Under　actual　working　conditions,　vibration　signals　of　wind　turbines　often　have　non-proportional　frequency　components,　especially　cross-frequencies,　which　presents　additional　challenges　in　fault　diagnosis　of　wind　turbines.　Therefore,　the　high-dimensional　extracting　chirplet　transform　(HDECT)　is　proposed　to　solve　the　above　problems.　In　the　HDECT,　a　new　dimension　is　first　defined　based　on　time-varying　window　length　and　chirprate,　and　then　the　local　maximum　extracting　operator　(LMEO)　is　constructed　based　on　time,　frequency,　and　new　dimension　to　reassign　the　energy　of　the　velocity　synchronous　linear　chirplet　transform　(VSLCT).　Specifically,　using　the　LMEO,　only　the　time-frequency　energy　at　the　position　of　the　instantaneous　frequency　(IF)　ridge　is　retained,　effectively　eliminating　interference　from　other　time-frequency　energy　sources,　and　the　algorithm’s　ability　to　resist　noise　is　improved.　The　HDECT　is　verified　by　analyzing　the　simulation　signal,　the　vibration　signal　of　the　wind　turbine　planetary　gearbox,　the　bearing　signal,　and　the　sound　signal　of　killer　whales.　The　analysis　results　show　that　the　HDECT　can　accurately　characterize　signals　with　non-proportional　fundamental　frequencies,　especially　crossed　IFs,　so　it　can　accurately　identify　wind　turbine　faults.　IEEE