Time–frequency　analysis　methods　can　be　used　to　characterize　the　time-varying　characteristics　of　a　signal.　The　postprocessing　algorithm　further　enhances　this　ability.　The　synchroextracting　transform　is　a　typical　postprocessing　algorithm　that　has　the　advantage　of　energy　aggregation.　However,　based　on　a　short-time　Fourier　transform,　shortcomings　such　as　a　fixed　window　length　and　amplitude　distortion　when　processing　frequency　modulation　signals　are　unavoidable.　This　paper　proposes　a　time–frequency　postprocessing　algorithm　with　high　adaptability,　which　is　called　the　adaptive　synchroextracting　transform　(ASET).　The　filter　window　width　for　the　ASET　is　adaptive　and　is　determined　by　the　instantaneous　frequency　change　rate　for　the　signal.　On　this　basis,　the　improved　extraction　operator　can　be　used　to　achieve　a　high-resolution　time–frequency​　representation.　This　algorithm　can　be　used　to　better　deal　with　strong　frequency　modulation　signals　and　has　better　noise　robustness　while　allowing　for　signal　reconstruction.　The　effectiveness　and　practicability　of　the　proposed　algorithm　are　demonstrated　by　simulation　signals　and　faulty　bearing　signals.　©　2023　ISA