误差影响下滚动轴承多重故障模态特征信号的盲源分离方法

doi:10.3969/j.issn.1000-1093.2018.07.021

兵工学报 ›› 2018, Vol. 39 ›› Issue (7): 1419-1428.doi: 10.3969/j.issn.1000-1093.2018.07.021

误差影响下滚动轴承多重故障模态特征信号的盲源分离方法

黄大荣^1,2, 陈长沙^1,2，柯兰艳¹，赵玲¹，米波¹，孙国玺²

(1.重庆交通大学信息科学与工程学院, 重庆 400074； 2.广东石油化工学院广东省石化装备故障诊断重点实验室，广东茂名 525000)

收稿日期:2017-11-21 修回日期:2017-11-21 上线日期:2018-08-24
作者简介:黄大荣（1978—），男，教授，硕士生导师。E-mail: hcx1978@163.com
基金资助:
国家自然科学基金项目（61703063、61663008、61573076、61473094、61304104、61004118）；教育部留学归国人员科研启动基金项目（2015-49)；重庆市高等学校优秀人才支持计划项目（2014-18）；重庆交通大学研究生教育创新基金项目（2017S0105）；广东省石化装备故障诊断重点实验室开放式基金项目（GDUPKLAB201501、GDUPKLAB201604）；广东省普通高校特色创新项目（201463104）；重庆市研究生教育教学改革研究重点项目（yjg152011）；重庆市自然科学基金项目（CSTC2015jcyjA0540、CSTC2017jcyjA1665）；重庆市教育委员会科学技术研究项目（KJ1705139、KJ1600518）

A Blind Source Separation Method for Multi-fault Modal Characteristic Signals of Rolling Bearings with Error Influences

HUANG Da-rong^1,2, CHEN Chang-sha^1,2, KE Lan-yan¹, ZHAO Ling¹, MI Bo¹, SUN Guo-xi²

(1.School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, China; 2.Guangdong Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis, Guangdong University of Petrochemical Technology,Maoming 525000, Guangdong,China)

Received:2017-11-21 Revised:2017-11-21 Online:2018-08-24

摘要/Abstract

摘要： 针对旋转机械设备受到测量误差及系统误差等因素影响，导致滚动轴承多故障模态信号难以分离的缺陷，提出一种误差影响下滚动轴承多重故障模态特征信号的盲源分离方法。对故障信号进行白化预处理得到白化矩阵，进而计算白化矩阵的4阶累积量，并构建4阶累积量矩阵；将累积量矩阵对角化，并取前K个较大特征值对应的特征向量作为新累积量矩阵；利用总体最小二乘方法估计最小化新累积量矩阵与目标正交矩阵的误差函数，最大程度地联合近似对角化新累积量矩阵，实现多故障信号的分离估计；为进一步评估该方法的有效性，选用时域相关系数及时频域双谱估计两种评价方法对分离结果进行验证。结果表明，该方法分离出来的信号与源信号相关系数高，并且时频域双谱估计相似，是一种有效分离多重故障的方法。

关键词: 滚动轴承, 多重故障模态, 特征信号, 盲源分离, 联合对角化, 总体最小二乘, 误差影响

Abstract: The multi fault modal signals of rolling bearing are difficult to be separated due to the measurement errors and system errors. For this reason, a blind source separation method for multi-fault modal characteristic signals of rolling bearings under the influence of errors is proposed. A whitening matrix is obtained by preprocessing the fault signal, and then the fourth-order cumulant of the whitening matrix is calculated, and a fourth-order cumulant matrix is established. The eigenvectors corresponding to the larger K eigenvalues are taken as the new cumulant matrix by diagonalizing the cumulant matrix. The total least squares method is used to minimize the error function between the cumulant matrix and the target orthogonal matrix, thus estimating the fault source signal. Furtherly, the time domain correlation coefficient and the time-frequency domain bispectrum estimation are introduced to verify the feasibility and effectiveness of the proposed method. The results show that the signal derived from the proposed method has high correlation coefficient with the source signal, and the time-frequency domain bispectrum estimation is similar, so it is an effective method to separate multiple faults. Key

Key words: rollingbearing, multi-faultmodal, characteristicsignal, blindsourceseparation, jointapproximatediagonalization, totalleastsquares, errorinfluence

中图分类号:

TH133.33⁺1

黄大荣, 陈长沙，柯兰艳，赵玲，米波，孙国玺. 误差影响下滚动轴承多重故障模态特征信号的盲源分离方法[J]. 兵工学报, 2018, 39(7): 1419-1428.

HUANG Da-rong, CHEN Chang-sha, KE Lan-yan, ZHAO Ling, MI Bo, SUN Guo-xi. A Blind Source Separation Method for Multi-fault Modal Characteristic Signals of Rolling Bearings with Error Influences[J]. Acta Armamentarii, 2018, 39(7): 1419-1428.

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误差影响下滚动轴承多重故障模态特征信号的盲源分离方法

A Blind Source Separation Method for Multi-fault Modal Characteristic Signals of Rolling Bearings with Error Influences

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