# Fault Detection of Stator Inter-Turn Short-Circuit in PMSM on Stator Current and Vibration Signal
## 1 Introduction  
With the development of technology, Permanent magnet Synchronous Motors (PMSM) have become some of the most important electric machines. In PMSM, the magnetic flux induced in the stator windings is generated by rare-earth magnets located on the rotor [1]. As a result, PMSM has some excellent features, such as high efficiency, high output to volume ratio, high power to weight ratio and low noise emissions [2]. With these advantages, PMSM is widely used in some industrial applications, such as robotic, automotive and electric traction. However, a motor fault might cause production shutdowns. These shutdowns may lead to wasting production time and raw resources. Therefore, it is necessary to focus on the condition monitoring and fault diagnosis methods. PMSM usual faults include electrical faults, mechanical faults and magnetic faults. Stator inter-turn short-circuit belongs to electrical faults [3], and it is one of the most common motor faults in PMSM. There are three common fault diagnosis methods: model-based, knowledge-based, and signal-based. The model-based method requires an accurate mathematical model of the motor, while knowledge-based method requires extensive expert experience. Thus, signal-based method is widely used in the field of PMSM fault diagnosis, and the method is fast and does not need any specific model. Signal-based method detects fault by processing the signals collected from the motors to find the difference between healthy PMSM and faulty PMSM. Current and vibration signals have long been used in motor fault detection [4]. Current signals are the most commonly used, one reason obeing that these signals are not disturbed acoustic signals [5]. Vibration signals are often used inthe diagnosis of bearing faults [6]. As for signal processing tools, fast Fourier transform (FFT) is a traditional tool to extract the frequency characteristics of the stator current signal [7]. However, it is sometimes difficult to obtain obvious fault characteristics by only relying on the stator current signal. Besides, due to loss of time information, FFT can only be used to process stationary signal. Therefore, the time-frequency techniques have received more attention, such as short-time Furrier transform (STFT), wavelet transform (WT) and the empirical wavelet transform (EWT) [8]. There have been some studies using signal processing method to diagnose motor faults. [PERSON] and [PERSON] found that, when the stator inter-turn short-circuit fault occurs in the motor, an increased fault harmonic occurs in the stator current [9; 10]. [PERSON], [PERSON] et al. proposed a strategy based on a motor fault model [11]. Using MATLAB/Simulink, the current characteristics in the fault condition were found and the short circuit fault was detected. In [12], the PMSM stator current signal is decomposed by wavelet packet transform, and the signal energy value of each frequency band is calculated. The increase of energy is used as the basis of fault judgment. In [13], by analyzing the harmonic components of the current and voltage of the motor with inter-turn short circuit fault, the FFT transform is used to obtain the characteristics of the space vector spectrum to detect the fault. In [14], wavelet analysis is used to extract the fault characteristics of the induction motor, which is better than the Fourier transform. In [15; 16], wavelet analysis is also used in PMSM fault diagnosis. A method based on a variety of signals has been used in the fault diagnosis of induction motors [17]. Some studies [18; 19] adopt the method of fusion of stator current, vibration and noise signals, and detect the short circuit fault based on the signal spectrum increment. In this paper, both stator current signal and vibration signal are used for the detection of short circuit fault. The two parameters are analyzed by the time-frequency method using improved wavelet packet transform. Improved wavelet packet transform can eliminate spectrum aliasing and get accurate fault characteristics.