A comparative study of FFT, STFT and wavelet techniques for induction machine fault diagnostic analysis (original) (raw)

Motor Current Signature Analysis (MCSA) has been successfully used for fault diagnosis in induction machines. The current spectrum of the induction machine for locating characteristic fault frequencies is used in MCSA. The spectrum is obtained using a Fast Fourier Transformation (FFT) that is performed on the signal under analysis. The fault frequencies occur in the motor current spectra are unique for different motor faults. However FFT does not always achieve good results with non-constant load torque. Other signal processing methods, such as Short-time Fourier Transform (STFT) and Wavelet transforms techniques may also be used for analysis. These techniques are capable of revealing aspects of data like trends, breakdown points, discontinuities in higher derivatives, and self-similarity which are not available in FFT analysis. In the present paper, the comparisons of various techniques are discussed to analyze the experimental results obtained.