针对传统包络谱和峭度图分析技术的缺陷，提出了一种基于双树复小波包峭度图的轴承故障诊断方法。该方法综合利用了双树复小波包变换和峭度图分析技术，克服了原峭度图方法只采用FIR和短时傅立叶变换滤波器的缺点，提高了从强噪声环境中提取瞬态冲击特征的能力。首先利用双树复小波包变换，将振动信号分解成不同频带的分量，然后计算各小波分量的谱峭度，再利用谱峭度的滤波器作用，计算最大峭度值对应分量信号的包络谱，根据包络谱就可识别齿轮箱轴承的故障部位和类型。齿轮箱轴承故障振动实验信号的研究结果表明：该方法不仅提高了信噪比和频带选择的正确性，而且能有效地识别轴承的故障。

According to the limitation of traditional envelope spectrum and kurtogram, a novel approach to fault diagnosis of bearing based on dual-tree complex wavelet packet transform and kurtogram is presented. The dual-tree complex wavelet packet transform is substituted for the filter in spectral kurtosis. The shortcomings of traditional kurtogram based on the FIR and short time Fourier transform filters is overcome and its accuracy in detecting transients in a signal from strong background noise is improved. Firstly, the bearing fault vibration signals were decomposed into various frequency band signals. Then the spectral kurtosis was computed and the maximum kurtosis was found. In the end, the filtered signal that maximizes kurtosis and its envelope spectrum were obtained. Therefore, the characteristics of the bearing faults can be recognized according to the envelope spectrum. The experimental results show that not only the frequency band selection accuracy and signal noise ratio are improved, but also the faults of the bearing can be effectively detected.