Condition monitoring methods, failure identification and analysis for Induction machines

Abstract

Induction motors are a critical component of many industrial processes and are frequently integrated in commercially available equipment and industrial processes. The studies of induction motor behavior during abnormal conditions, and the possibility to diagnose different types of faults have been a challenging topic for many electrical machine researchers. The Motor Current Signature Analysis (MCSA) is considered the most popular fault detection method now a day because it can easily detect the common machine fault such as turn to turn short ckt, cracked /broken rotor bars, bearing deterioration etc. This paper presents theory and some experimental results of Motor current signature analysis. The MCSA uses the current spectrum of the machine for locating characteristic fault frequencies. The spectrum is obtained using a Fast Fourier Transformation (FFT) that is performed on the signal under analysis. The fault frequencies that occur in the motor current spectra are unique for different motor faults. However this method does not always achieve good results with non-constant load torque. Therefore, different signal processing methods, such as Short- time Fourier Transform (STFT) and Wavelet transforms techniques are also proposed and compared in this paper. Keywords—Fault diagnosis, Fast Fourier transforms, MCSA, Short time Fourier transform (STFT), Wavelet transform I. INTRODUCTION DVANCES in digital signal processing technology have enabled researchers to process more data in less time. As a result, information that is not previously available can be extracted from the collected data. In the light of these developments, condition monitoring via MCSA has recently drawn more attention from researchers. MCSA focuses its efforts on the spectral analysis of the stator current and has been successfully used in the detection of broken rotor bars, bearing damage and the dynamic eccentricity (1). MCSA analyzes the stator current in search of current harmonics directly related to new rotating flux components, which are caused by faults in the motor-flux distribution (2). The advantage of this technique is that it is well recognized nowadays as a standard due to its simplicity: It needs only one current sensor per machine and is based on straightforward signal-processing techniques such as fast Fourier transforms (FFT). The MCSA uses the current spectrum of the machine for locating characteristic fault frequencies. The spectrum is obtained using a Fast Fourier Transformation (FFT) that is performed on the signal under analysis. But, the FFT in the stator current is quite difficult to apply with accuracy due to problems such as frequency resolution, magnitude accuracy at steady state, and more generally, due to data processing (3). Therefore, this paper also discusses some new methods (Short Time Fourier Transform, Wavelet transform) based on stator- current analysis for online fault detection in induction machines, which would overcome the averaging problems of classical FFT.