SIA OpenIR  > 工业信息学研究室  > 工业控制系统研究室
An online self-constructing wavelet fuzzy neural network for machine condition monitoring
Guo QJ(郭前进); Yu HB(于海斌); Xu AD(徐皑冬)
Department工业控制系统研究室
Conference Name4th International Conference on Machine Learning and Cybernetics
Conference DateAugust 18-21, 2005
Conference PlaceCanton, China
Author of SourceIEEE Systems, Man & Cybernet TCC, Hong Kong Polytechn Univ, Hebei Univ, S China Univ Technol, Chongqing Univ, Sun Yatsen Univ, Harbin Inst Technol, Int Univ Germany
Source PublicationProceedings of 2005 International Conference on Machine Learning and Cybernetics, Vols 1-9
PublisherIEEE
Publication PlaceNEW YORK
2005
Pages4193-4200
Indexed ByEI ; CPCI(ISTP)
EI Accession number2005509539462
WOS IDWOS:000235325606052
Contribution Rank1
ISBN0-7803-9091-1
KeywordFuzzy Neural Networks Wavelet Transformation Machine Condition Monitoring Fault Diagnosis
Abstract

The subject of machine condition monitoring is charged with developing new technologies to diagnose the machinery problems. A problem with diagnostic techniques is that they require constant human interpretation of the results. Fuzzy neural networks show good ability of self-adaption and self-learning, wavelet transformation or analysis shows the time frequency location characteristic and multi-scale ability. Inspired by these advantages, a wavelet fuzzy neural network (WFNN) is proposed for fault diagnosis in this paper. This fuzzy neural network uses wavelet basis function as membership function whose shape can be adjusted on line so that the networks have better learning and adaptive ability. An on-line learning algorithm is applied to automatically construct the wavelet fuzzy neural network. There are no rules initially in the wavelet fuzzy neural network. They are created and adapted as on-line learning proceeds via simultaneous structure and parameter learning. The advantages of this learning algorithm are that it converges quickly and the obtained fuzzy rules are more precise. The results of simulation show that this SWFNN network method has the advantage of faster learning rate and higher diagnosing precision.

Language英语
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/8059
Collection工业信息学研究室_工业控制系统研究室
Corresponding AuthorGuo QJ(郭前进)
Affiliation1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning, 110006, China
2.Graduate School, Chinese Academy of Sciences, Beijing 100039, China
Recommended Citation
GB/T 7714
Guo QJ,Yu HB,Xu AD. An online self-constructing wavelet fuzzy neural network for machine condition monitoring[C]//IEEE Systems, Man & Cybernet TCC, Hong Kong Polytechn Univ, Hebei Univ, S China Univ Technol, Chongqing Univ, Sun Yatsen Univ, Harbin Inst Technol, Int Univ Germany. NEW YORK:IEEE,2005:4193-4200.
Files in This Item: Download All
File Name/Size DocType Version Access License
HYQW000278.pdf(462KB)会议论文 开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Guo QJ(郭前进)]'s Articles
[Yu HB(于海斌)]'s Articles
[Xu AD(徐皑冬)]'s Articles
Baidu academic
Similar articles in Baidu academic
[Guo QJ(郭前进)]'s Articles
[Yu HB(于海斌)]'s Articles
[Xu AD(徐皑冬)]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Guo QJ(郭前进)]'s Articles
[Yu HB(于海斌)]'s Articles
[Xu AD(徐皑冬)]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: HYQW000278.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.