Abstract: Rolling bearings are common parts in rotating mechanical structures and can cause significant damage if they fail.With the advent of the era of big data,modern intelligent diagnostic methods are widely used in bearing fault diagnosis.Aiming at the problems existing in the intelligent diagnosis method,this paper introduced the statistical model into the bearing fault diagnosis,and proposed a fault diagnosis method based on the variational Bayesian.The method performs local feature scale decomposition on the bearing vibration signal to obtain several intrinsic scale components and extracts the time domain feature composition feature set.The feature set training is used to generate the mixed multidimensional Gaussian distribution model based on variational Bayes,and the different bearings are calculated.The probability of failure is to achieve fault diagnosis.The experimental results show that the diagnostic accuracy rate is 99.6%.Compared with the bearing diagnosis method based on support vector machine,diagnostic accuracy rate is up to 39.6%.The proposed method can comprehensively and effectively diagnose rolling bearing faults,and has a good diagnostic effect on high-dimensional complex fault data.

Key words: Bearing fault diagnosis, Gaussian mixture model, Local characteristic scale decomposition, Variational Bayes