A Novel Feature Extraction Method for Soft Faults in Nonlinear Analog Circuits Based on LMD-GFD and KPCA
To obtain feature information of soft faults in non-linear analog circuits in a more effective way, this paper proposed a novel feature extraction method for soft faults in non-linear analog circuits based on Local Mean Decomposition-Generalized Fractal Dimension (LMD-GFD) and Kernel Principal Compo...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
2021-01-01
|
| Series: | Tehnički Vjesnik |
| Subjects: | |
| Online Access: | https://hrcak.srce.hr/file/385034 |
| _version_ | 1797206879118557184 |
|---|---|
| author | Xinmiao Lu* Jiaxu Wang Qiong Wu Yuhan Wei Yanwen Su |
| author_facet | Xinmiao Lu* Jiaxu Wang Qiong Wu Yuhan Wei Yanwen Su |
| author_sort | Xinmiao Lu* |
| collection | DOAJ |
| description | To obtain feature information of soft faults in non-linear analog circuits in a more effective way, this paper proposed a novel feature extraction method for soft faults in non-linear analog circuits based on Local Mean Decomposition-Generalized Fractal Dimension (LMD-GFD) and Kernel Principal Component Analysis (KPCA). First, the fault signals were subject to LMD, the features of each component signal were extracted by GFD for the first time, and a high-dimensional feature space was formed. Then, KPCA was employed to reduce the dimensionality of the high-dimensional feature space, and feature extraction was performed again; at last, KPCA and Support Vector Machine (SVM) were adopted to diagnose the faults. The experimental results showed that the proposed LMD-GFD-KPCA method had effectively extracted the features of the soft faults in the non-linear analog circuits, and it achieved a high diagnosis rate. |
| first_indexed | 2024-04-24T09:14:01Z |
| format | Article |
| id | doaj.art-a690d54cea884bb8af84bfe5df89b070 |
| institution | Directory Open Access Journal |
| issn | 1330-3651 1848-6339 |
| language | English |
| last_indexed | 2024-04-24T09:14:01Z |
| publishDate | 2021-01-01 |
| publisher | Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek |
| record_format | Article |
| series | Tehnički Vjesnik |
| spelling | doaj.art-a690d54cea884bb8af84bfe5df89b0702024-04-15T17:17:24ZengFaculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in OsijekTehnički Vjesnik1330-36511848-63392021-01-012862121212610.17559/TV-20210429033711A Novel Feature Extraction Method for Soft Faults in Nonlinear Analog Circuits Based on LMD-GFD and KPCAXinmiao Lu*0Jiaxu Wang1Qiong Wu2Yuhan Wei3Yanwen Su4School of Measurement-Control Technology and Communications Engineering, Harbin University of Science and Technology, Harbin 150080, ChinaSchool of Measurement-Control Technology and Communications Engineering, Harbin University of Science and Technology, Harbin 150080, ChinaHeilongjiang Network Space Research Center, Harbin 150090, ChinaSchool of Measurement-Control Technology and Communications Engineering, Harbin University of Science and Technology, Harbin 150080, ChinaSchool of Measurement-Control Technology and Communications Engineering, Harbin University of Science and Technology, Harbin 150080, ChinaTo obtain feature information of soft faults in non-linear analog circuits in a more effective way, this paper proposed a novel feature extraction method for soft faults in non-linear analog circuits based on Local Mean Decomposition-Generalized Fractal Dimension (LMD-GFD) and Kernel Principal Component Analysis (KPCA). First, the fault signals were subject to LMD, the features of each component signal were extracted by GFD for the first time, and a high-dimensional feature space was formed. Then, KPCA was employed to reduce the dimensionality of the high-dimensional feature space, and feature extraction was performed again; at last, KPCA and Support Vector Machine (SVM) were adopted to diagnose the faults. The experimental results showed that the proposed LMD-GFD-KPCA method had effectively extracted the features of the soft faults in the non-linear analog circuits, and it achieved a high diagnosis rate.https://hrcak.srce.hr/file/385034Fault Feature ExtractionGeneralized Fractal Dimension (GFD)Kernel Principal Component Analysis (KPCA)Local Mean Decomposition (LMD)Nonlinear Analog Circuit |
| spellingShingle | Xinmiao Lu* Jiaxu Wang Qiong Wu Yuhan Wei Yanwen Su A Novel Feature Extraction Method for Soft Faults in Nonlinear Analog Circuits Based on LMD-GFD and KPCA Tehnički Vjesnik Fault Feature Extraction Generalized Fractal Dimension (GFD) Kernel Principal Component Analysis (KPCA) Local Mean Decomposition (LMD) Nonlinear Analog Circuit |
| title | A Novel Feature Extraction Method for Soft Faults in Nonlinear Analog Circuits Based on LMD-GFD and KPCA |
| title_full | A Novel Feature Extraction Method for Soft Faults in Nonlinear Analog Circuits Based on LMD-GFD and KPCA |
| title_fullStr | A Novel Feature Extraction Method for Soft Faults in Nonlinear Analog Circuits Based on LMD-GFD and KPCA |
| title_full_unstemmed | A Novel Feature Extraction Method for Soft Faults in Nonlinear Analog Circuits Based on LMD-GFD and KPCA |
| title_short | A Novel Feature Extraction Method for Soft Faults in Nonlinear Analog Circuits Based on LMD-GFD and KPCA |
| title_sort | novel feature extraction method for soft faults in nonlinear analog circuits based on lmd gfd and kpca |
| topic | Fault Feature Extraction Generalized Fractal Dimension (GFD) Kernel Principal Component Analysis (KPCA) Local Mean Decomposition (LMD) Nonlinear Analog Circuit |
| url | https://hrcak.srce.hr/file/385034 |
| work_keys_str_mv | AT xinmiaolu anovelfeatureextractionmethodforsoftfaultsinnonlinearanalogcircuitsbasedonlmdgfdandkpca AT jiaxuwang anovelfeatureextractionmethodforsoftfaultsinnonlinearanalogcircuitsbasedonlmdgfdandkpca AT qiongwu anovelfeatureextractionmethodforsoftfaultsinnonlinearanalogcircuitsbasedonlmdgfdandkpca AT yuhanwei anovelfeatureextractionmethodforsoftfaultsinnonlinearanalogcircuitsbasedonlmdgfdandkpca AT yanwensu anovelfeatureextractionmethodforsoftfaultsinnonlinearanalogcircuitsbasedonlmdgfdandkpca AT xinmiaolu novelfeatureextractionmethodforsoftfaultsinnonlinearanalogcircuitsbasedonlmdgfdandkpca AT jiaxuwang novelfeatureextractionmethodforsoftfaultsinnonlinearanalogcircuitsbasedonlmdgfdandkpca AT qiongwu novelfeatureextractionmethodforsoftfaultsinnonlinearanalogcircuitsbasedonlmdgfdandkpca AT yuhanwei novelfeatureextractionmethodforsoftfaultsinnonlinearanalogcircuitsbasedonlmdgfdandkpca AT yanwensu novelfeatureextractionmethodforsoftfaultsinnonlinearanalogcircuitsbasedonlmdgfdandkpca |