As　the　most　basic　component　of　rotating　machinery,　rolling　bearing　frequently　works　in　harsh　environments　and　complex　working　conditions,　and　its　health　status　affects　seriously　the　working　efficiency.　The　health　statuses　of　rolling　bearing　can　not　only　reduce　equipment　maintenance　costs　but　also　contribute　to　reducing　major　accidents.　Based　on　this,　an　adaptive　diagnosis　method　that　combines　deep　gated　recurrent　unit　(DGRU)　with　wavelet　packet　decomposition　(WPD)　and　extreme　learning　machine　(ELM)　is　proposed　for　rolling　bearing.　Firstly,　WPD　is　utilized　to　eliminate　the　noise　of　data.　Secondly,　DGRU　is　designed　to　extract　the　representative　features　of　denoised　data.　Finally,　ELM　is　utilized　to　output　the　diagnosis　results.　Massive　results　prove　that　the　superiority　and　robustness　of　our　approach　outperform　existing　popular　methods.　Additionally,　the　proposed　method　can　also　achieve　powerful　antinoise　ability.　©　2022　Zhen　Li　et　al.