%0 Journal Article %A DENG Hui %A LUO Qian %A WANG Yan %T Rolling Bearing Diagnosis Based on IMF Energy Moments and Bayes-LSSVM %D 2019 %R 10.3778/j.issn.1002-8331.1806-0058 %J Computer Engineering and Applications %P 156-161 %V 55 %N 17 %X For the traditional method, the fault feature extraction effect of rolling bearings is still limited, and the parameters of the least squares support vector machine classifier are not easy to determine, which reduces the accuracy of fault diagnosis. Least squares support vector machine based on the energy moment of the eigenmode function and the Bayesian framework is proposed for fault diagnosis of rolling bearings. In this method, the original signal is decomposed into multiple eigenmode functions by empirical mode decomposition, and then the integral of the time axis is used as the eigenmode function to obtain the energy-moment feature fault vector of the intrinsic mode function. Bayesian inference method is used to perform three-level hierarchical inference and the parameters of the least squares support vector machine classifier have arbitrariness and uncertainty, and the parameters are optimized. The simulation results of the rolling bearing show that this method can effectively and accurately diagnose the fault, and the diag-nostic accuracy rate reaches 98.75%. %U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.1806-0058