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.% 针对滚动轴承故障的振动信号具有非平稳特性，存在强烈噪声干扰，难以提取故障特征频率的情况，提出了基于双树复小波包变换阈值降噪的故障诊断方法。首先将非平稳的故障振动信号进行双树复小波包分解，得到不同频带的分量；然后对每个分量求其峭度值和相关系数并进行比较；最后选取峭度值和相关系数较大的分量进行软阈值降噪和双树复小波包重构，即可有效地消除振动信号中噪声的干扰，同时保留信号中的有效信息即实现了故障特征信息的提取。本文对轴承外圈故障试验和实际工程数据进行了相关分析，并对比传统离散小波包降噪的效果，本文方法处理后的信号冲击周期性更好，较理想地去除了噪声的影响，验证了该方法可以有效地去除噪声并提取滚动轴承故障的特征信息。
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