Blind　deconvolution　can　remove　the　effects　of　complex　paths　and　extraneous　disturbances,　thus　recovering　simple　features　of　the　original　fault　source,　and　is　used　extensively　in　the　field　of　fault　diagnosis.　However,　it　can　only　identify　and　extract　the　statistical　mean　of　the　fault　impact　features　in　a　single　domain　and　is　unable　to　simultaneously　highlight　the　local　features　of　the　signal　in　the　time-frequency　domain.　Therefore,　the　extraction　effect　of　weak　fault　signals　is　generally　not　ideal.　In　this　paper,　a　new　time-frequency　slice　extraction　method　is　proposed.　The　method　first　computes　a　high　temporal　resolution　spectrum　of　the　signal　by　short-time　Fourier　transform　to　obtain　multiple　frequency　slices　with　distinct　temporal　waveforms.　Subsequently,　the　constructed　harmonic　spectral　feature　index　is　used　to　quantify　and　target　the　intensity　of　feature　information　in　each　frequency　slice　and　enhance　their　fault　characteristics　using　maximum　correlation　kurtosis　deconvolution.　Enhancing　the　local　features　of　selected　frequency　slice　clusters　can　reduce　noise　interference　and　obtain　signal　components　with　more　obvious　fault　signatures.　Finally,　the　validity　of　the　method　was　confirmed　by　a　simulated　signal　and　fault　diagnosis　of　the　rolling　bearing　outer　and　inner　rings　was　accomplished　sequentially.　Compared　with　other　common　deconvolution　methods,　the　proposed　method　obtains　more　accurate　and　effective　results　in　identifying　fault　messages.　©　2024　ISA