强噪声背景下滚动轴承微弱故障特征信号的经验模态分解  

Empirical mode decomposition of weak fault characteristic signal of rolling bearing under strong noise background

作　　者：杨建华[1,2] 韩帅[1,3] 张帅 刘后广[1] 唐超权[1] YANG Jian-hua;HAN Shuai;ZHANG Shuai;LIU Hou-guang;TANG Chao-quan(School of Mechatronic Engineering,China University of Mining and Technology,Xuzhou 221116,China;Jiangsu Key Laboratory of Mine Mechanical and Electrical Equipment,China University of Mining and Technology,Xuzhou 221116,China;Jinxi Industries Group Co.Ltd.,Taiyuan 030024,China)

机构地区：[1]中国矿业大学机电工程学院,江苏徐州221116 [2]中国矿业大学江苏省矿山机电装备重点实验室,江苏徐州221116 [3]晋西工业集团有限责任公司,山西太原030024

出　　处：《振动工程学报》2020年第3期582-589,共8页Journal of Vibration Engineering

基　　金：国家自然科学基金资助项目(11672325,61603394);江苏省优势学科项目。

摘　　要：针对强噪声背景下滚动轴承早期微弱故障信号经验模态分解问题,提出了一种基于级联自适应分段线性随机共振系统降噪的经验模态分解方法。该方法依赖于级联自适应分段线性随机共振系统优良的降噪特性,首先对含噪信号进行降噪处理,然后再进行经验模态分解。通过对轴承故障仿真信号和滚动轴承实验信号的分析,结果表明该方法能有效滤除高频噪声,减少经验模态分解阶数,提高经验模态分解的质量,实现强噪声背景下滚动轴承早期微弱故障特征提取。Aiming at the problem of empirical mode decomposition(EMD)of early weak fault signals of rolling bearing under strong noise background,an EMD method based on cascaded adaptive piecewise linear stochastic resonance system is proposed.The method depends on the excellent noise reduction characteristics of cascaded adaptive piecewise linear stochastic resonance system.Firstly,the noisy signal is denoised,and then EMD is carried out.Through the analysis of the simulation signal of bearing fault and the experimental signal of rolling bearing,the results show that the method can effectively filter out the highfrequency noise,reduce the orders of EMD,improve the quality of EMD,and achieve the extraction of early weak fault feature of rolling bearing under strong noise background.

关 键 词：故障诊断 滚动轴承 经验模态分解 级联分段线性系统 自适应随机共振 

分 类 号：TH165.3[机械工程—机械制造及自动化] TH133.3