The　incipient　damageof　wind　turbine　rolling　bearingsis　very　difficult　to　be　detected,　because　the　fault　signalsare　nonlinear,　nonstationary,　and　likely　to　be　buried　by　strong　background　noise.　In　light　of　this　problem,　a　comprehensive　methodology　that　combines　variational　modal　decomposition　(VMD)　and　maximum　correlated　kurtosis　deconvolution　(MCKD)　is　presented.　The　parameters　of　VMD　and　MCKD　are　selected　automatically　by　the　particle　swarm　optimization　algorithm　(PSO).　First,　the　optimal　α　and　K　in　VMD　are　calculated　by　PSO,　and　the　most　sensitive　modal　is　selected　according　to　the　VMD　decomposition　of　incipient　fault　signals.　Then,　theoptimal　L　and　T　in　MCKD　algorithm　are　calculated　by　PSO　so　as　to　boost　the　fault　shock　in　the　modal.　Finally,　the　incipient　fault　feature　is　extractedfrom　the　envelope　demodulation　of　the　faults.　Simulation　results　as　well　as　experimental　tests　both　validate　that　the　proposed　method　can　adaptively　enhance　the　weak　fault　component　of　rolling　bearing,　thus　can　effectively　extract　incipient　fault　features　of　rolling　bearings　from　strong　background　noise.　©　2020,　Editorial　Department　of　JVMD.　All　right　reserved.