Joint time-frequency and kernel principal component based SOM for machine maintenance

CORC > 沈阳自动化研究所 > 中国科学院沈阳自动化研究所 > 工业信息学研究室 > 工业控制系统研究室

	Joint time-frequency and kernel principal component based SOM for machine maintenance
	Guo QJ(郭前进); Yu HB(于海斌); Nie YY(聂义勇); Xu AD(徐皑冬)
	2006
会议名称	3rd International Symposium on Neural Networks (ISNN 2006)
会议日期	May 28-31, 2006
会议地点	Chengdu, China
页码	1144-1154
通讯作者	郭前进
中文摘要	Conventional vibration signals processing techniques are most suitable for stationary processes. However, most mechanical faults in machinery reveal themselves through transient events in vibration signals. That is, the vibration generated by industrial machines always contains nonlinear and nonstationary signals. It is expected that a desired time-frequency analysis method should have good computation efficiency, and have good resolution in both time domain and frequency domain. In this paper, the auto-regressive model based pseudo-Wigner-Ville distribution for an integrated time-frequency signature extraction of the machine vibration is designed, the method offers the advantage of good localization of the vibration signal energy in the time-frequency domain. Kernel principal component analysis (KPCA) is used for the redundancy reduction and feature extraction in the time-frequency domain, and the self-organizing map (SOM) was employed to identify the faults of the rotating machinery. Experimental results show that the proposed method is very effective.
收录类别	SCI ; EI ; CPCI（ISTP）
产权排序	1
会议主办者	Univ Electr Sci & Technol China, Chinese Univ Hong Kong, Asia Pacific Neural Network Assembly, European Neural Network Soc, IEEE Circuits & Syst Soc, IEEE Computat Intelligence Soc, Int Neural Network Soc, Natl Nat Sci Fdn China, KC Wong Educ Fdn Hong Kong
会议录	ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 3, PROCEEDINGS
会议录出版者	SPRINGER-VERLAG
会议录出版地	BERLIN
语种	英语
ISSN号	0302-9743
ISBN号	3-540-34482-9
WOS记录号	WOS:000239485300167
研究领域[WOS]	Computer Science
WOS标题词	Science & Technology ; Technology
内容类型	会议论文
源URL	[http://ir.sia.cn/handle/173321/8097]
专题	沈阳自动化研究所_工业信息学研究室_工业控制系统研究室
推荐引用方式 GB/T 7714	Guo QJ,Yu HB,Nie YY,et al. Joint time-frequency and kernel principal component based SOM for machine maintenance[C]. 见:3rd International Symposium on Neural Networks (ISNN 2006). Chengdu, China. May 28-31, 2006.