## 1 Introduction  
Induction motors are used in many industrial sites because of their simple structure, good durability, and low cost. However, when the induction motor breaks down owing to certain reasons, it causes extensive damage to the industry. Therefore, a system for managing the induction motor is needed. The main causes of induction motor failure can be classified into mechanical and electrical failures. Electrical failures include stator failure and mechanical failures include rotor and bearing failures [1]. In [2], the incidence of failures of induction motors was analyzed, and it was revealed that stator failure, rotor failure, and bearing failure account for 46%, 26%, and 11%, respectively. Among these types of failures, stator failure can lead to critical failures, which are the focus of diagnosis [3]. In particular, quick detection of inter-turn short circuit (ITSC) is the main research objective [4; 5; 6; 7]. The diagnosing methods are classified into a sensor-based method and a motor electrical signature analysis (MESA) method. The sensor-based method is divided into wired and wireless methods, according to the development of information and communications technology (ICT). Although the wired method involves direct coupling, recent developments in wireless communication technology have enabled diagnosis without direct coupling. Furthermore, many research studies have been conducted based on the aforementioned developments [8; 9; 10]. Typically, amplitudes of frequencies ratio 50 s frequency coefficient (MSAF-RATIO-50-SFC), MSAF-RATIO50-SFC-EXPANDED, MSAF-RATIO-24-MULTIEXPANDED-FILTER-8, and the shortened method of frequencies selection (SMoFS-15) are normally used, but the analysis is somewhat complicated [11; 12]. Since 1989, FFT has been used for the diagnosis of a stator using MESA [13]. Later, [PERSON]'s vector approach (PVA) was studied to diagnose in a rotating state [14; 15; 16]. PVA transformed three phases into two-phase orthogonal equations to diagnose failures in a rotating state in a circular pattern. However, due to the power factor problem of the induction motor, there is no difference between the degree of distortion of the normal state and the degree of distortion of the ITSC, which makes it difficult to detect the ITSC. To visualize the PVA, EPVA was studied to analyze the PVA in the form of an ellipse by applying the square root; however, it is difficult to detect the ITSC perfectly [17; 18; 19]. As the previous studies of MESA only considered the size, simplified methods such as the method using the change of phase angle [20], the one representing the distortion index of the circle by the distortion rate [21], and the half-period frequency analysis method were proposed [22]. However, there is difficulty in the direct diagnosis of ITSC. In addition, the three-phase source of voltage and current of the MESA is used for voltage total harmonic distortion (VTHD) of IEEE Std. 519, current unbalance factor (CUF) of IEC60034-1, voltage total harmonic distortion (VTHD), each voltage harmonic distortion (EVHD), current total harmonic distortion (CTHD), and each current harmonic distortion (ECHD) of IEEE Std. 519 [23; 24; 25; 26]. While such various characteristic diagnostic methods exist, complete diagnosis is difficult even if the diagnosis is performed using the MESA and the sensors. Particularly at light loads, it is difficult to diagnose using these methods. To solve this problem, AI technology can be used. It was proven that Neural Network (NN) classifier is a good approach in terms of classification speed, accuracy, and suitability for hardware implementation. In the application of this technology, the approach using the motor current signature analysis(MCSA), the sensor, and the complex method was used, and it depends on the sampling and fast fourier transform(FFT) methods, according to the current of each phase and the measured values of the sensors [27].