## 1 Introduction
The main drawback of fuzzy logic is that the membership functions and the amount of overlap between linguistic variables must be properly chosen while fuzzifying them [11]. ANN is not easily explainable, despite the superior performance that may be associated with it. It is difficult to compare it to the human brain's inference function [12]. ANFIS is a hybrid of ANN and fuzzy logic that can incorporate important features of both techniques [13]. The ANN's learning ability and the fuzzy system's logical reasoning ability have been combined in ANFIS [14]. ANFIS takes into account the positive features of ANN and fuzzy logic techniques for classifying and detecting different rotating machinery faults [15]. A Discrete Wavelet Transform (DWT) is used to analyze the raw currents in the time domain [16]. The DWT method is very significant for extracting features of the measured stator current signal where it was revealed in the performance of the fault diagnostic model [17]. The DWT is studied for the Park's vector modulus of current signals to detect ITSC fault [18]. The abnormal signals are used to detect different states of faults. Wavelets are employed to extract fault features [19]. The DWT is a much stronger and more efficient technique compared to Fourier transform techniques [19]. The stator current is analyzed based on DWT to detect and classify the stator faults in IM. The detection and classification are based on the Euclidean Distance of the Wavelet Energy Coefficient of the stator current [20]. The Auto-Regressive (AR) model relied on the auto-correlation approach to extract the signal's hidden features. The AR model is a widely used technique in applied research. The AR model's coefficients are computed using linear prediction coding [21]. The most related references are included in the literature review. In this research work, the stator currents are acquired from a real SC-IM. The _abc_ stator currents are converted to _qd_ stationary axes to enhance the prospects of IM faults diagnosis. ANFIS has a fundamental limitation, which is the fact that it is computationally expensive when the size of the input vector is large. When the number of inputs is large, the number of rules and their tenable parameters increase exponentially. Hence, the three-phase stator currents are converted to _lq_ and _ld_ frame using Clark's transformation to simplify the number of inputs. The DWT pre-processes the _qd_ signals to get the characteristics of the signal. The ANFIS model studies the extracted features in order to give high accuracy and overcome the drawbacks of the traditional techniques. The ANFIS model based on the extracted features from DWT is performed to detect and classify the combined ITSC and BRB faults. Therefore, the proposed classifier is trained and tested with a data set obtained from the extracted features. The experimental tests are implemented on a 1.5 Hp SC-IM with different states of combined ITSC and BRB faults under different loading conditions. The experimental results demonstrate that the proposed method can accurately analyze and recognize two SC-IM faults simultaneously. The performance of ANFIS based on DWT has been compared to ANFIS. Furthermore, ANFIS based on DWT is compared to ANFIS based on the AR model. ANN plays a main role in determining the correct input-output fault diagnosis relation. Furthermore, ANN has the advantage of self-learning from available data. Fuzzy logic presents heuristic reasoning about the fault diagnosis process. ANFIS is a hybrid of ANN and fuzzy logic; ANFIS takes the advantage of both techniques. The effectiveness of using ANFIS are successfully investigated using experimental tests. The experimental results give evidence of the robustness and scalability of ANFIS. The obtained results illustrate that ANFIS has the ability to diagnose two different SC-IM faults at the same time accurately. In future research work, the comparison between ANFIS, PCA and machine learning techniques will be done. It was observed that ANFIS based on DWT achieved better performance.