Parametric Fault Diagnosis of Analog Circuits Based on a Semi-Supervised Algorithm
| Autor: | Wei Zhang, Hui Tian, Ling Wang, Dongfang Zhou, Hao Zhang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
lifting wavelet
Physics and Astronomy (miscellaneous) Computer science Generalization General Mathematics 02 engineering and technology Hardware_PERFORMANCEANDRELIABILITY Fault (power engineering) Computer Science::Hardware Architecture local preserving projection 0202 electrical engineering electronic engineering information engineering Computer Science (miscellaneous) semi-supervised random forest Parametric statistics Analogue electronics lcsh:Mathematics Dimensionality reduction 020208 electrical & electronic engineering Wavelet transform Fisher linear discriminant analysis fault diagnosis lcsh:QA1-939 Linear discriminant analysis Range (mathematics) ComputingMethodologies_PATTERNRECOGNITION Chemistry (miscellaneous) 020201 artificial intelligence & image processing Algorithm |
| Zdroj: | Symmetry; Volume 11; Issue 2; Pages: 228 Symmetry, Vol 11, Iss 2, p 228 (2019) |
| ISSN: | 2073-8994 |
| DOI: | 10.3390/sym11020228 |
| Popis: | The parametric fault diagnosis of analog circuits is very crucial for condition-based maintenance (CBM) in prognosis and health management. In order to improve the diagnostic rate of parametric faults in engineering applications, a semi-supervised machine learning algorithm was used to classify the parametric fault. A lifting wavelet transform was used to extract fault features, a local preserving mapping algorithm was adopted to optimize the Fisher linear discriminant analysis, and a semi-supervised cooperative training algorithm was utilized for fault classification. In the proposed method, the fault values were randomly selected as training samples in a range of parametric fault intervals, for both optimizing the generalization of the model and improving the fault diagnosis rate. Furthermore, after semi-supervised dimensionality reduction and semi-supervised classification were applied, the diagnosis rate was slightly higher than the existing training model by fixing the value of the analyzed component. |
| Databáze: | OpenAIRE |
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