  Frequency domain analysis using the Fast Fourier transform (FFT) has been a
popular method for diagnosing broken rotor bar (BRB) faults in squirrel-cage
induction motors (IM). However, FFT analysis is limited by sampling frequency
and time acquisition constraints, making it less effective under time-varying
conditions. To overcome these difficulties, a novel BRB fault detection method
for non-stationary conditions is proposed. The proposed strategy is based on
the recently developed robust local mean decomposition (RLMD) and Hilbert
transform (HT) methods. Using these techniques, the BRB characteristic
frequency and amplitude component are obtained from only one phase stator
current allowing automation of the features detection process. in fact, HT is
used to extract the stator current envelope (SCE). Then, the SCE is processed
by RLMD for determining the sub signals production functions (PFs). Finally, HT
is applied to the most sensible PF to compute its instantaneous frequency and
amplitude. The tracking of the BRB fault characteristic can inform us about the
condition of the induction motor. The effectiveness of the proposed diagnostic
strategy is validated through simulation conducted in the Matlab environment.
The simulation results show the capability of this method to track accurately
the frequency and amplitude of the 2sf component where f and s represent the
fundamental stator current frequency and motor slip respectively
