To　solve　the　problem　of　noise　interference　in　bearing　vibration　signals,　a　hierarchical　adaptive　wavelet　threshold　function　denoising　method　is　proposed.　First,　the　bearing　vibration　signal　is　decomposed　into　wavelet　coefficients　obtaining　the　wavelet　coefficients　of　each　decomposition　layer.　After　that,　the　wavelet　coefficients　of　low　frequency　signals　are　retained　and　the　wavelet　coefficients　of　high　frequency　signals　are　processed　by　hierarchical　adaptive　thresholding.　Finally,　the　wavelet　of　coefficients　after　threshold　processing　is　reconstructed　to　get　the　denoised　signal.　By　constructing　a　hierarchical　adaptive　threshold　function　that　is　continuous　at　the　threshold　and　derivable　in　wavelet　domain,　the　defects　of　the　reconstruction　deviation　of　traditional　threshold　function　and　excessive　noise　reduction　can　be　improved.　There　is　a　trend　of　the　threshold　function　parameter,　affected　by　the　occupying　ratio　of　noise　energy.　By　adjusting　this　parameter,　the　threshold　function　can　be　adaptively　obtained　in　each　wavelet　decomposition　layer　to　achieve　more　effective　denoising　effect.　The　simulation　results　of　the　noise　reduction　for　bearing　fault　simulation　signal　show　that　the　signal-to-noise　ratio　(SNR)　and　the　root　mean　square　error　(RMSE)　of　the　proposed　method　are　better　than　others,　and　has　better　noise　reduction　effect　as　well.　The　experiments　of　on　the　mechanical　fault　simulation　test　rig　show　that　this　method　saves　more　fault　information　while　removing　noise,　and　thus　improves　the　fault　diagnosis　rate　for　the　noise　reduction　of　bearings.　©　2019,　Nanjing　Univ.　of　Aeronautics　an　Astronautics.　All　right　reserved.