Rolling bearing is significant research content for rolling machine condition monitoring and fault diagnosis. In order to diagnose the rolling bearing fault position and degree more effectively, a rolling bearing fault diagnosis method based on Hilbert marginal spectrum and support vector data description (SVDD) optimized by improved particle swarm optimization (IPSO) is proposed. In this method, the rolling bearing vibration signal is decomposed into a set of intrinsic mode functions (IMFs), then marginal spectrum and autoregressive (AR) model parameters are established and system feature vector is constructed of AR parameters and feature power function, which is obtained from marginal spectrum. In order to solve the problem of deciding SVDD’s significant parameters by traditional gridsearching or experience, a method using dynamic factor based particle swarm algorithm is used to find the optimized SVDD’s significant parameters penalty constant C and kernel function width σ, and the optimized model is put into use of intelligent rolling bearing fault diagnosis. The experiment results of manual and real data sets show that different kinds of rolling bearing fault conditions can be recognized effectively by the proposed method with higher efficiency and precision than traditional gridsearching method.