The　vibration　signals　produced　by　rotating　machinery　are　mostly　non-stationary,　and　there　are　numerous　methods　for　dealing　with　them.　People＇s　expectations　for　time-frequency　analysis　(TFA)　results　are　increasing　all　the　time.　The　emergence　of　post-processing　algorithms　based　on　the　short-time　Fourier　transform　(STFT)　provides　scholars　with　new　ideas,　but　such　algorithms　heavily　rely　on　the　window　length　selected　by　STFT　and　have　significant　uncertainty.　To　address　this　issue,　we　propose　the　generalized　S-synchroextracting　transform,　a　new　time-frequency　post-processing　algorithm　(GS-SET).　The　algorithm　extracts　the　coefficients　on　the　TF　ridge　of　the　generalized　S-transform　(GST)　to　remove　the　majority　of　the　dispersed　TF　energy,　allowing　the　time-frequency　representation　(TFR)　to　achieve　optimal　TF　resolution.　The　results　of　the　analog　signal　processing　show　that　the　method　can　characterize　the　signal　clearly　and　accurately,　and　it　has　good　noise　robustness.　To　process　the　fault　signals　of　the　three　groups　of　rolling　bearings,　we　use　different　TFA　methods.　The　results　show　that　the　method　can　more　precisely　determine　the　characteristic　frequency　of　the　faulty　bearing.　Finally,　the　superiority　of　this　method　is　demonstrated　further　by　processing　data　from　Case　Western　Reserve　University＇s　faulty　bearing　database.