关键词: 故障诊断, 经验模态分解, 本征模函数能量矩, 贝叶斯, 最小二乘支持向量机, 参数优化

Abstract: 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%.

Key words: fault diagnosis, empirical mode decomposition, energy moment of eigenmode function, Bayesian, least squares support vector machine, parameter optimization