V ol 36No.5
Oct.2016
噪
声与振动控制NOISE AND VIBRATION CONTROL 第36卷第5期2016年10月
文章编号：1006-1355(2016)05-0144-06
基于机匣振动信号的滚动轴承故障特征提取
韩
特，蒋东翔，付道鹏
（电力系统及发电设备控制与仿真国家重点实验室，清华大学热能工程系，北京100084）
摘要：通过进行带机匣测点的滚动轴承故障模拟实验，获取滚动轴承在故障状态条件下，轴承座测点和机匣测点的振动数据。分析结果显示，相对于轴承座，机匣上的振动信号成分复杂，轴承故障特征不明显，直接进行包络解调无法提取故障特征。通过奇异值分解(singular value decomposition ，SVD)，差分谱中各峰值处奇异值可以表征不同成分的信号。当轴承故障信号微弱时，第一个峰值处的奇异值重构信号往往代表转频及其调制信号分量，选取该靠后峰值处的奇异值进行信号重构可以有效提取轴承故障特征信号。研究内容为实际基于机匣测点信号的航空发动机滚动轴承故障特征提取提供了一种新的方法。
关键词：振动与波；滚动轴承；机匣测点；故障特征提取；奇异值分解；差分谱中图分类号：TP206+.3
文献标识码：A
DOI 编码：10.3969/j.issn.1006-1335.2016.05.030
Fault Feature Extraction of Rolling Bearings
Based on Casing Vibration Signals
HAN Te ,JIANG Dong-xiang ,FU Dao-peng
(State Key Lab of Control and Simulation of Power Systems and Generation Equipment,Department of Thermal Engineering,Tsinghua University,Beijing 100084,China )
Abstract :Fault feature extraction of rolling bearings based on casing vibration signal is studied.The experiment for the fault simulation of the rolling bearings is done and the vibration signals of the bearing base and casing are acquired.Analysis results show that,compared to the bearing base,the vibration signal of the casing is complex and the fault feature of the bearing is not obvious.The envelope demodulation method cannot extract the fault characteristics directly.Therefore,the singular value decomposition (SVD)is employed to process the vibration signal.It is found that the singular values at different peaks in the difference spectrum can represent the signals of different components.The singular value reconstruction signal at the first peak always represents the components of the rotating frequency and modulation signals when the fault signals of the bearing are weak.The fault modulation signals can be effectively extracted by selecting the singular values after the first peak in the difference spectrum.This study provides a new method for the fault feature extraction of rolling bearings based on the vibration signals of casing.
Key words :vibration and wave;rolling bearing;casing measurement point;fault feature extraction;singular value decomposition (SVD);difference spectrum
滚动轴承发生故障时，由故障引起的脉冲冲击信号往往激发系统的高频固有振动，振动信号往往表现出频率调制的特点[1]。传统的共振解调技术是
收稿日期：2016-01-17
基金项目：国家自然科学基金资助项目（11572167）作者简介：韩特（1993－），男，合肥市人，直博研究生，主要研
究方向为燃气轮机、航空发动机故障诊断与结构健康监测，信号处理与数据挖掘。
通讯作者：蒋东翔，男，博士生导师。
E-mail:jiangdx@https://www.doczj.com/doc/f914144575.html,
处理滚动轴承故障的一种极为有效的分析方法。通过找出共振频带进行滤波，对滤波信号进行包络解调得到低频的故障特征频率。但滤波过程中的共振频带如何选择往往依靠工程经验[2–3]。小波变换，小波包分解等方法可以对信号进行分解降噪，从而提取包含轴承故障信息分量。但依然存在小波基选取、阈值设定等问题。经验模式分解(empirical mode decomposition,EMD)通过将信号分解为一些基本模式分量(IMF)之和，再从各基本模式分量中提取故障特征。当信号中含有大量噪声时，诊断效果
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