This　paper　proposed　a　novel　fault　recognition　method　for　rotating　machinery　on　the　basis　of　multi-sensor　data　fusion　and　bottleneck　layer　optimized　convolutional　neural　network　(MB-CNN).　A　conversion　method　converting　vibration　signals　from　multiple　sensors　to　images　is　proposed　that　can　integrate　information　to　get　richer　features　than　vibration　signals　from　single　sensor.　By　this　method　feature　maps　of　different　fault　types　can　be　obtained　without　tedious　parameter　adjustments.　Based　on　the　feature　maps　from　multi-sensor　data,　a　corresponding　novel　convolutional　neural　network　is　also　constructed.　The　constructed　network　performs　the　bottleneck　layers　with　an　increased　number　of　input　features　to　avoid　information　lost.　The　data　at　the　same　time　node　can　be　fused　by　the　convolutional　kernels　of　which　the　size　matches　the　number　of　sensors.　Practical　examples　of　diagnosis　for　the　wind　power　test　rig　and　the　centrifugal　pump　test　rig　are　given　in　order　to　verify　the　effectiveness　of　the　proposed　approaches,　and　prediction　accuracy　of　99.47%　and　97.32%　is　obtained　respectively.　Otherwise,　the　performances　of　other　conventional　methods　such　as　deep　belief　network　(DBN),　support　vector　machine　(SVM)　and　artificial　neural　network　(ANN)　are　evaluated　for　contrast　with　the　proposed　method.　As　shown　in　the　results,　the　novel　convolutional　neural　network　obtains　higher　recognition　accuracy　and　faster　convergence　speed.　(C)　2018　Elsevier　B.V.　All　rights　reserved.