This　study　addresses　the　high　level　of　misdiagnoses　and　low　reliability　of　individual　rolling　element　bearing　fault　diagnosis　methods　by　proposing　a　fault　diagnosis　scheme　with　enhanced　diagnosis　accuracy　that　combines　the　results　of　two　individual　diagnosis　methods　based　on　an　improved　information　fusion　method.　The　proposed　scheme　applies　variational　mode　decomposition　in　conjunction　with　a　support　vector　machine　for　conducting　fault　diagnosis　in　the　frequency　domain,　which　achieves　high　fault　diagnosis　precision　for　learned　fault　conditions.　Meanwhile,　good　generalization　ability　is　achieved　for　identifying　the　operational　conditions　of　bearings　in　the　time　domain　by　integrated　mathematical　morphology　and　correlation　analysis.　Subsequently,　conflicts　arising　between　evidences　derived　from　the　individual　detection　results　are　measured　comprehensively　using　a　novel　strategy,　and　the　evidences　are　then　combined　based　on　the　Dempster-Shafer　theory　(DST)　to　enhance　the　information　fusion　effect.　The　effectiveness　of　the　proposed　information　fusion　method　is　verified　by　means　of　a　numerical　example　in　comparison　with　other　fusion　methods　based　on　DST.　The　experimental　application　of　the　proposed　information　fusion　fault　diagnosis　scheme　demonstrates　the　complementary　advantages　of　the　two　individual　methods　for　significantly　improving　the　diagnosis　accuracy　relative　to　the　accuracies　of　the　individual　methods　alone.