Time-frequency　(TF)　analysis　(TFA)　is　one　of　the　effective　methods　to　deal　with　non-stationary　signals.　Due　to　their　advantages,　many　experts　and　scholars　have　recently　developed　post-processing　algorithms　based　on　traditional　TFA.　Among　them,　short-time　Fourier　transform　(STFT)　based　post-processing　algorithms　have　developed　the　fastest.　However,　these　methods　rely　heavily　on　the　window　length　selected　in　STFT,　which　has　great　influence　on　the　post-processing　algorithm.　In　this　paper,　a　post-processing　algorithm　for　effectively　processing　pulse　signals　was　proposed　and　called　time-extracting　S-transform　(TEST).　The　time-domain　extraction　method　based　on　S-transform　avoids　the　influence　of　uncertain　parameters.　After　comparing　the　performance　of　various　TFA　methods　when　processing　analog　signals,　the　proposed　TEST　can　clearly　show　the　pulse　occurrence　time　under　the　premise　of　ensuring　high　TF　aggregation.　The　actual　signal　proves　that　the　method　can　be　used　for　fault　diagnosis　of　rolling　bearings.