基于JADE-ICA的滚动轴承多故障信号盲源分离

摘要
图/表
参考文献(13)
相关文章 (15)

全文: PDF (1028 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要本文研究一种基于源信号高阶统计信息的矩阵联合近似对角化独立元分析(JADE-ICA)方法，并将其应用于滚动轴承故障声发射(AE)信号的盲源分离。滚动轴承的声发射源信号一般具有衰减性和准周期性，多组信号间还具有时差性，信号被多个传感器接收。通过最大程度的联合近似对角化，可以使源信号与分离信号有效的一一对应，克服非线性和时差的影响；通过高阶统计的高斯噪声不敏感性可以有效抑制随机观测噪声对分离结果的影响。选用相关系数、二次残差、性能指数和频谱特征构成系列时频域评价指标对分离结果进行较为全面的验证。仿真结果证明了本文方法的可行性和有效性。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章

关键词 ： JADE, 滚动轴承, 故障诊断, 声发射

Abstract：Here,a joint approximate diagonalization of eigen-matrix and independent component analysis (JADE-ICA) method based on the high-order statistics of source signals was studied.It was applied to the blind source separation of rolling bearing faults’ acoustic emission (AE) signals.The multi-AE source signals of rolling bearings were collected with multi-sensor.It was shown that the bolling bearing multi-AE source signals have characteristics,such as,time difference among multi-signals,with decay and quasi periodicity.Through the maximum joint approximate diagonalization,the one-to-one match between source signals and separated signals was realized to overcome the influence of nonlinearity and time difference.With the insensitivity of high-order statistics to Gaussian noise,the effects of random measured noise on the separated results were effectively suppressed.Correlation coefficient,quadratic residual,performance index and spectral characteristics were chosen to form a set of time-frequency domain evaluation indexes to verify the separated results.The simulation results verified the feasibility and effectiveness of the proposed method.

Key words： JADE rolling bearing fault diagnosis acoustic emission

收稿日期: 2015-07-14 出版日期: 2017-02-25

引用本文:

席剑辉崔健驰蒋丽英. 基于JADE-ICA的滚动轴承多故障信号盲源分离[J]. 振动与冲击, 2017, 36(5): 231-237.
XI Jianhui,CUI Jianchi,JIANG Liying. JADE-ICA-based blind source separation of multi-fault signals of rolling bearings. JOURNAL OF VIBRATION AND SHOCK, 2017, 36(5): 231-237.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2017/V36/I5/231

[1] 郝如江，卢文秀，褚福磊. 声发射检测技术用于滚动轴承故障诊断的研究综述[J]. 振动与冲击，2008，27(3):75-79.
    HAO Ru-jiang,LU Wen-xiu,CHU Fu-lei, Review of diagnosis of rolling element bearings defaults by means of acoustic emission technique[J].Journal of Vibration and Shock, 2008,27(3) :75-79.
[2] Tandon N, Choudhury A. A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings. Tribology International, 1999, 32: 469-480.
[3] 何沿江，齐明侠，罗红梅.基于ICA和SVM的滚动轴承  声发射故障诊断技术[J].振动与冲击，2008，27(3)：150-153.
    HE Yan-jiang,QI Ming-xia,LUO Hong-mei, AE based fault diagnosis of rolling bearings by use of ICA and SVM [J].Journal of Vibration and Shock, 2008,27(3) : 150-153.
[4] 杨黎明. 声发射技术用于段修货车轴承故障诊断研究[ D]. 成都：西南交通大学车辆工程系，2003，11.
    YANG Li-ming, Acoustic emission technique research on fault diagnosis to freight train’s bearing in depot[D]. ChengDu: Southwest Jiaotong University,Master Degree Thesis,2003,11.
[5] 范虹，孟庆丰，张优云，等. 基于滤波器组和高阶累积量的信号特征检测.振动与冲击，2007，26(2)：29-32，173.
    FAN Hong,MENG Qing-feng,ZHANG You-yun, etc. Signal feature detection based on filter bank and higher order cumulants [J].Journal of Vibration and Shock, 2007, 26(2) : 29-32,173.
[6] Cardoso J F, Souloumiac A. Blind beamforming for non Gaussian signals [J]. IEE Proc. F: Radar and Signal Process, 1993, 140(6): 362-370.
[7] 王权锋，胥德平，等. 金属矿地震数据降噪研究[J].国土资源科技管理，2012，29(6)：106-110.
    WANG Quan-feng, XU De-ping, etc. Research on seismic data noise reduction in metallic ore based on wavelet domain blind source separation JADE algorithm[J]. Scientific and　Technological Management of Land and Resources, 2012, 29(6) : 106-110.
[8] 陈恩利，张玺，申永军，等.基于SVD 降噪和盲信号分离的滚动轴承故障诊断.振动与冲击，2012，31(23)：185-190.
    CHEN En-li, ZHANG Xi, SHEN Yong-jun, etc.Fault diagnosis of rolling bearings based on SVD denoising and blind signals separation[J]. Journal of Vibration and Shock, 2012,31(23) : 185-190.
[9] Cardoso J F. High-Order Contrasts for Independent Component Analysis [J]. Neural Computation, 1999, 11(1): 157-192.
[10] 张安清.盲分离技术及其在水声信号中的应用研究[D].大连：大连理工大学，2006.
    ZHANG An-qing.Study on blind separation technology and its application of underwater acoustic signals[D].DaLian:DaLian University of Technology,2006.
[11] Gelle G,Colas M,Delaunay G. Blind sources separation applied to rotating machines monitoring by acoustical and vibrations analysis[J]. Mechanical Systems and Signal Processing, 2000, 14(3):427-442.
[12] Cardoso J F, Laheld B. Equivariant adaptive source separation[J]. The IEEE Transactions on Signal Processing, 1996, 44(12):3017-3029.
[13] 艾延廷，费成巍，张凤玲，等. ICA在航空发动机振动信号盲源分离中的应用. 振动、测试与诊断，2010，30(6)：671-674，711.
    AI Yan-ting,FEI Cheng-wei,ZHANG Feng-ling,etc.Blind source separation for aero-engines vibration signalby independent component analysis [J]. Journal of Vibration, Measurement& Diagnosis, 2010, 30(6): 671-674, 711.