Enhanced Fault Detection in Rolling Element Bearings Using Kurtogram-Driven Spectral Kurtosis for Optimal Band Selection
Abstract: This paper introduces a robust methodology aimed at improving the detection and diagnosis of rolling element bearing faults, particularly in settings where other machinery components create masking signals that complicate fault identification. The proposed approach integrates advanced signal processing techniques with a straightforward classification method to achieve precise fault diagnosis. The methodology involves signal preprocessing, which includes wavelet transform-based denoising and normalization to enhance the signal-to-noise ratio and standardize signal amplitude.

Authors: Sandeep Yadav, Khemraj Beragi

File Name: Sandeep_12011-24-103.pdf
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