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Research on Performance Degradation Estimation of Key Components of High-Speed Train Bogie Based on Multi-Task Learning

The safe and comfortable operation of high-speed trains has attracted extensive attention. With the operation of the train, the performance of high-speed train bogie components inevitably degrades and eventually leads to failures. At present, it is a common method to achieve performance degradation...

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Detalles Bibliográficos
Autores principales: Ren, Junxiao, Jin, Weidong, Wu, Yunpu, Sun, Zhang, Li, Liang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10137914/
https://www.ncbi.nlm.nih.gov/pubmed/37190484
http://dx.doi.org/10.3390/e25040696
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author Ren, Junxiao
Jin, Weidong
Wu, Yunpu
Sun, Zhang
Li, Liang
author_facet Ren, Junxiao
Jin, Weidong
Wu, Yunpu
Sun, Zhang
Li, Liang
author_sort Ren, Junxiao
collection PubMed
description The safe and comfortable operation of high-speed trains has attracted extensive attention. With the operation of the train, the performance of high-speed train bogie components inevitably degrades and eventually leads to failures. At present, it is a common method to achieve performance degradation estimation of bogie components by processing high-speed train vibration signals and analyzing the information contained in the signals. In the face of complex signals, the usage of information theory, such as information entropy, to achieve performance degradation estimations is not satisfactory, and recent studies have more often used deep learning methods instead of traditional methods, such as information theory or signal processing, to obtain higher estimation accuracy. However, current research is more focused on the estimation for a certain component of the bogie and does not consider the bogie as a whole system to accomplish the performance degradation estimation task for several key components at the same time. In this paper, based on soft parameter sharing multi-task deep learning, a multi-task and multi-scale convolutional neural network is proposed to realize performance degradation state estimations of key components of a high-speed train bogie. Firstly, the structure takes into account the multi-scale characteristics of high-speed train vibration signals and uses a multi-scale convolution structure to better extract the key features of the signal. Secondly, considering that the vibration signal of high-speed trains contains the information of all components, the soft parameter sharing method is adopted to realize feature sharing in the depth structure and improve the utilization of information. The effectiveness and superiority of the structure proposed by the experiment is a feasible scheme for improving the performance degradation estimation of a high-speed train bogie.
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spelling pubmed-101379142023-04-28 Research on Performance Degradation Estimation of Key Components of High-Speed Train Bogie Based on Multi-Task Learning Ren, Junxiao Jin, Weidong Wu, Yunpu Sun, Zhang Li, Liang Entropy (Basel) Article The safe and comfortable operation of high-speed trains has attracted extensive attention. With the operation of the train, the performance of high-speed train bogie components inevitably degrades and eventually leads to failures. At present, it is a common method to achieve performance degradation estimation of bogie components by processing high-speed train vibration signals and analyzing the information contained in the signals. In the face of complex signals, the usage of information theory, such as information entropy, to achieve performance degradation estimations is not satisfactory, and recent studies have more often used deep learning methods instead of traditional methods, such as information theory or signal processing, to obtain higher estimation accuracy. However, current research is more focused on the estimation for a certain component of the bogie and does not consider the bogie as a whole system to accomplish the performance degradation estimation task for several key components at the same time. In this paper, based on soft parameter sharing multi-task deep learning, a multi-task and multi-scale convolutional neural network is proposed to realize performance degradation state estimations of key components of a high-speed train bogie. Firstly, the structure takes into account the multi-scale characteristics of high-speed train vibration signals and uses a multi-scale convolution structure to better extract the key features of the signal. Secondly, considering that the vibration signal of high-speed trains contains the information of all components, the soft parameter sharing method is adopted to realize feature sharing in the depth structure and improve the utilization of information. The effectiveness and superiority of the structure proposed by the experiment is a feasible scheme for improving the performance degradation estimation of a high-speed train bogie. MDPI 2023-04-20 /pmc/articles/PMC10137914/ /pubmed/37190484 http://dx.doi.org/10.3390/e25040696 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
Ren, Junxiao
Jin, Weidong
Wu, Yunpu
Sun, Zhang
Li, Liang
Research on Performance Degradation Estimation of Key Components of High-Speed Train Bogie Based on Multi-Task Learning
title Research on Performance Degradation Estimation of Key Components of High-Speed Train Bogie Based on Multi-Task Learning
title_full Research on Performance Degradation Estimation of Key Components of High-Speed Train Bogie Based on Multi-Task Learning
title_fullStr Research on Performance Degradation Estimation of Key Components of High-Speed Train Bogie Based on Multi-Task Learning
title_full_unstemmed Research on Performance Degradation Estimation of Key Components of High-Speed Train Bogie Based on Multi-Task Learning
title_short Research on Performance Degradation Estimation of Key Components of High-Speed Train Bogie Based on Multi-Task Learning
title_sort research on performance degradation estimation of key components of high-speed train bogie based on multi-task learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10137914/
https://www.ncbi.nlm.nih.gov/pubmed/37190484
http://dx.doi.org/10.3390/e25040696
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