基于CPWP混合原子分解的滚动轴承故障诊断方法研究

Rolling bearing fault diagnosis based on CPWP merged atomic decomposition

目前常用的各种滚动轴承故障诊断信号处理方法缺乏自适应性,过完备原子分解则具有灵活的自适应能力。将余弦包(CP)及小波包(WP)的快速算法应用于匹配追踪算法设计了CPWP混合原子分解算法,对滚动轴承故障仿真信号进行CP、WP以及CPWP原子分解并比较三种分解结果,得出CPWP混合原子分解可以更加清晰全面地反映冲击调幅信号的特征,分辨率高于单一原子库分解。将上述三种方法分别应用于滚动轴承外圈故障实测信号分析,进一步验证了对信号不同特征敏感的异类原子库的结合可提高对信号的自适应识别能力,CPWP混合原子分解得到较CP、WP原子分解更多的冲击调制信息,能够有效提取滚动轴承的故障特征。

A variety of existing signal processing approaches commonly used for rolling bearing fault diagnosis lack adaptability, while the overcomplete atomic decomposition has a flexible adaptive ability. With application of CP(Cosine packet) and WP(Wavelet Packet) fast algorithms in matching pursuit, the algorithm of CPWP merged atomic decomposition was proposed here. The simulated signal of a rolling bearing fault was analyzed by using CP, WP and CPWP merged atomic decomposition, respectively. The results showed that CPWP merged atomic decomposition has a higher revolution factor and provides clearer and more comprehensive characters of an amplitude-modulated signal of impact than CP or WP atomic decomposition do. Moreover, the real signal of outer race fault of rolling bearing was analyzed by using the mentioned above techniques. It was validated that the combination of different atomic dictionaries is sensitive to different features of a signal, it enhances the ability of adaptive signal recognition; the CPWP merged dictionary analysis can obtain more information of impact and modulation than either CP or WP dictionary decomposition can and has better performances in extracting rolling bearing fault features.