Motor Current Signature Analysis Using Robust Modulation Spectrum Correlation Gram for Gearbox Fault Detection

Spectrum correlation (SC), as a typical demodulation algorithm, has been investigated for fault extraction by restraining interference components. However, SC ignores the uneven distribution of fault features in the entire frequency range, which makes the results vulnerable to interference components. To overcome these shortcomings, a novel robust modulation spectrum correlation (RMSC) gram is proposed. First, the signal is demodulated into a bispectral map display containing fundamental and modulation frequency through RMSC, and a finite impulse response filter on fundamental frequency is utilized to obtain RMSC subbands. Subsequently, the fault feature index of subbands under healthy and fault conditions is calculated, and its failure signature ratio (FSR) to generate RMSC gram is utilized. Finally, the RMSC with the maximum FSR is selected as an optimal subband, and envelope analysis is executed on the subband to obtain fault features. Simulations and experiments are performed to validate the effectiveness of RMSC in comparison with the state-of-the-art methods.

Automated summary

In this paper, a novel robust modulation spectrum correlation (RMSC) gram algorithm is proposed for fault extraction. The signal is demodulated into a bispectral map display containing fundamental and modulation frequency through RMSC, and RMSC subbands are obtained using a finite impulse response filter on the fundamental frequency. The fault feature index of subbands under healthy and fault conditions is calculated to generate RMSC gram using its failure signature ratio (FSR). The RMSC with the maximum FSR is selected as the optimal subband, and envelope analysis is performed on the subband to extract fault features.