In order to improve the accuracy of rolling bearing performance degradation assessment for one class support vector machine (OCSVM), an ensemble empirical mode decomposition method based on adaptive white noise was proposed. A performance degradation evaluation method combining CEEMDAN, particle swarm optimization ( PSO) and One class SVM. Firstly, CEEMDAN was used to expand the collected vibration signal calculation into intrinsic mode functions ( IMFs), and typical characteristic signals were obtained according to the IMFs energy density. Secondly, the parameters ν of OCSVM and radial basis kernel function g are optimized by particle swarm optimization to enhance the learning ability and generalization ability of OCSVM. Finally, 3σ was used to set the adaptive threshold, determine the early failure threshold of the bearing and verify the accuracy of the evaluation results by using the CEEMDAN and Hilbert envelope demodulation method. The validity of the proposed model was verified by bearing experimental life data from the University of Cincinnati. The results show that the PSO algorithm optimized OCSVM model can accurately monitor the bearing running life state. Compared with the support vector data description ( SVDD) and parameter optional OCSVM model, the performance degradation assessment model of this method is more effective and superior.