An enhanced rolling bearing fault detection method combining sparse code shrinkage denoising with fast spectral correlation

Rolling bearings are important supporting components widely used in rotating machinery and are prone to failure, it is thus important to perform fault detection of rolling bearing quickly and accurately. Aiming at the problem that it is difficult to extract the weak impulses buried in strong background noise in rolling bearing fault diagnosis, this paper proposes an enhanced fault detection method combining sparse code shrinkage denoising with fast spectral correlation according to the cyclic statistical properties of defective bearing vibration signals. First, in view of the non-Gaussian statistical properties of the periodic impulses caused by the localized bearing defect in vibration signals, the sparse code shrinkage algorithm is employed to denoise the original noisy signal, thereby highlighting the periodic impulses. Then, the Fast Spectral Correlation (Fast-SC) algorithm is used to process the denoised signal to get the cyclic spectral correlation. Finally, the squared enhanced envelope spectrum (SEES) is presented to effectively detect and identify the rolling bearing faults. Experimental results demonstrate the validity and superiority of the proposed method in rolling bearing fault detection through the comparison with the Fast-SC, spectral kurtosis and Infogram. (C) 2020 ISA. Published by Elsevier Ltd. All rights reserved.