To　accurately　extract　fault　information　from　rolling　bearing　(RB)　vibration　signals　with　strong　nonlinear　and　non-stationary　characteristics,　a　novel　method　using　adaptive　variational　mode　decomposition　with　noise　suppression　and　fast　spectral　correlation　(AVMDNS-FSC)　is　proposed.　The　AVMDNS　algorithm　can　adaptively　select　VMD　parameters　K　and　alpha,　which　reduces　the　error　caused　by　the　improper　selection　of　VMD　parameters　based　on　experience　or　prior　knowledge　of　the　signal.　Meanwhile,　the　AVMDNS　also　effectively　suppresses　noise　in　intrinsic　mode　function　(IMFs)　and　avoids　unexpected　removal　of　the　IMFs　containing　important　fault　information.　In　addition,　the　FSC　can　further　suppress　residual　noise　and　interference　harmonics　to　enhance　the　periodic　fault　pulses　and　hence　accurately　extract　bearing　fault　features.　Simulation　analysis　and　experimental　studies　are　carried　out　through　comparison　with　other　methods.　Results　show　that　the　AVMDNS-FSC　method　has　higher　sensitivity　and　effectiveness　in　extracting　early　periodic　fault　pulses　of　RB　vibration　signals.