Vibration　analysis　is　one　of　the　main　effective　ways　for　rolling　bearing　fault　diagnosis,　and　a　challenge　is　how　to　accurately　separate　the　inner　and　outer　race　fault　features　from　noisy　compound　faults　signals.　Therefore,　a　novel　compound　fault　separation　algorithm　based　on　parallel　dual-Q-factors　and　improved　maximum　correlation　kurtosis　deconvolution　(IMCKD)　is　proposed.　First,　the　compound　fault　signal　is　sparse-decomposed　by　the　parallel　dual-Q-factor,　and　the　low-resonance　components　of　the　signal　(compound　fault　impact　component　and　small　amount　of　noise)　are　obtained,　but　it　can　only　highlight　the　impact　of　compound　faults,　and　failed　to　separate　the　inner　and　outer　race　compound　fault　signal.　Then,　the　MCKD　is　improved　(IMCKD)　by　optimizing　the　selection　of　parameters　(the　shift　order　M　and　the　filter　length　L)　based　on　the　iterative　calculation　method　with　the　Teager　envelope　spectral　kurtosis　(TEK)　index.　Finally,　after　the　composite　fault　signal　is　filtered　and　de-noised　by　the　proposed　method,　the　inner　and　outer　race　fault　signals　are　obtained　respectively.　The　fault　characteristic　frequency　is　consistent　with　the　theoretical　calculation　value.　The　results　show　that　the　proposed　method　can　efficiently　separate　the　mixed　fault　information　and　avoid　the　mutual　interference　between　the　components　of　the　compound　fault.