Wigner-Ville distributions for detection of rotor faults in brushless DC (BLDC) motors operating under non-stationary conditions

Electric motors often operate under operating conditions that are constantly changing with time. Most of such applications demand high reliability from the motor. Early detection of developing motor faults could help provide this needed reliability. While diagnostics of faults in motors operating under steady state conditions is straight forward due to the use of the well known Fourier transformation, diagnostics of motors operating under non-stationary conditions may need more sophisticated signal processing. In this paper, the Wigner-Ville family of time-frequency distributions is presented as an alternative to short time Fourier transforms (STFTs) and wavelets, for the diagnostics of rotor faults in a BLDC motor. Wigner-Ville distributions possess better frequency resolution than the STFT and wavelets. Several variants of the Wigner-Ville distributions are presented. The drawbacks of these distributions and methods to overcome them are also presented. Simulation and experimental results demonstrate that the Wigner-Ville distributions can be a viable tool to analyze rotor faults in BLDC motors operating under continuous non-stationarity.