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...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Electric Drive for Locomotives
2020-03-01
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| Series: | 机车电传动 |
| Subjects: | |
| Online Access: | http://edl.csrzic.com/thesisDetails#10.13890/j.issn.1000-128x.2020.02.015 |
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| Summary: | 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. |
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| ISSN: | 1000-128X |