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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/25/4/696 |
| _version_ | 1797605527106093056 |
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| author | Junxiao Ren Weidong Jin Yunpu Wu Zhang Sun Liang Li |
| author_facet | Junxiao Ren Weidong Jin Yunpu Wu Zhang Sun Liang Li |
| author_sort | Junxiao Ren |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-11T05:02:22Z |
| format | Article |
| id | doaj.art-aee13a428b814b8ea34bd963018c8b13 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-11T05:02:22Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-aee13a428b814b8ea34bd963018c8b132023-11-17T19:09:49ZengMDPI AGEntropy1099-43002023-04-0125469610.3390/e25040696Research on Performance Degradation Estimation of Key Components of High-Speed Train Bogie Based on Multi-Task LearningJunxiao Ren0Weidong Jin1Yunpu Wu2Zhang Sun3Liang Li4School of Electrical Engineering, Southwest Jiaotong University, 999 Xi’an Road, Chengdu 611756, ChinaSchool of Electrical Engineering, Southwest Jiaotong University, 999 Xi’an Road, Chengdu 611756, ChinaSchool of Electrical Engineering and Electronic Information, Xihua University, 9999 Hongguang Road, Pidu District, Chengdu 610097, ChinaSchool of Electrical Engineering and Electronic Information, Xihua University, 9999 Hongguang Road, Pidu District, Chengdu 610097, ChinaSchool of Electrical Engineering, Southwest Jiaotong University, 999 Xi’an Road, Chengdu 611756, ChinaThe 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.https://www.mdpi.com/1099-4300/25/4/696high-speed trainsignal processingfault diagnosisperformance degradationconvolution neural networkmulti-task learning |
| spellingShingle | Junxiao Ren Weidong Jin Yunpu Wu Zhang Sun Liang Li Research on Performance Degradation Estimation of Key Components of High-Speed Train Bogie Based on Multi-Task Learning Entropy high-speed train signal processing fault diagnosis performance degradation convolution neural network 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 | high-speed train signal processing fault diagnosis performance degradation convolution neural network multi-task learning |
| url | https://www.mdpi.com/1099-4300/25/4/696 |
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