Time–Frequency Analysis for Efficient Fault Diagnosis and Failure Prognosis for Interior Permanent-Magnet AC Motors

The detection of noncatastrophic faults in conjunction with other factors can be used to determine the remaining life of an electric drive. As the frequency and severity of these faults increase, the working life of the drive decreases, leading to eventual failure. In this paper, four methods to identify developing electrical faults are presented and compared. They are based on the short-time Fourier transform, undecimated-wavelet analysis, and Wigner and Choi–Williams distributions of the field-oriented currents in permanent-magnet ac drives. The different fault types are classified using the linear-discriminant classifier and $k$-means classification. The comparison between the different methods is based on the number of correct classifications and Fisher's discriminant ratio. Multiple-class discrimination analysis is also introduced to remove redundant information and minimize storage requirements.