Nonstationary　fault　signals　collected　from　wind　turbine　planetary　gearboxes　and　bearings　often　exhibit　close-spaced　instantaneous　frequencies　(IFs),　or　even　crossed　IFs,　bringing　challenges　for　existing　time-frequency　analysis　(TFA)　methods.　To　address　the　issue,　a　data-driven　TFA　technique,　termed　CTNet　is　developed.　The　CTNet　is　a　novel　model　that　combines　a　fully　convolutional　auto-encoder　network　with　the　convolutional　block　attention　module　(CBAM).　In　the　CTNet,　the　encoder　layer　is　first　designed　to　extract　coarse　features　of　the　time-frequency　representation　(TFR)　calculated　by　the　general　linear　Chirplet　transform　(GLCT);　second,　the　decoder　layer　is　combined　to　restore　and　conserve　details　of　the　key　time-frequency　features;　third,　the　skip　connections　are　designed　to　accelerate　training　by　linking　extracted　and　reconstructed　features;　finally,　the　CBAM　is　introduced　to　adaptively　explore　channel　and　spatial　relationships　of　the　TFR,　focusing　more　on　close-spaced　or　crossed　frequency　features,　and　effectively　reconstruct　the　TFR.　The　effectiveness　of　the　CTNet　is　validated　by　numerical　signals　with　close-spaced　or　crossed　IFs,　and　real-world　signals　of　wind　turbine　planetary　gearbox　and　bearings.　Comparison　analysis　with　state-of-the-art　TFA　methods　shows　that　the　CTNet　has　high　time-frequency　resolution　in　characterizing　nonstationary　signals　and　a　much　better　ability　to　detect　wind　turbine　faults.　©　2024　ISA