基于模型的CICA及其在滚动轴承故障诊断中的应用

摘要
图/表
参考文献(14)
相关文章 (15)

全文: PDF (1404 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要由独立成分分析（ICA）的顺序不确定性带来的源数估计和对传感器个数的估计问题使得ICA在机械故障诊断中的广泛应用受到了限制，而约束独立成分分析（CICA）充分利用了设备的先验知识作为ICA的约束条件，可以使ICA算法收敛到感兴趣的故障信号。本文提出了一种基于滚动轴承模型的约束独立成分分析（CICA）方法，该方法可以从传感器信号中快速诊断出设备是否发生了滚动轴承故障，并用仿真和实验验证了该方法在滚动轴承故障诊断中的有效性。

	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
	王志阳1
	杜文辽 2
	陈进3

关键词 ：独立成分分析, 约束独立成分分析, 盲源分离, 机械故障诊断, 滚动轴承

Abstract：The order ambiguity from independent component analysis (ICA) makes it very difficult to estimate the numbers of sources and sensors. Constrained independent component analysis (CICA) can only converge to the desired faulty signal using some prior knowledge from the machinery as a constraint. This paper presents a model-based constrained independent component analysis method for fault diagnosis of rolling element bearing, and it is successfully verified by computation simulation and rolling element bearing experiment.

Key words： Independent component analysis Constrained independent component analysis Blind source separation Machine fault diagnosis Rolling element bearing

收稿日期: 2013-06-02 出版日期: 2015-04-25

引用本文:

王志阳1，杜文辽 2，陈进3. 基于模型的CICA及其在滚动轴承故障诊断中的应用[J]. 振动与冲击, 2015, 34(8): 66-70.
Wang Zhiyang1, Du Wenliao2, Chen Jin3. Fault Diagnosis of Rolling Element Bearing Based on Model-based Constrained Independent Component Analysis. JOURNAL OF VIBRATION AND SHOCK, 2015, 34(8): 66-70.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2015/V34/I8/66

[1] A.C. Seungjin Choi, Hyung-Min Park and Soo-Young Lee, Blind Source Separation and Independent Component Analysis: A Review [J], Neural Information Processing - Letters and Reviews 2005,Vol.6(No.1):1-57.
[2] A. Kardec Barros, J. Carlos Principe, D. Erdogmus, Independent Component Analysis and Blind Source Separation [J], Signal Processing 2007,87(8):1817-1818.
[3] A. Hyvärinen,Hyvarinen, Independent component analysis: algorithms and applications [J], Neural computation 2001,13(7):1527.
[4] A.K. Barros, A. Cichocki, Extraction of Specific Signals with Temporal Structure [J], Neural Computation 2001,13(9):1995-2003.
[5] C.G. Puntonet,E.W. Lang, Blind source separation and independent component analysis [J], Neurocomputing 2006,69(13-15):1413-1413.
[6] Wang, ZY, Chen, J, Dong, GM, et al., Constrained independent component analysis and its application to machine fault diagnosis, Mechanical Systems and Signal Processing, 2011, 25(7):2501-2512.
[7] 王志阳，陈进，肖文斌等, 基于约束独立成分分析的滚动轴承故障诊断[J]. 振动与冲击, 2012. 31(9): 105-109.
Wang Zhiyang,Chen Jin, Xiao Wenbin et.al, Fault Diagnosis of Rolling Element Bearing Based on Constrained Independent Component Analysis[J] . Journal of vibration and shock, 2012, 31(9): 105-109.
[8] Wang, X., Huang, Z., Zhou, Y.,et al. , Approaches and applications of semi-blind signal extraction for communication signals based on constrained independent component analysis: The complex case, Neurocomputing, 3013, 101(0): 204-216.
[9]A. Hyvärinen, One-Unit Contrast Functions for Independent Component Analysis: A Statistical Analysis [J], Neural Networks for Signal Processing 1997,VII:388-397.
[10]W. Lu, J.C. Rajapakse, ICA with Reference [J], Neurocomputing 2006,69(16-18):2244-2257.
[11]R.B. Randall, J. Antoni, S. Chobsaard, The relationship between special correlation and envelope analysis in the diagnostics of bearing faults and other cyclostationary machine signals [J], Mechanical Systems and Signal Processing 2001,15(5):945-962.
[12]Antoni, J, Randall, B. R, Differential diagnosis of gear and bearing faults [J], American Society of Mechanical Engineers 2002,124(2).
[13]N. Y, Pan, J,Chen, X. L, Li, Spectral entropy: a complementary index for rolling element bearing performance degradation assessment, Mechanical Engineering Science. 2009, 1223-1231
[14]陈进, 机械设备振动监测与故障诊断[M]: 上海交通大学出版社. 上海, 1999.
Chen Jin, Condition Monitoring and Fault Diagnostics of Machines[M]. Shanghai jiaotong University press. Shanghai,1999.