Denoising method based on dual-tree complex wavelet transform and MCA and its application in gear fault diagnosis

The vibration signals of gearbox incipient failure often contain strong noise, which results difficulty in fault feature extraction by the conventional denoising method, such as threshold based method.Thus, a new method based on dual-tree complex wavelet transform (DT-CWT) and morphological component analysis (MCA) was proposed.In the processing, the signal was firstly processed by DT-CWT to gain the coefficients of different layers.Secondly, MCA was employed to denoise the coefficient which was more periodic.Then, the denoised signal with weak fault feature could be gotten from a following single reconstruction.Finally, the fault characteristic frequency could be located accurately by simple envelope spectrum analysis.A simulate signal and incipient failure vibration signal of mill gearbox were processed using this method, and the results show that the method can remove the strong background noise in the signal effectively, and has better effect than single MCA and soft threshold method, and get a more clear fault characteristic frequency, thereby providing a new method for gearbox incipient fault diagnosis.