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...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-02-01
|
| Series: | Symmetry |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-8994/11/2/228 |
| _version_ | 1817989057621262336 |
|---|---|
| author | Ling Wang Dongfang Zhou Hui Tian Hao Zhang Wei Zhang |
| author_facet | Ling Wang Dongfang Zhou Hui Tian Hao Zhang Wei Zhang |
| author_sort | Ling Wang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T00:41:26Z |
| format | Article |
| id | doaj.art-54c5afc19aa444faa5e45d1c2c934b29 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-04-14T00:41:26Z |
| publishDate | 2019-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-54c5afc19aa444faa5e45d1c2c934b292022-12-22T02:22:10ZengMDPI AGSymmetry2073-89942019-02-0111222810.3390/sym11020228sym11020228Parametric Fault Diagnosis of Analog Circuits Based on a Semi-Supervised AlgorithmLing Wang0Dongfang Zhou1Hui Tian2Hao Zhang3Wei Zhang4College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou 450002, ChinaDepartment of Communication, National Digital Switching System Engineering and Technology R&D Center (NDSC), Zhengzhou 450002, ChinaCollege of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou 450002, ChinaCollege of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou 450002, ChinaCollege of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou 450002, ChinaThe 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.https://www.mdpi.com/2073-8994/11/2/228fault diagnosislifting waveletlocal preserving projectionFisher linear discriminant analysissemi-supervised random forest |
| spellingShingle | Ling Wang Dongfang Zhou Hui Tian Hao Zhang Wei Zhang Parametric Fault Diagnosis of Analog Circuits Based on a Semi-Supervised Algorithm Symmetry fault diagnosis lifting wavelet local preserving projection Fisher linear discriminant analysis semi-supervised random forest |
| title | Parametric Fault Diagnosis of Analog Circuits Based on a Semi-Supervised Algorithm |
| title_full | Parametric Fault Diagnosis of Analog Circuits Based on a Semi-Supervised Algorithm |
| title_fullStr | Parametric Fault Diagnosis of Analog Circuits Based on a Semi-Supervised Algorithm |
| title_full_unstemmed | Parametric Fault Diagnosis of Analog Circuits Based on a Semi-Supervised Algorithm |
| title_short | Parametric Fault Diagnosis of Analog Circuits Based on a Semi-Supervised Algorithm |
| title_sort | parametric fault diagnosis of analog circuits based on a semi supervised algorithm |
| topic | fault diagnosis lifting wavelet local preserving projection Fisher linear discriminant analysis semi-supervised random forest |
| url | https://www.mdpi.com/2073-8994/11/2/228 |
| work_keys_str_mv | AT lingwang parametricfaultdiagnosisofanalogcircuitsbasedonasemisupervisedalgorithm AT dongfangzhou parametricfaultdiagnosisofanalogcircuitsbasedonasemisupervisedalgorithm AT huitian parametricfaultdiagnosisofanalogcircuitsbasedonasemisupervisedalgorithm AT haozhang parametricfaultdiagnosisofanalogcircuitsbasedonasemisupervisedalgorithm AT weizhang parametricfaultdiagnosisofanalogcircuitsbasedonasemisupervisedalgorithm |