MVB Fault Diagnosis Based on Waveform Feature Extraction and FA-Grid SVM
The fault diagnosis of train communication network has always been achallenge in train health management. A fault diagnosis method based on waveform feature extraction and FA-Grid SVM for multi-function vehicle bus(MVB) was proposed. The time-domain features were extracted from physical waveform of...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Electric Drive for Locomotives
2020-03-01
|
| Series: | 机车电传动 |
| Subjects: | |
| Online Access: | http://edl.csrzic.com/thesisDetails#10.13890/j.issn.1000-128x.2020.02.015 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850143075048882176 |
|---|---|
| author | Xiaomin DU Lide WANG Zhaozhao LI Hui SONG |
| author_facet | Xiaomin DU Lide WANG Zhaozhao LI Hui SONG |
| author_sort | Xiaomin DU |
| collection | DOAJ |
| description | The fault diagnosis of train communication network has always been achallenge in train health management. A fault diagnosis method based on waveform feature extraction and FA-Grid SVM for multi-function vehicle bus(MVB) was proposed. The time-domain features were extracted from physical waveform of the MVB bus and used as inputs of SVM which construct MVB fault dataset. Due to the concentration of optimal parameters of SVM, a two-step parameter optimization method based on FA-Grid was provided. Experimental results show that compared with traditional grid optimization and genetic algorithm (GA), the proposed FA-Grid optimization model has lower complexity and higher efficiency and could accurately diagnose MVB faults. |
| format | Article |
| id | doaj-art-196d55226cc44801bc308dced7c1c1a5 |
| institution | OA Journals |
| issn | 1000-128X |
| language | zho |
| publishDate | 2020-03-01 |
| publisher | Editorial Department of Electric Drive for Locomotives |
| record_format | Article |
| series | 机车电传动 |
| spelling | doaj-art-196d55226cc44801bc308dced7c1c1a52025-08-20T02:28:50ZzhoEditorial Department of Electric Drive for Locomotives机车电传动1000-128X2020-03-017174,8020921352MVB Fault Diagnosis Based on Waveform Feature Extraction and FA-Grid SVMXiaomin DULide WANGZhaozhao LIHui SONGThe fault diagnosis of train communication network has always been achallenge in train health management. A fault diagnosis method based on waveform feature extraction and FA-Grid SVM for multi-function vehicle bus(MVB) was proposed. The time-domain features were extracted from physical waveform of the MVB bus and used as inputs of SVM which construct MVB fault dataset. Due to the concentration of optimal parameters of SVM, a two-step parameter optimization method based on FA-Grid was provided. Experimental results show that compared with traditional grid optimization and genetic algorithm (GA), the proposed FA-Grid optimization model has lower complexity and higher efficiency and could accurately diagnose MVB faults.http://edl.csrzic.com/thesisDetails#10.13890/j.issn.1000-128x.2020.02.015MVB networkfault diagnosiswaveform feature extractionFA-Grid SVMtrain communication |
| spellingShingle | Xiaomin DU Lide WANG Zhaozhao LI Hui SONG MVB Fault Diagnosis Based on Waveform Feature Extraction and FA-Grid SVM 机车电传动 MVB network fault diagnosis waveform feature extraction FA-Grid SVM train communication |
| title | MVB Fault Diagnosis Based on Waveform Feature Extraction and FA-Grid SVM |
| title_full | MVB Fault Diagnosis Based on Waveform Feature Extraction and FA-Grid SVM |
| title_fullStr | MVB Fault Diagnosis Based on Waveform Feature Extraction and FA-Grid SVM |
| title_full_unstemmed | MVB Fault Diagnosis Based on Waveform Feature Extraction and FA-Grid SVM |
| title_short | MVB Fault Diagnosis Based on Waveform Feature Extraction and FA-Grid SVM |
| title_sort | mvb fault diagnosis based on waveform feature extraction and fa grid svm |
| topic | MVB network fault diagnosis waveform feature extraction FA-Grid SVM train communication |
| url | http://edl.csrzic.com/thesisDetails#10.13890/j.issn.1000-128x.2020.02.015 |
| work_keys_str_mv | AT xiaomindu mvbfaultdiagnosisbasedonwaveformfeatureextractionandfagridsvm AT lidewang mvbfaultdiagnosisbasedonwaveformfeatureextractionandfagridsvm AT zhaozhaoli mvbfaultdiagnosisbasedonwaveformfeatureextractionandfagridsvm AT huisong mvbfaultdiagnosisbasedonwaveformfeatureextractionandfagridsvm |