With　the　manufacturing　industry　stepping　into　the　emerging　new　era　of　big　data　and　intelligence,　the　amount　of　data　collected　from　perception　and　monitoring　systems　with　multiple　smart　sensors　has　increased　tremendously.　Such　huge　amount　of　multisensory　data　may　not　only　power　many　aspects　of　fault　diagnosis,　but　also　bring　great　opportunities　and　challenges　in　modern　manufacturing　industry.　In　addition,　with　respect　to　intelligent　fault　diagnosis　for　machinery,　few　researches　have　been　focused　on　the　compound　fault　diagnosis　under　big-data　circumstance.　Therefore,　a　novel,　intelligent,　compound,　fault　decoupling　method　based　on　deep　capsule　network　(CN)　and　ensemble　learning　is　developed　for　compound　fault　decoupling　and　diagnosis　using　multisensory　data.　First,　a　decoupling　CN　(DCN)　is　constructed　as　the　basic　model.　Second,　taking　the　full　advantage　of　multisensory　data,　the　DCN　model　can　be　pretrained　with　multiple　sensor　data,　which　can　obtain　various　pretrained　DCN　models.　Finally,　combining　with　ensemble　learning　skill,　the　pretrained　DCN　models　are　integrated　by　a　combination　strategy　to　obtain　the　deep　ensemble　CN　(DECN)　model　for　intelligent　compound　fault　decoupling　and　diagnosis.　The　performance　of　the　DECN　model　is　validated　on　an　automobile　transmission　(AT)　data　set　with　two　compound　faults,　and　the　experimental　results　illustrate　that　the　DECN　model　obtains　higher　diagnosis　accuracy　and　decouples　the　compound　fault　correctly.