An　enhanced　intelligent　diagnosis　method　for　rotary　equipment　is　proposed　based　on　multi-sensor　data-fusion　and　an　improved　deep　convolutional　neural　network　(CNN)　models.　An　improved　CNN　based　on　LeNet-5　is　constructed　which　can　enhance　the　features　of　the　samples　by　stacking　bottleneck　layers　without　changing　the　size　of　the　samples.　A　new　conversion　approaches　are　also　proposed　for　converting　multi-sensor　vibration　signals　into　color　images,　and　it　can　refine　features　and　enlarge　the　differences　between　different　types　of　fault　signals　by　the　fused　images　transformed　in　red-green-blue　(RGB)　color　space.　In　the　last　stage　of　network　learning,　visual　clustering　is　realized　with　t-distributed　stochastic　neighbor　embedding　(t-SNE)　to　evaluate　the　performance　of　the　network.　To　verify　the　effectiveness　of　the　proposed　method,　examples　in　practice　such　as　the　diagnosis　for　the　wind　power　test　rigs　and　industrial　fan　system　are　provided　with　the　prediction　accuracies　of　99.89%　and　99.77%,　respectively.　In　addition,　the　efficiency　of　other　comparative　baseline　approaches　such　as　the　deep　belief　network　and　support　vector　machine　(SVM)　is　evaluated.　In　conclusion,　the　proposed　intelligent　diagnosis　method　based　on　multi-sensor　data-fusion　and　CNN　shows　higher　prediction　accuracy　and　more　obvious　visualization　clustering　effects.