The　methods　of　mode　decomposition　based　on　the　amplitude　of　spectrum　are　widely　used　in　the　fault　diagnosis　of　bearings　and　gears　in　rotating　machinery　such　as　centrifuges,　slurry　pumps,　fans,　etc.　Analytical　mode　decomposition　with　excellent　filtering　could　set　the　bisecting　frequency　to　distinguish　useful　modes.　However,　the　vibration　signal　of　equipment　in　actual　operation　is　complex.　This　paper　is　devoted　to　the　research　of　a　novel　spectral　segmentation　mode　decomposition　method.　The　proposed　enhanced　analytical　mode　decomposition　takes　advantage　of　the　trend　of　spectrum　fluctuations.　In　order　to　obtain　the　most　critical　bisecting　frequency,　the　trend　spectrum　estimation　method　was　proposed　based　on　order　statistics　filter.　The　filtering　effect　of　enhanced　analytical　mode　decomposition　was　verified　by　the　simulated　signal.　The　experimental　results　show　that　the　proposed　method　is　efficient　and　the　bearing　inner　and　outer　ring　faults　in　the　rotating　machine　can　be　successfully　extracted.　©　2020　Elsevier　Ltd