As　gear　fault　vibration　signal　is　always　nonlinear　and　nonstationary　and　with　a　strong　background　noise　which　result　difficulty　in　fault　feature　extraction,　a　new　method　based　on　Dual-tree　Complex　Wavelet　Transform　(DT-CWT)　and　local　projective　method　is　proposed.　As　an　improved　method　of　the　conventional　discrete　wavelet　transform　(DWT),　DT-CWT　has　many　advantages　over　DWT,　such　as　the　improvement　of　frequency　aliasing　and　oscillations　of　wavelet　coefficients　which　is　the　key　for　the　proposed　method.　Local　projective　algorithm　for　nonlinear　time　series　has　a　good　ability　of　signal　period　strengthen　and　noise　suppression,　which　fits　for　wavelet　coefficients　denoising.　Firstly,　the　fault　signal　is　decomposed　by　dual-tree　complex　wavelet　transform　to　obtain　the　coefficients　of　different　layers.　Secondly,　the　local　projective　algorithm　is　employed　to　strengthen　the　periodicity　of　the　coefficient　whose　amplitude　shows　stronger　periodicity,　and　then　do　soft-threshold　denoising.　Finally,　the　fault　characteristic　signal　can　be　obtained　by　coefficient　reconstruction.　The　fault　frequency　can　be　located　accurately　by　envelope　spectrum　analysis.　Simulation　and　engineering　applications　showed　the　effectiveness　of　the　method　in　incipient　gear　fault　diagnosis.