# Linear Method for Diagnosis of Inter-Turn Short Circuits in 3-Phase Induction Motors
## 1 Introduction
The extended Park's vector approach (EPVA) was researched to improve the visualization of PVA so that the PVA could be analyzed in the form of an ellipse by applying the square root [33; 34; 35; 36; 37]. However, the research was conducted in a complex way by adding algorithms to algorithms (e.g., applying it to the fast Fourier transform, FFT). Existing studies have revealed that this is difficult because only the magnitude of the current is considered by reanalyzing the PVA to maintain its simplicity and readability. A method using change of the PVA phase angle was also researched [38; 39; 40], but it was difficult to directly solve the ITSC problem. On the one hand, from the view point of the harmonics, EPVA analysis of the second time harmonic in the frequency in order to detect the current supply unbalance is suggested, but data by MCSA is not easy to interpret because the harmonics of the hi-order generates as a lot of information is merged when the 3 phase is converted to 2 phase; there is the difficulty that additional work must be performed to identify harmonics [33; 34; 35; 36; 37; 41]. In addition, using the 3 phase source as the voltage total distortion (VTHD) of IEEE Std. 519, current unbalance factor (CUF) of IEC60034-1, voltage total harmonic distortion (VTHD), each voltage distortion harmonic (EV), current total harmonic distortion (CTHD), each current harmonic distortion (ECHD) characterized by a hybrid method in combination with vibration signature analysis, was studied. However, this method, which is the power system's point of view, is difficult for diagnosing ITSC and is highly dependent on vibration signature analysis [42; 43; 44; 45]. Because the diagnosis of turn-to-turn shorts is important, a fault diagnosis method using the magnitude of a reverse phase component (impedance) was also studied. This was done by analysis of complex circuit modeling, even though the circuit was really complicated [46; 47; 48; 49; 50; 51; 52; 53; 54]. However, the algorithm became too complex due to the complex circuit and needed to be simplified. Machine learning is a method later used for diagnosis with the recent development of artificial intelligence (AI) technology [18; 23; 30; 37; 55; 56; 57; 58; 59]. AI-based methods are divided into feature extraction, fault identification, and fault severity evaluation. Compared with the existing general diagnostic methods,an advantage of the artificial intelligence diagnostic method is that the diagnosis requires only minimal prior knowledge. There is no need to analyze the detailed model of the system or to model the fault. Most techniques use MCSA, vibration characteristics analysis, or a combination of both for fault detection. Many of these proposed techniques use FFT to analyze the spectrum in order to find the spectral characteristics of the fault. Once the feature extraction process to characterize the state of the motor is realized, the classification technology should be incorporated into the system. Neural network (NNs) classifiers have proved to be a good method in classification rate, accuracy and suitable hardware implementation. But the basic diagnosis method remains insufficient. Various recognition methods using AI technology such as k-nearest neighbors (k-NN), multi-layer perceptron (MLP), classification and regression tree (CART), and multi-class support vector machine (MCSVM) use the signal source of the voltage or current as it is, which makes it difficult to perform linear distinction of faults due to the noise of the power supply and environmental factors. Furthermore, 100% diagnosis is difficult because it is expressed as the failure rate of recognition. Moreover, applying the diagnostic apparatus is difficult because it shows differences that depend on the conditions and environments of industrial sites. The challenge was to perfectly solve the turn-to-turn short fault problem of induction motors even if using a somewhat complex method because they can cause major accidents.