Aiming　at　the　problem　that　it　is　difficult　to　separate　and　extract　the　composite　fault　features　of　rolling-element　bearings,　a　composite　fault　diagnosis　method　combining　robust　local　mean　decomposition　(RLMD),　sparrow　search　algorithm　(SSA),　maximum　second-order　cyclostationarity　blind　deconvolution　(CYCBD),　is　proposed.　First,　the　RLMD　is　used　to　decompose　the　product　function　of　the　signal,　and　the　two　indicators,　the　excess　and　the　correlation　coefficient　are　then　used　as　evaluation　criteria　to　select　the　appropriate　components　for　reconstruction.　The　reconstructed　signal　is　then　inputted　into　the　SSA-optimized　CYCBD　algorithm,　by　specifying　the　objective　function　parameter　which　separates　the　faults　and　obtains　multiple　single　fault　signals　with　optimal　noise　reduction.　Finally,　envelope　demodulation　analysis　is　used　for　the　multiple　single　fault　signals,　to　obtain　the　characteristic　frequencies　of　the　corresponding　faults,　so　as　to　complete　the　fault　separation　and　feature　extraction　of　composite　faults.　In　order　to　verify　the　effectiveness　of　the　method,　the　initial　signals　and　the　actual　signals　generated　by　the　computer　shall　be　used.　The　algorithm　is　verified　using　the　XJTU-SY　rolling-element　bearing　dataset,　which　shows　the　good　performance　of　the　method.