The　rolling　bearings　often　suffer　from　compound　fault　in　practice.　Compared　with　single　fault,　compound　fault　contains　multiple　fault　features　that　are　coupled　together　and　make　it　difficult　to　detect　and　extract　all　fault　features　by　traditional　methods　such　as　Hilbert　envelope　demodulation,　wavelet　transform　and　empirical　node　decomposition　(EMD).　In　order　to　realize　the　compound　fault　diagnosis　of　rolling　bearings　and　improve　the　diagnostic　accuracy,　we　developed　negentropy　spectrum　decomposition　(NSD),　which　is　based　on　fast　empirical　wavelet　transform　(FEWT)　and　spectral　negentropy,　with　cyclic　extraction　as　the　extraction　method.　The　infogram　is　constructed　by　FEWT　combined　with　spectral　negentropy　to　select　the　best　band　center　and　bandwidth　for　band-pass　filtering.　The　filtered　signal　is　used　as　a　new　measured　signal,　and　the　fast　empirical　wavelet　transform　combined　with　spectral　negentropy　is　used　to　filter　the　new　measured　signal　again.　This　operation　is　repeated　to　achieve　cyclic　extraction,　until　the　signal　no　longer　contains　obvious　fault　features.　After　obtaining　the　envelope　of　all　extracted　components,　compound　fault　diagnosis　of　rolling　bearings　can　be　realized.　The　analysis　of　the　simulation　signal　and　the　experimental　signal　shows　that　the　method　can　realize　the　compound　fault　diagnosis　of　rolling　bearings,　which　verifies　the　feasibility　and　effectiveness　of　the　method.　The　method　proposed　in　this　paper　can　detect　and　extract　all　the　fault　features　of　compound　fault　completely,　and　it　is　more　reliable　for　the　diagnosis　of　compound　fault.　Therefore,　the　method　has　practical　significance　in　rolling　bearing　compound　fault　diagnosis.