Working　conditions　of　rolling　bearings　of　wind　turbine　generators　are　complicated,　and　their　vibration　signals　often　show　non-linear　and　non-stationary　characteristics.　In　order　to　improve　the　efficiency　of　feature　extraction　of　wind　turbine　rolling　bearings　and　to　strengthen　the　feature　information,　a　new　structural　element　and　an　adaptive　algorithm　based　on　the　peak　energy　are　proposed,　which　are　combined　with　spectral　correlation　analysis　to　form　a　fault　diagnosis　algorithm　for　wind　turbine　rolling　bearings.　The　proposed　method　firstly　addresses　the　problem　of　impulsive　signal　omissions　that　are　prone　to　occur　in　the　process　of　fault　feature　extraction　of　traditional　structural　elements　and　proposes　a　＇W＇　structural　element　to　capture　more　characteristic　information.　Then,　the　proposed　method　selects　the　scale　of　multi-scale　mathematical　morphology,　aiming　at　the　problem　of　multi-scale　mathematical　morphology　scale　selection　and　structural　element　expansion　law.　An　adaptive　algorithm　based　on　peak　energy　is　proposed　to　carry　out　morphological　scale　selection　and　structural　element　expansion　by　improving　the　computing　efficiency　and　enhancing　the　feature　extraction　effect.　Finally,　the　proposed　method　performs　spectral　correlation　analysis　in　the　frequency　domain　for　an　unknown　signal　of　the　extracted　feature　and　identifies　the　fault　based　on　the　correlation　coefficient.　The　method　is　verified　by　numerical　examples　using　experimental　rig　bearing　data　and　actual　wind　field　acquisition　data　and　compared　with　traditional　triangular　and　flat　structural　elements.　The　experimental　results　show　that　the　new　structural　elements　can　more　effectively　extract　the　pulses　in　the　signal　and　reduce　noise　interference,　and　the　fault-diagnosis　algorithm　can　accurately　identify　the　fault　category　and　improve　the　reliability　of　the　results.　©　2021,　Institute　of　Automation,　Chinese　Academy　of　Sciences　and　Springer-Verlag　GmbH　Germany,　part　of　Springer　Nature.