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Symmetry 2019, 11(2), 228; https://doi.org/10.3390/sym11020228

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

1
College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou 450002, China
2
Department of Communication, National Digital Switching System Engineering and Technology R&D Center (NDSC), Zhengzhou 450002, China
*
Author to whom correspondence should be addressed.
Received: 10 January 2019 / Revised: 6 February 2019 / Accepted: 12 February 2019 / Published: 14 February 2019
(This article belongs to the Special Issue Symmetry in Engineering Sciences)
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Abstract

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. View Full-Text
Keywords: fault diagnosis; lifting wavelet; local preserving projection; Fisher linear discriminant analysis; semi-supervised random forest fault diagnosis; lifting wavelet; local preserving projection; Fisher linear discriminant analysis; semi-supervised random forest
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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.

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