The　weak　fault-related　features　of　vibration　signal,　which　originates　from　wind　turbine　rolling　element　bearings,　are　generally　immersed　in　environmental　noise　and　harmonic　interference　and　difficult　to　extract.　This　issue　is　addressed　in　this　paper　by　proposing　a　new　enhanced　morphological　filtering　scheme　for　fault　diagnosis.　Firstly,　a　new　morphology　analysis　method,　named　morphological　comprehensive　filter-hat　transform　(MCFHT),　was　constructed　to　extract　fault-related　impulses　from　measured　signal　in　strong　background　noise.　And　its　filtering　property　was　investigated　by　the　nonlinear　filter　frequency　response　characteristics,　which　provides　a　theoretical　basis　for　the　application　of　fault-related　impulses　extraction.　Secondly,　an　adaptive　scale　selection　strategy　was　explored　to　obtain　appropriate　filter　scale　for　MCFHT.　Thirdly,　an　improved　envelope　derivative　energy　operator　was　utilized　to　enhance　the　impulse　characteristics　of　the　signal　after　morphological　filtering　and　to　suppress　the　frequency　of　in-band　noise.　In　the　both　simulation　and　experimental　studies　for　wind　turbine　bearing,　the　proposed　method　delivered　better　fault　feature　extraction　and　noise　reduction　performance　than　the　traditional　methods.　©　2021,　Editorial　Office　of　Journal　of　Vibration　and　Shock.　All　right　reserved.