Blind　deconvolution　can　effectively　highlight　the　fault　impulses　submerged　in　vibration　signals.　However,　the　objective　function　of　existing　deconvolution　methods　deeply　depends　on　prior　knowledge　about　fault　periods,　which　is　often　difficult　to　acquire　in　advance　or　may　lack　accuracy　in　practical　applications.　Additionally,　its　filter　length　selection　remains　an　open　problem,　hindering　the　performance　and　generalization　in　industrial　scenarios.　To　address　the　above　issues,　a　maximum　cyclostationary　characteristic　energy　index　deconvolution　(MCCEID)　is　proposed　to　recover　periodic　impulses,　where　a　novel　cyclostationary　characteristic　energy　index　(CCEI)　is　established　as　the　objective　function.　The　CCEI　captures　local　variation　features　at　the　fault　characteristic　frequency　(FCF)　to　iteratively　enhance　periodic　components,　instead　of　focusing　on　aperiodic　noise.　Meanwhile,　a　periodic　hierarchical　assessment　method　is　developed　to　sequentially　identify　resonance　frequency　slice　and　FCF,　in　which　the　interference　from　other　frequencies　is　excluded　and　only　the　resonance　frequency　slice　is　applied　to　estimate　FCF.　In　addition,　an　adaptive　filter　length　determination　framework　is　designed　considering　both　the　value　of　CCEI　and　time　cost,　thereby　avoiding　the　manual　determination　of　the　filter　length.　The　performance　of　MCCEID　is　demonstrated　by　simulated　and　experimental　signals,　and　the　results　illustrate　that　MCCEID　outperforms　the　other　methods　in　fault　feature　extraction.　©　2025