Novel rotor fault diagnostic method based on rlmd and ht techniques
Asma Guedidi, Widad Laala

TL;DR
This paper introduces a new rotor fault detection method for induction motors using RLMD and HT techniques, which effectively identifies fault features under non-stationary conditions from single-phase current data.
Contribution
It presents a novel diagnostic approach combining RLMD and HT for accurate rotor fault detection in non-stationary environments, improving over traditional FFT methods.
Findings
Accurately tracks the 2sf component frequency and amplitude.
Effective under time-varying conditions.
Validated through Matlab simulations.
Abstract
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…
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