Faults　in　rolling　element　bearings　often　cause　the　breakdown　of　rotating　machinery.　Not　only　the　fault　type　identification　but　also　the　fault　severity　assessment　is　important.　So　this　paper　emphasizes　the　fault　severity　assessment.　The　method　proposed　in　this　paper　contains　two　steps:　first,　identify　the　fault　type　based　on　the　combination　of　empirical　mode　decomposition　(EMD)　and　fast　kurtogram;　Second,　assess　the　fault　severity.　In　the　first　step,　the　original　signal　is　firstly　decomposed　into　some　intrinsic　mode　functions　(IMFs)　and　the　representative　IMFs　are　selected　based　on　correlation　analysis,　and　then　the　reconstruction　signal　(RS)　is　generated;　Secondly,　the　fast　kurtogram　method　is　applied　to　the　RS,　and　the　optimum　band　width　and　center　frequency　is　obtained.　The　fault　type　can　be　identified　based　on　the　fault　characteristic　frequency　marked　in　the　envelope　demodulation　spectrum.　In　the　second　step,　the　energy　percentage　of　the　most　fault-related　IMF　is　chosen　as　an　indicator　of　the　fault　severity　assessment.　Experimental　data　of　rolling　element　bearings　inner　raceway　fault　(IRF)　with　three　severities　at　four　running　speeds　were　analyzed.　The　results　show　that　the　IRF　identification　and　fault　severity　assessment　is　realized.　The　breakthrough　attempt　provides　the　great　potential　in　the　application　of　condition　monitoring　of　bearings.