As　key　rotational　components　of　wind　turbines,　planetary　gearboxes,　and　bearings　need　special　health　monitoring　and　fault　diagnosis　for　reducing　downtime　and　maintenance　costs.　However,　it　is　still　a　challenging　issue　for　time–frequency　analysis　(TFA)　techniques　to　analyze　nonstationary　and　close-spaced　fault　frequencies　of　wind　turbines.　Hence,　a　high-　concentration　TFA　technique,　termed　frequency-chirprate　synchrosqueezing-based　scaling　chirplet　transform　(FCSSCT)　is　developed.　In　the　FCSSCT,　the　novel　dimension　of　chirprate　is　introduced　to　map　the　signal　into　the　three-dimensional　space　of　time–frequency-chirprate　in　contrast　to　the　time–frequency　domain　in　the　synchrosqueezing　transform　(SST),　herein,　the　three-dimensional　space　of　time,　frequency　and　chirprate　is　calculated　based　on　the　scaling-basis　chirplet　transform　(SBCT);　then,　a　frequency-chirprate　synchrosqueezing　operator　(FCSO)　is　defined　in　the　frequency-chirprate　domain　to　reassign　the　amplitude　coefficients　of　the　SBCT　results　and　a　three-dimensional　representation　of　time–frequency-chirprate　is　obtained;　finally,　the　time–frequency　representation　(TFR)　with　concentrated　energy　is　obtained　by　transforming　the　three-dimensional　representation　of　the　time–frequency-chirprate　space　into　the　time–frequency　domain.　The　effectiveness　of　the　developed　FCSSCT　is　verified　by　the　simulated　multi-component　signals　with　close-spaced　frequencies　or　crossover　frequencies.　Experimental　analysis　results　on　wind　turbine　planetary　gearbox　and　bearing　show　that　the　developed　technique　is　effective　in　characterizing　nonstationary　fault　frequencies.　Rényi　entropies　demonstrate　that　the　FCSSCT　has　much　better　energy　concentration　compared　with　several　advanced　TFA　methods.　©　2024　Elsevier　Ltd