In　actual　operating　conditions,　rolling　bearings　vibration　signals　are　easily　covered　by　heavy　noise,　increasing　the　difficulty　of　fault　diagnosis.　A　fault　diagnosis　method　based　on　auto　regressive　moving　average　(ARMA)　model　and　multipoint　optimal　minimum　entropy　deconvolution　adjusted　(MOMEDA)　algorithm　is　proposed　to　address　this　issue.　Firstly,　ARMA　model　is　used　to　remove　the　intrinsic　components　and　pre-whitening　the　signals.　Then　parameters　of　MOMEDA　are　optimized　by　Sparrow　Search　Algorithm　(SSA),　the　periodic　fault　signals　are　recovered　by　the　optimized　MOMEDA　and　the　secondary　noise　reduction　of　the　signals　is　realized.　Finally,　a　class　of　time-domain　average　dimensionless　features,　namely　average　pulse　factor,　average　kurtosis　factor　and　average　margin　factor,　are　proposed　and　combined　with　the　Gini　index　as　fault　diagnosis　indexes　then　input　into　ELM　classifier　to　identify　fault　types.　Experimental　results　show　the　proposed　method　can　identify　fault　types　effectively　and　achieve　accurate　diagnosis　of　rolling　bearings.　©　2021　Elsevier　Ltd