Incipient　damages　of　wind　turbine　rolling　bearing　are　very　difficult　to　be　detected　because　of　the　interference　of　multi-frequency　components　and　strong　ambient　noise.　To　solve　this　problem,　this　paper　proposes　a　new　detected　method　named　VMD-AMCKD,　combining　complementary　advantages　of　variational　mode　decomposition　(VMD)　and　adaptive　maximum　correlated　kurtosis　deconvolution　(AMCKD).　A　novel　index　is　proposed　to　screen　out　the　most　sensitive　mode　containing　fault　information　after　VMD　decomposition.　The　mode　also　can　determine　a　suspectable　range　for　the　fault　frequency,　based　on　which　the　optimized　range　of　devolution　period　T　in　MCKD　can　be　pre-determined.　The　Grasshopper　optimization　algorithm　(GOA)　is　adapted　to　adaptively　select　the　key　parameters　in　MCKD.　The　proposed　method　can　successfully　diagnose　the　simulated　signal　mixed　with　strong　white　Gaussian　noise.　Its　robustness　is　further　proven　by　the　diagnosis　for　three　different　types　of　experimental　signal　from　CWRU　bearing　data　center.　Finally,　the　VMD-AMCKD　is　applied　to　detect　incipient　damages　of　rolling　bearings　in　a　laboratory　wind　turbine.