一种簇绒机故障诊断方法的研究（英文）  

Research on a method of tufting machine fault diagnosis

作　　者：高翔[1] 戴惠良[1] 王弘鹏[1] 

机构地区：[1]东华大学机械工程学院,上海201620

出　　处：《机床与液压》2017年第6期31-36,61,共7页Machine Tool & Hydraulics

摘　　要：针对簇绒机的故障诊断问题,提出采集滚动轴承的振动信号进行故障诊断。采集的实际振动信号中往往存在噪声信号,需要去掉噪声后再进行诊断。局部均值分解(local mean decomposition,LMD)方法是一种新型的信号自适应分解的时频分析方法,并且已经应用到故障诊断中。为了进一步提高LMD的性能,提出采用分段Hermite插值替代原始的滑动平均方法。提出一种新的故障诊断方法,首先应用小波包变换分析方法,去除信号中夹杂的噪声,然后使用改进后的LMD方法进行信号的分解,选择相关系数较大的PF分量进行希尔伯特变化包络谱分析,成功提取相关的故障特征。通过仿真实例的分析和对滚动轴承的实际故障数据进行故障诊断,证明了该方法在故障诊断应用中的有效性。It is an effective fault diagnosis method to collect vibration signals from fault rolling bearing of tufting machine. In actual engineering applications, the collected vibration signals usually contain a lot of noise signals which require signal de-noising. Local mean decomposition （LMD） method is a new adaptive time-frequency a- nalysis method and it has been successfully applied in fault diagnosis. In order to improve the performance of LMD, the cubic Hermite interpolation approach is applied to construct the local mean function and envelope esti- mation function instead of moving average algorithm. A new method of fault diagnosis was proposed, first of all, wavelet packet de-noising process was applied to de-nosing, and then the improved-LMD method was applied to decompose signals into several product functions （PF）. Finally, the most effective PF was selected to conduct the Hilbert envelope spectrum analysis and the fault features were extracted. The simulation test and the actual bearing fauh diagnosis test showed that the proposed method is effective. rolling

关 键 词：滚动轴承 故障诊断 小波包降噪 局部均值分解(LMD) 

分 类 号：TH17[机械工程—机械制造及自动化]