The　fault　diagnosis　of　wind　turbines　is　crucial　for　wind　power　generation.　However,　the　violent　variation　in　wind　power　and　directions　of　offshore　wind　turbines　often　results　in　high　nonstationary　vibration　signals,　which　poses　a　challenge　for　effective　fault　recognition.　Time-frequency　analysis　(TFA)　is　a　common　method　to　reveal　the　timevarying　frequency　components　caused　by　faults.　In　this　paper,　we　propose　a　component　separable　synchroextracting　transform　(CSSET)　by　exploring　the　unique　modulation　characteristics　of　wind　turbine　vibration　signals.　For　the　method,　a　fundamental　frequency　is　firstly　estimated　by　a　time-frequency　ridge　optimization　method,　and　the　potential　fault　characteristic　frequency　(FCF)　set　is　constructed　according　to　the　modulation　characteristics　of　the　wind　turbine　vibration　signal.　Next,　the　instantaneous　amplitudes　(IAs)　of　these　components　in　the　set　are　estimated　by　a　time-varying　bandpass　filter.　Then,　the　instantaneous　frequency　(IF)　of　the　reconstructed　single　component　is　extracted　by　the　synchronous　extraction　operator　(SEO),　and　a　high-precision　time-frequency　representation　(TFR)　is　obtained.　The　proposed　method　takes　full　advantage　of　the　physical　modulation　characteristics　of　vibration　signals　and　the　frequency　components　which　are　most　relevant　to　fault　information　are　only　preserved.　The　effectiveness　of　the　proposed　method　is　verified　by　analyzing　simulation　and　experimental　signals.