In　view　of　the　fact　that　vibration　signals　of　rolling　bearings　are　much　contaminated　by　noise　in　the　early　failure　period,　this　paper　presents　a　new　denoising　SVD-VMD　method　by　combining　singular　value　decomposition　(SVD)　and　variational　mode　decomposition　(VMD).　SVD　is　used　to　determine　the　structure　of　the　underlying　model,　which　is　referred　to　as　signal　and　noise　subspaces,　and　VMD　is　used　to　decompose　the　original　signal　into　several　band-limited　modes.　Then　the　effective　components　are　selected　from　these　modes　to　reconstruct　the　denoised　signal　according　to　the　difference　spectrum　(DS)　of　singular　values　and　kurtosis　values.　Simulated　signals　and　experimental　signals　of　roller　bearing　faults　have　been　analyzed　using　this　proposed　method　and　compared　with　SVD-DS.　The　results　demonstrate　that　the　proposed　method　can　effectively　retain　the　useful　signals　and　denoise　the　bearing　signals　in　extremely　noisy　backgrounds.