Condition monitoring methods, failure identification and analysis for Induction machines

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 Shorttime 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