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