噪声统计特性LMD滚动轴承故障诊断

发布时间：2018-06-17 02:01

本文选题：局部均值分解 + 噪声统计特性　；参考：《中国测试》2016年06期

【摘要】：工程实际中测得的滚动轴承信号往往含有大量的噪声,这使得轴承故障特征淹没在噪声中难以被提取。针对这一问题,提出一种基于随机噪声统计特性与局部均值分解(local mean decomposition,LMD)理论相结合的滚动轴承故障诊断方法。首先,利用LMD将原信号分解,得到若干乘积函数(production function,PF)分量;其次,将第一阶PF分量随机排序,与剩余PF分量相加;然后,对第2步进行P次循环,求平均;最后,把第3步得到的信号作为原信号,重复第1、2步Q次,对得到的信号进行频谱分析,提取故障特征。通过对仿真信号和实验台轴承实验信号进行分析研究表明,该方法可准确诊断滚动轴承元件故障,具有有效性。
[Abstract]:In engineering practice, the rolling bearing signals often contain a lot of noise, which makes it difficult to extract the bearing fault characteristics in the noise. In order to solve this problem, a rolling bearing fault diagnosis method based on the statistical characteristics of random noise and the local mean decomposition (LMD) theory is proposed. First, the original signal is decomposed by LMD, and some product functions are obtained. Secondly, the first order PF component is sorted randomly with the remaining PF component. Then, the second step is cycled P to get the average. The signal obtained in step 3 is taken as the original signal and the second step Q is repeated. The frequency spectrum of the obtained signal is analyzed and the fault feature is extracted. Through the analysis of the simulation signal and the experimental signal of the bearing, it is shown that the method can accurately diagnose the fault of the rolling bearing element, and it is effective.
【作者单位】：内蒙古科技大学机械工程学院;
【基金】：国家自然科学基金项目(21366017) 内蒙古科技厅应用与研究开发计划项目——高新技术领域科技计划重大项目(20130302)
【分类号】：TH133.33

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