基于改进双树复小波变换的轴承多故障诊断

针对双树复小波变换产生频率混叠的缺陷,提出了改进双树复小波变换的轴承多故障诊断方法,该方法综合利用了双树复小波包变换和经验模态分解技术。首先,利用双树复小波变换将振动信号分解成不同频带的分量;然后,将各小波分量进行经验模态分解,获得各小波分量的主频率分量信号;最后,计算各小波分量的主频率分量信号的包络谱,根据包络谱识别齿轮箱轴承的故障部位和类型。通过仿真信号和齿轮箱轴承多故障振动实验信号的研究结果表明,该方法不仅消除了频率混叠现象,提高了信噪比和频带选择的正确性,而且提高了从强噪声环境中提取瞬态冲击特征的能力,能有效识别轴承的故障类型。

Bearing Multi-faults Diagnosis Based on Improved Dual-Tree Complex Wavelet Transform

According to the shortcomings of traditional dual-tree complex wavelet transform which has spectral aliasing, a novel approach to multi-faults diagnos is of bearing based on improved dual-tree complex wavelet transform is presented. Firstly, the bearing fault vibration signals are decomposed into various frequency band signals. Then the band signals are decomposed using empirical mode decomposition technique, and the maximum energy component is found. In the end, the envelope spectrum of maximum energy components is computed. Therefore, the characteristics of the bearing faults can be recognized according to the envelope spectrum. The simulative and experimental results show that not only the spectral aliasing is eliminated, but also the frequency band selection accuracy and signal noise ratio are improved, the multi-faults of the bearing can be effectively detected.