Parametric Fault Diagnosis of Analog Circuits Based on a Semi-Supervised Algorithm
AbstractThe 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. View Full-Text
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Wang, L.; Zhou, D.; Tian, H.; Zhang, H.; Zhang, W. Parametric Fault Diagnosis of Analog Circuits Based on a Semi-Supervised Algorithm. Symmetry 2019, 11, 228.
Wang L, Zhou D, Tian H, Zhang H, Zhang W. Parametric Fault Diagnosis of Analog Circuits Based on a Semi-Supervised Algorithm. Symmetry. 2019; 11(2):228.Chicago/Turabian Style
Wang, Ling; Zhou, Dongfang; Tian, Hui; Zhang, Hao; Zhang, Wei. 2019. "Parametric Fault Diagnosis of Analog Circuits Based on a Semi-Supervised Algorithm." Symmetry 11, no. 2: 228.
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