Parametric Fault Diagnosis of Analog Circuits Based on a Semi-Supervised Algorithm

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...

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Veröffentlicht in:Symmetry (Basel) Jg. 11; H. 2; S. 228
Hauptverfasser: Wang, Ling, Zhou, Dongfang, Tian, Hui, Zhang, Hao, Zhang, Wei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.02.2019
Schlagworte:
Algorithms
Analog circuits
Circuits
Classification
Decision trees
Discriminant analysis
Eigenvalues
Fault diagnosis
Feature extraction
Machine learning
Optimization
Principal components analysis
Wavelet transforms
ISSN:2073-8994, 2073-8994
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Zusammenfassung: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.
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ISSN:2073-8994
2073-8994
DOI:10.3390/sym11020228