The　bearing　vibration　of　wind　turbines　is　nonlinear　and　non-stationary.　To　effectively　extract　bearing　vibration　signal　features　for　fault　diagnosis,　a　method　of　feature　vector　extraction　based　on　variational　mode　decomposition　(VMD)　and　energy　entropy　is　proposed.　In　addition,　the　support　vector　machine　(SVM)　classifier　is　used　to　identify　the　types　of　vibration　faults.　VMD　transforms　the　constrained　variational　objective　function　into　the　unconstrained　one　to　optimize　solution.　Compared　with　the　VMD　and　empirical　mode　decomposition　(EMD),　the　modal　decomposition　layer　of　VMD　is　less　than　EMD,　no　found　false　modality,　and　truly　reflecting　signal　components.　After　the　Hilbert　transformation,　double　logarithmic　coordinates　show　that　the　VMD-based　spectral　characteristics　are　significant.　VMD　is　performed　on　the　different　types　of　vibration　signals　of　wind　turbines.　Therefore,　VMD　is　not　that　affected　by　noise　and　has　few　decomposition　levels.　The　energy　entropy　of　the　normalized　four　modal　components　is　considered　the　eigenvalue　and　classified　by　SVM,　and　compared　with　EMD-based　and　wavelet　db4-based　energy　entropy　eigenvalue　extraction　methods.　Experimental　results　indicate　that　the　accuracy　of　the　method　is　higher　than　those　based　on　EMD　and　wavelet　db4,　under　the　limited　sample　condition.　Thus,　a　referential　diagnostic　method　is　provided　for　practical　applications.　©　2019　Elsevier　Ltd