Wavelet neural network approach for fault diagnosis of analogue circuits

被引：54
|
作者：
He, Y [1 ]
Tan, Y
Sun, Y
机构：
[1] Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Peoples R China
[2] Univ Hertfordshire, Dept Elect Commun & Elect Engn, Hatfield AL10 9AB, Herts, England
来源：
IEE PROCEEDINGS-CIRCUITS DEVICES AND SYSTEMS | 2004年 / 151卷 / 04期
关键词：
D O I：
10.1049/ip-cds:20040495
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
A systematic method for fault diagnosis of analogue circuits based on the combination of neural networks and wavelet transforms is presented. Using wavelet decomposition as a tool for removing noise from the sampled signals, optimal feature information is extracted by wavelet noise removal, multi-resolution decomposition, PCA (principal component analysis) and data normalisation. The features are applied to the proposed wavelet neural network and the fault patterns are classified. Diagnosis principles and procedures are described. The reliability of the method and comparison with other methods are shown by two active filter examples.
引用
收藏
页码：379 / 384
页数：6
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