Vibration　signals　of　rolling　element　bearings　(REBs)　contain　substantial　bearing　motion　state　information.　However,　the　property　of　nonlinear　and　nonstationary　vibration　signals　decreases　the　diagnostic　accuracy　of　REBs.　To　improve　the　accuracy　of　fault　diagnosis　for　REBs,　an　ensemble　approach　based　on　ensemble　empirical　mode　decomposition　(EEMD),　multi-scale　permutation　entropy　(MPE),　and　backpropagation　(BP)　neural　network　optimized　by　genetic　algorithm　(GA)　is　proposed.　Firstly,　the　REBs　are　decomposed　into　a　set　of　intrinsic　mode　functions　(IMFs)　that　contain　various　fault　features　by　EEMD.　The　fault　features　of　the　first　four　IMFs　are　extracted　by　MPE,　and　the　feature　vectors　are　formed.　Then,　the　BP　neural　network　optimized　by　GA　is　utilized　as　a　classifier　for　fault　diagnosis　to　train　and　test　the　feature　vector　set,　and　the　fault　diagnosis　of　the　REBs　is　realized　in　the　form　of　probability　output.　Experimental　results　show　that　the　proposed　method　can　identify　the　fault　pattern　of　the　vibration　signals　of　REBs　precisely.　Compared　with　the　existing　fault　diagnosis　methods,　the　proposed　method　can　realize　the　fault　diagnosis　of　REBs　with　16　fault　patterns,　and　demonstrates　an　excellent　accuracy　rate.