DocumentCode :
1121065
Title :
Wavelet neural network approach for fault diagnosis of analogue circuits
Author :
He, Y. ; Tan, Y. ; Sun, Y.
Author_Institution :
Coll. Of Electr. & Inf. Eng., Hunan Univ., Changsha, China
Volume :
151
Issue :
4
fYear :
2004
Firstpage :
379
Lastpage :
384
Abstract :
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.
Keywords :
analogue circuits; fault simulation; neural nets; principal component analysis; signal denoising; wavelet transforms; active filter; analogue circuits; data normalisation; fault diagnosis; fault patterns; feature information extraction; multiresolution decomposition; noise removal; principal component analysis; wavelet decomposition; wavelet neural network; wavelet transforms;
fLanguage :
English
Journal_Title :
Circuits, Devices and Systems, IEE Proceedings
Publisher :
iet
ISSN :
1350-2409
Type :
jour
DOI :
10.1049/ip-cds:20040495
Filename :
1338152
Link To Document :
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