Vibration signals of fault rolling bearings contain interference signals, which makes it difficult to extract fault information accurately. In this paper, a fault diagnosis method for fault rolling bearings was proposed based on singular value decomposition (SVD), variational mode decomposition ( VMD) and support vector machine ( SVM). First, the singular value decomposition was used to process the signal, and the effective order of the reconstructed matrix was determined according to the kurtosis difference spectrum of the singular value. Then, the reconstructed signal was reconstructed according to the effective order, and the VMD decomposition was performed on the reconstructed signal. The number of the decomposed intrinsic mode function ( IMF) components was determined according to the above effective order. From the IMF component of the decomposed to extract the fault characteristic parameters, as the input parameters of support vector machine ( SVM) to fault diagnosis. Finally validated bearing tester adopts Hefei university of technology, and directly into the decomposition of VMD and band-pass filter signal denoising based fault diagnosis method is compared, the results show that the method can effectively identify roller bearing fault type and can also be used for rolling bearing fault diagnosis.