## 1 Introduction  
A Squirrel Cage Induction Motor (SC-IM) is a vital electrical machine in industrial plants. Although the SC-IM is one of the normally used machines, for various technical, extraordinarily reliable, and economic reasons, the SC-IM faces various stresses throughout operation states which cause many varieties of faults. It is therefore helpful to research automatic machine fault diagnosis techniques. The SC-IM faults are varied. The common faults are stator winding faults [1], bearing faults [2], and broken rotor bar faults [3]. The Inter Turn Short Circuit (ITSC) occurred due to insulation faults. The winding can be damaged when a high current flows in the short-circuited coils. The Broken Rotor Bar (BRB) fault is catastrophic. It can harm other SC-IM components. For this reason, the detection of ITSC and BRB faults improves the reliability of the system. Recently, several researchers have focused on BRB and ITSC faults. The stator current is analyzed to detect BRB faults [4]. The analysis shows that the presence of unbalanced current in a three-phase induction motor affects the amplitude of the characteristic frequencies of broken bars fault on the stator current spectrum. The analysis of permanent magnet synchronous machines' stator winding impedance variation in ITSC and eccentricity faults are studied in [5]. The impedance difference of healthy and faulty motors is used to detect and classify these faults, and the results are verified through finite element analysis simulations and experimental analysis. The Hilbert transformation is used to locate and detect BRB faults [6]. The proposed method is based on monitoring specific statistical parameters derived from a start-up stator current envelope study. The validation of the proposed method is implemented for direct line start and inverter fed operations. The induction motor model was investigated in the case of no-load and full load at various fault severity levels. Mathematical modelling of ITSC of permanent magnet synchronous motor windings using a multiple-coupled circuit has been developed in [7]. In this study, power spectral density was used to detect stator winding short circuit faults and estimate the fault severity. The stator current frequency spectrum was analyzed to diagnose the fault and location of ITSC. It was identified that the amplitude of the third harmonic of the current was the most distinguishing characteristic for detecting the ITSC fault ratio of a permanent magnet synchronous motor. Many researchers use artificial intelligence techniques to increase the accuracy of diagnosing ITSC or BRB faults: the Artificial Neural Network (ANN) is used to diagnose ITSC faults [1], and BRB faults are detected and classified using the Adaptive Neural Fuzzy Inference System (ANFIS) [2]. The common techniques are ANN, fuzzy logic, and ANFIS. In [8], the authors proposed the radial basis function multilayer perceptron cascade neural network-based stator winding inter-turn short and rotor eccentricity faults detection for three-phase induction motors. Simple statistical characteristics of stator current are extracted to be input features. The principal component analysis is used to select appropriate input features for the classifier. In [9], a feed-forward neural network technique for diagnosing stator inter-turn faults is developed. The current signals are obtained using the finite element model for interior mount line-start permanent magnet synchronous motors. An experimental test rig was implemented to validate the finite element model. The input to the neural network is a group of statistical and frequency-based features extracted from the steady-state three-phase stator current signals. In [10], the efficacy of fuzzy logic is presented for the detection and diagnosis of faults for induction motor drive systems. The root-mean-square value and total harmonic distortion of the stator currents can accurately diagnose different drive fault conditions. The developed algorithm is verified using simulation in MATLAB Simulink.