This　paper　presents　a　fault　diagnosis　scheme　based　on　morphological　multi-fractal　(MMF)　analysis　and　improved　grey　relational　analysis　(IGRA)　for　rolling　element　bearings.　In　this　scheme,　firstly,　the　multi-fractal　characteristics　of　bearing　signals　are　illustrated　by　quality　index　and　partition　function.　Secondly,　the　parameters　of　generalized　fractal　dimension　and　multi-fractal　spectrum　in　different　bearing　operating　conditions　are　calculated　by　morphology,　from　which　some　parameters　with　good　discrimination　ability　are　selected　as　fault-related　feature.　Thirdly,　maximizing　deviation　is　employed　to　improve　the　reliability　of　the　classical　grey　relational　analysis.　Finally,　the　effectiveness　of　this　method　is　verified　by　simulation　analysis　and　application　example.　The　results　show　that　the　proposed　scheme　can　recognize　the　different　fault　categories,　which　is　more　stable　and　higher　accurate　than　the　traditional　method,　and　the　operation　time　is　shorter,　which　is　suitable　for　solving　practical　engineering　problems.　©　2021,　Editorial　Department　of　JVMD.　All　right　reserved.