Effectively　identifying　the　health　status　of　rolling　bearings　can　reduce　the　maintenance　costs　of　rotating　mechanical　components.　With　the　development　and　improvement　of　various　signal　processing　theories,　the　mode　of　extracting　fault　information　from　the　frequency　domain　has　gradually　replaced　the　mode　from　the　time　domain.　As　a　traditional　spectrum　segmentation　analysis　method,　Fast　Kurtogram　can　adaptively　extract　frequency　bands　that　may　contain　fault　information　to　diagnose　faults.　However,　the　frame　of　the　center　frequency　and　bandwidth　obtained　by　the　1/3　binary　tree　filter　bank　segmentation　method　adopted　by　the　Fast　Kurtogram　is　fixed.　This　paper　proposed　a　new　method　of　segmenting　the　spectrum　and　accurately　filtering　fault　information　from　the　frequency　domain　Fast　Entrogram.　The　fluctuation　state　of　the　Fourier　spectrum　is　of　key　importance　in　distinguishing　the　distribution　of　different　components　in　the　signal　at　each　frequency.　After　the　Fourier　transform　of　the　spectrum　is　intercepted　and　reconstructed,　the　minimum　points　of　the　new　sequence　can　separate　different　components　in　the　signal.　Subsequently,　the　frequency　slice　function　is　used　to　extract　each　frequency　band　to　obtain　better　filtering　effects　than　the　finite　impulse　response　filter.　Finally,　the　proposed　novel　correlation　spectral　negentropy　is　sensitive　to　periodic　pulses　and　can　be　used　to　screen　the　component　that　contains　the　most　fault　information.　The　simulation　results　show　that　the　proposed　Fast　Entrogram　can　effectively　extract　periodic　pulses.　It　is　verified　by　experimental　signals　that　the　method　can　be　applied　to　fault　diagnosis　of　bearing　inner　and　outer　rings.　(C)　2020　Elsevier　Ltd.　All　rights　reserved.