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Improved RAkEL’s Fault Diagnosis Method for High-Speed Train Traction Transformer

The traction system is very important to ensure the safe operation of high-speed trains, and the failure of the traction transformer is the most likely fault in the traction system. Fault diagnosis in actual work relies largely on manual experience. This paper proposes an improved RAkEL (Random k-La...

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Autores principales: Li, Man, Zhou, Xinyi, Qin, Siyao, Bin, Ziyan, Wang, Yanhui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10574964/
https://www.ncbi.nlm.nih.gov/pubmed/37836898
http://dx.doi.org/10.3390/s23198067
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author Li, Man
Zhou, Xinyi
Qin, Siyao
Bin, Ziyan
Wang, Yanhui
author_facet Li, Man
Zhou, Xinyi
Qin, Siyao
Bin, Ziyan
Wang, Yanhui
author_sort Li, Man
collection PubMed
description The traction system is very important to ensure the safe operation of high-speed trains, and the failure of the traction transformer is the most likely fault in the traction system. Fault diagnosis in actual work relies largely on manual experience. This paper proposes an improved RAkEL (Random k-Labelsets) algorithm for the fault diagnosis of high-speed train traction transformers. Firstly, this article starts from the large amount of “sleeping” fault maintenance data accumulated by the railway department, takes a single maintenance record as an instance, uses specific monitoring values to construct an instance vector, and uses the fault phenomena corresponding to the monitoring indicators as labels. Then, this paper improves the step of selecting k-labelsets in RAkEL, and extracts associated faults using the Relief algorithm. Finally, this paper excavates and uses the association rules between data and faults to identify traction transformer faults. The results showed that the improved RAkEL diagnostic method had a significant improvement in the evaluation indicators. Compared with other multi-label classification algorithms, including BR (Binary Relevance) and CLR (Calibrated Label Ranking), this method performs well on multiple evaluation indicators. It can further help engineers perform timely maintenance work in the future.
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spelling pubmed-105749642023-10-14 Improved RAkEL’s Fault Diagnosis Method for High-Speed Train Traction Transformer Li, Man Zhou, Xinyi Qin, Siyao Bin, Ziyan Wang, Yanhui Sensors (Basel) Article The traction system is very important to ensure the safe operation of high-speed trains, and the failure of the traction transformer is the most likely fault in the traction system. Fault diagnosis in actual work relies largely on manual experience. This paper proposes an improved RAkEL (Random k-Labelsets) algorithm for the fault diagnosis of high-speed train traction transformers. Firstly, this article starts from the large amount of “sleeping” fault maintenance data accumulated by the railway department, takes a single maintenance record as an instance, uses specific monitoring values to construct an instance vector, and uses the fault phenomena corresponding to the monitoring indicators as labels. Then, this paper improves the step of selecting k-labelsets in RAkEL, and extracts associated faults using the Relief algorithm. Finally, this paper excavates and uses the association rules between data and faults to identify traction transformer faults. The results showed that the improved RAkEL diagnostic method had a significant improvement in the evaluation indicators. Compared with other multi-label classification algorithms, including BR (Binary Relevance) and CLR (Calibrated Label Ranking), this method performs well on multiple evaluation indicators. It can further help engineers perform timely maintenance work in the future. MDPI 2023-09-25 /pmc/articles/PMC10574964/ /pubmed/37836898 http://dx.doi.org/10.3390/s23198067 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Li, Man
Zhou, Xinyi
Qin, Siyao
Bin, Ziyan
Wang, Yanhui
Improved RAkEL’s Fault Diagnosis Method for High-Speed Train Traction Transformer
title Improved RAkEL’s Fault Diagnosis Method for High-Speed Train Traction Transformer
title_full Improved RAkEL’s Fault Diagnosis Method for High-Speed Train Traction Transformer
title_fullStr Improved RAkEL’s Fault Diagnosis Method for High-Speed Train Traction Transformer
title_full_unstemmed Improved RAkEL’s Fault Diagnosis Method for High-Speed Train Traction Transformer
title_short Improved RAkEL’s Fault Diagnosis Method for High-Speed Train Traction Transformer
title_sort improved rakel’s fault diagnosis method for high-speed train traction transformer
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10574964/
https://www.ncbi.nlm.nih.gov/pubmed/37836898
http://dx.doi.org/10.3390/s23198067
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