Fault diagnosis of rolling bearing based on dual-treecomplex wavelet packet transform

Abstract: The operation states of rolling bearings which are the most common and important parts in the mechanical equipment, will affect the whole machine operation condition directly. Due to the working environment of rolling, bearing is complicated, the fault vibration signal of rolling bearing is usually non-stationary, and the strong noise interference is contained in the vibration signal at the same time. So it is important to eliminate the noise interference and extract fault feature information effectively for the rolling bearing. Dual-tree complex wavelet packet transform is a new method of signal processing. Dual-tree complex wavelet packet transform has many good characteristics, for example, approximate shift invariance, good directional selectivity、perfect reconstruction, limited data redundancy, efficient computational efficiency and so on. The high frequency part of dual-tree complex wavelet transform that is not decomposed, is further decomposed by dual-tree complex wavelet packet transform, so as to improve the whole frequency band signal frequency resolution and reduce the loss of information. In view of the above situation, a new fault diagnosis method is proposed based on dual-tree complex wavelet packet transform and threshold de-noising. Firstly, the non-stationary fault signal is decomposed into several different frequency band components through dual-tree complex wavelet packet decomposition. Secondly, Kurtosis and the cross-correlation coefficient of each component are obtained and compared. Due to the kurtosis reflecting the signal variations, if the kurtosis value is bigger, the degree of the change of signal is bigger too. The correlation coefficient can reflect the proximity between the component and the original signal at the same time, the correlation coefficient is bigger, the more similar with the original signal. Finally, the components that have a bigger value are chosen to be de-noised by a soft threshold and reconstructed by dual-tree complex wavelet packet transform. The noise interference was eliminated effectively, and the effective signal information was retained at the same time. Thus, the fault feature information was extracted. In this paper, the outer bearing fault test and the engineering practical fault data of rolling bearing were analyzed by dual-tree complex wavelet packet transform and threshold de-noising. In order to contrast analysis, the vibration signals were processed by the traditional discrete wavelet packet transform and threshold de-noising. The signal has the better periodic impact obtained by the method proposed in this paper, and the noise was eliminated ideally. So the fault diagnosis method based on dual-tree complex wavelet packet transform and threshold de-noising can effectively eliminate the noise in the vibration signals. Thus, the fault feature information was extracted and the feasibility and effectiveness of this method is verified.