Enhanced Diagnosis of Broken Rotor Bars in Induction Machines Using Combined Envelope and Maximum Likelihood Estimation Approach

This paper presents an improved approach for diagnosing broken rotor bars in induction machines by combining peak detection envelopes with the maximum likelihood estimation approach (MLEA). The proposed method releases the fault signature by transforming three current envelopes into two in-phase and quadrature components using MLEA. The modulus of these components (MMLEA) serves as the fault signature. The classical Fast Fourier Transform (FFT) subsequently analyses this signature in the frequency domain. The approach is theoretically demonstrated and experimentally confirmed by testing three induction motors under various operational circumstances, including low load. The experimental results are compared with classical technique such as motor current signature analysis (MCSA), MLEA, and combining the envelope with MLEA extracted by peak detection and Hilbert transform (HT). These comparisons demonstrate the robustness and effectiveness of the proposed approach for diagnosing broken rotor bars.