Intelligent　compound　fault　diagnosis　technology　is　significantly　important　to　ensure　that　rotating　machinery　works　in　high-efficiency,　security　and　reliability,　and　it　remains　a　great　challenge　in　this　field.　A　lot　of　traditional　intelligent　fault　diagnosis　techniques　have　been　developed　with　certain　achievements　in　recent　years,　however,　these　methods　inherently　suffer　from　the　obvious　shortcoming　that　the　traditional　classifier　only　outputs　one　label　for　a　testing　sample　of　compound　fault,　rather　than　multiple　labels.　Consequently,　it　cannot　classify　a　compound　fault　as　two　or　more　single　faults.　To　solve　this　problem,　a　novel　method　named　1D　DCNN-MLC,　One-Dimensional　Deep　Convolutional　Neural　Network　(1D　DCNN)　with　a　Multi-Label　Classifier　(MLC),　is　proposed　for　intelligent　compound　fault　identification.　1D　DCNN　is　employed　to　learn　the　representations　from　the　vibration　raw　signals　effectively.　MLC　is　then　designed　to　discriminate　and　predict　the　single　or　compound　fault　by　outputting　single　or　multiple　labels.　The　proposed　method　is　validated　by　a　gearbox　dataset　with　bearing　fault,　gear　fault　and　compound　fault.　The　experimental　results　demonstrate　that　the　proposed　method　can　effectively　detect　and　identify　compound　fault.　To　the　best　knowledge　of　the　authors,　this　work　is　the　first　effort　to　identify　compound　fault　for　rotating　machinery　via　outputs　multiple　labels.　©　2019　IEEE.