.021 s, 25,000 samples for two BRB faults are selected. After 0.021 s, 25,000 samples for 2% of the ITSC fault are selected. After 0.021 s, 25,000 samples for a combined two BRB and 2% of ITSC faults are selected as shown in Figure 10.  
Figure 10: The index of fault for combined BRB and 2% of ITSC faults.  
Figure 9: FLC model to diagnose combined BRB and 2% of ITSC faults.  
A Wavelet packet transform is used for the detection of bearing failures via stator current analysis [26]. In all stages, both low-pass and high-pass, bands are split in wavelet packet transform. The stator current is decomposed by the Meyer wavelet. Afterward, energy is calculated. Energy is used as a fault index. It is increased in the case of bearing faults compared with a health condition. In this paper, the DWT is used to extract features from _qd_ signals. The low-frequency component is used as input data in the ANFIS model to detect and classify combined ITSC and BRB faults in SC-IM. RMSE scores are used to evaluate the diagnostic model. That means both techniques are combined to get better results.