中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
A hybrid PSO-GD based intelligent method for machine diagnosis

文献类型:期刊论文

作者Guo QJ(郭前进); Yu HB(于海斌); Xu AD(徐皑冬)
刊名Digital Signal Processing
出版日期2006
卷号16期号:4页码:402-418
关键词Wavelet neural network Particle swarm optimization Gradient descent algorithm Machine diagnosis
ISSN号1051-2004
产权排序1
通讯作者于海斌
中文摘要This paper presents an intelligent methodology for diagnosing incipient faults in rotating machinery. In this fault diagnosis system, wavelet neural network techniques are used in combination with a new evolutionary learning algorithm. This new evolutionary learning algorithm is based on a hybrid of the constriction factor approach for particle swarm optimization (PSO) technique and the gradient descent (GD) technique, and is thus called HGDPSO. The HGDPSO is developed in such a way that a constriction factor approach for particle swarm optimization (CFA for PSO) is applied as a based level search, which can give a good direction to the optimal global region, and a local search gradient descent (GD) algorithm is used as a fine tuning to determine the optimal solution at the final. The effectiveness of the HGDPSO based WNN is demonstrated through the classification of the fault signals in rotating machinery. The simulated results show its feasibility and validity.
WOS标题词Science & Technology ; Technology
类目[WOS]Engineering, Electrical & Electronic
研究领域[WOS]Engineering
关键词[WOS]PARTICLE SWARM OPTIMIZATION ; NEURAL NETWORKS ; WAVELET ; POWER
收录类别SCI ; EI
语种英语
WOS记录号WOS:000239395300007
公开日期2012-05-29
源URL[http://ir.sia.cn/handle/173321/6896]  
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,Xu AD. A hybrid PSO-GD based intelligent method for machine diagnosis[J]. Digital Signal Processing,2006,16(4):402-418.
APA Guo QJ,Yu HB,&Xu AD.(2006).A hybrid PSO-GD based intelligent method for machine diagnosis.Digital Signal Processing,16(4),402-418.
MLA Guo QJ,et al."A hybrid PSO-GD based intelligent method for machine diagnosis".Digital Signal Processing 16.4(2006):402-418.

入库方式: OAI收割

来源:沈阳自动化研究所

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