Bearings　are　crucial　in　electromechanical　equipment,　and　the　state　significantly　affects　operational　efficiency　and　industrial　income.　Therefore,　efficient　and　accurate　monitoring　of　bearing　health　and　fault　diagnosis　are　imperative　for　intelligent　equipment　operation　and　maintenance.　The　Kurtogram　is　widely　used　for　bearing　fault　diagnosis　as　it　adeptly　extracts　faulty　information　from　the　frequency　domain.　However,　this　approach　imposes　limitations　on　the　center　frequency　and　bandwidth,　preventing　the　association　of　specific　frequency　groups　with　the　spectrum.　Furthermore,　the　Kurtogram　exhibits　poor　robustness　and　cannot　support　its　use　with　new　equipment　or　under　extreme　working　conditions.　To　address　these　shortcomings,　this　paper　introduces　a　novel　technique　known　as　Cepsogram.　First,　a　new　cepstrum　reconstruction　spectral　trend　estimation　method　is　designed　to　distinguish　different　modal　information　in　the　frequency　domain.　In　this　method,　the　number　of　reconstruction　iterations　is　increased　to　improves　the　complexity　of　spectrum　trends　and　expand　the　diversity　of　modular　segmentation.　Additionally,　to　capture　the　cyclic　transient　characteristics　of　the　signal,　spectral　negentropy　is　employed　to　reduce　interference　and　obtain　more　obvious　fault　characteristics　compared　to　steepness.　The　use　of　engineering　data　is　evaluated　to　substantiate　the　effectiveness　and　stability　of　this　proposed　method.　Authors