Workplace　accidents　can　be　avoided　through　effective　rolling　bearing　condition　detection.　From　the　perspective　of　frequency　domain,　various　multi-stage　segmentation　algorithms　have　improved　the　accuracy　of　fault　identification,　but　the　fixed　framework　lacks　adaptability.　This　paper　proposes　a　new　spectrum　segmentation　mode　called　Cpsgram.　The　trend　spectrum　is　created　by　summarizing　the　properties　of　the　periodic　components　in　the　spectrum　using　the　cepstrum,　and　the　segmentation　boundaries　of　various　modal　information　are　determined　based　on　the　minimum　points　contained　therein.　A　filter　group　is　created　based　on　the　frequency　slice　function　to　extract　each　signal　component.　Finally,　the　robust　Modulated　Gini　index　is　used　to　improve　fault　detection.　The　simulation　results　show　that　the　proposed　method　can　effectively　extract　periodic　instantaneous　pulses,　and　the　experimental　results　show　that　this　method　can　be　effectively　applied　to　the　fault　diagnosis　of　the　inner　and　outer　rings　of　rolling　bearings.