大型回转支承故障信号处理方法综述

Reserch on signal processing method of large slewing bearing

大型回转支承是许多机械设备中重要零件,因此对其进行故障诊断的意义重大。这篇文章针对回转支承的结构特点,总结了现有的信号处理方法。重点介绍了基于知识的故障诊断信号处理方法、基于遗传算法处理方法、全息谱信号分析方法、双树复小波变换方法(DTCWT)及基于多尺度主元分析的聚类经验模式分解法(EEMD-MSPCA),并对这些方法的优缺点进行了分析比较,提出适合大型回转支承信号处理的方法,并对今后大型回转支承信号处理方法的前景做了展望,指出数据融合的方法是今后处理大型回转支承故障信号的主要发展方向。

Large slewing bearing is an important part in a lot of machinery parts ,so it is significantto diagnose fault.According to the structure characteristics of slewing ring, it summarizes the existing signal processing methods ,in which some new methods are introduced,such as fault diagnosis signal processing method based on the knowledge,processing methods based on genetic algorithm,holagraphic spectrum sig- nal anal？sis methods as well as double tree complex wavelet transform method and clustering empirical mode decomposition method based on multi-scale principal component analysis.Then the advantages and disadvantages of these methods are analyzed and compared,and the methods adaptable for dealing with the fault signals of large slewing bearing are proposed.Finally the prospect of signal processing methods that adapt to large slewing bearings is forseen,and Data Fusion is prospected to be the main developing trend.