Nonlinear Soft Fault Diagnosis of Analog Circuits Based on RCCA-SVM
| Autor: | Zhang Rui, Yang Li, Yinjing Guo, Pengfei Huan, Manlin Zhang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
General Computer Science
Analogue electronics Computer science business.industry RCCA-SVM 020208 electrical & electronic engineering General Engineering Pattern recognition 02 engineering and technology Fault (power engineering) 020202 computer hardware & architecture Wavelet packet decomposition Support vector machine Dimension (vector space) Feature (computer vision) Nonlinear analog circuit fault diagnosis Principal component analysis Classifier (linguistics) WPT 0202 electrical engineering electronic engineering information engineering General Materials Science Artificial intelligence lcsh:Electrical engineering. Electronics. Nuclear engineering business lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 8, Pp 60951-60963 (2020) |
| ISSN: | 2169-3536 |
| Popis: | In the soft fault diagnosis of nonlinear analog filter circuits, the single feature can't maximally reveal the behaviors hidden in signals. In order to overcome such shortcomings, a fusion algorithm weighted feature from multi-group is proposed. This method use reliefF algorithm to optimize canonical correlation analysis combines support vector machine(RCCA-SVM) for diagnosis. The fault characteristics used in this method are extracted from the time-domain, statistical features and frequency-domain by wavelet packet transform (WPT). And then the CCA algorithm is used to improve the correlation of features according to the weights of the features. Finally, the fusion features are dimension reduced by principal component analysis(PCA), support vector machine(SVM) is the classifier of the diagnosis. The simulations show that the proposed method has a good diagnostic effect on circuit fault diagnosis of non-linear analog circuits. |
| Databáze: | OpenAIRE |
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