Minimum nonprobabilistic entropy deconvolution for fault diagnosis of rolling element bearings

Abstract

The blind deconvolution methods (BDMs) is one of the most common methods for fault diagnosis of rolling bearings, and it is essential to maintain the safe and reliable operation of mechanical equipment. However, noise interference and the need for prior periods limit the scope of application of the BDMs. In this paper, a new minimum nonprobabilistic entropy deconvolution (MNPED) method is proposed. According to the correlation between fault impact and non-Gaussianity, the Gaussian membership function in fuzzy set theory is used to map the sample points to the membership degree of Gaussian distribution, and then the nonprobabilistic entropy (NPE) is formed to measure the impact characteristics of the signal. Then NPE is incorporated into the iterative process of solving the filter coefficient. Finally, the target signal and the optimal filter coefficient are selected based on the criterion of minimum NPE. MNPED is capable of adaptively extracting the periodic pulse of a signal without requiring prior knowledge of the period, even in the presence of strong noise interference. The effectiveness and robustness of the proposed approach are validated through simulation and experimental data.