{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T12:42:13Z","timestamp":1777293733456,"version":"3.51.4"},"reference-count":26,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,4,20]],"date-time":"2023-04-20T00:00:00Z","timestamp":1681948800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["62203368"],"award-info":[{"award-number":["62203368"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["2023NSFSC1440"],"award-info":[{"award-number":["2023NSFSC1440"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018542","name":"Sichuan Natural Science Foundation","doi-asserted-by":"publisher","award":["62203368"],"award-info":[{"award-number":["62203368"]}],"id":[{"id":"10.13039\/501100018542","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018542","name":"Sichuan Natural Science Foundation","doi-asserted-by":"publisher","award":["2023NSFSC1440"],"award-info":[{"award-number":["2023NSFSC1440"]}],"id":[{"id":"10.13039\/501100018542","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/e25040696","type":"journal-article","created":{"date-parts":[[2023,4,21]],"date-time":"2023-04-21T03:01:48Z","timestamp":1682046108000},"page":"696","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Research on Performance Degradation Estimation of Key Components of High-Speed Train Bogie Based on Multi-Task Learning"],"prefix":"10.3390","volume":"25","author":[{"given":"Junxiao","family":"Ren","sequence":"first","affiliation":[{"name":"School of Electrical Engineering, Southwest Jiaotong University, 999 Xi\u2019an Road, Chengdu 611756, China"}]},{"given":"Weidong","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Southwest Jiaotong University, 999 Xi\u2019an Road, Chengdu 611756, China"},{"name":"China-ASEAN International Joint Laboratory of Integrated Transportation, Nanning University, 8 Longting Road, Nanning 541699, China"}]},{"given":"Yunpu","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Electronic Information, Xihua University, 9999 Hongguang Road, Pidu District, Chengdu 610097, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3176-6351","authenticated-orcid":false,"given":"Zhang","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Electronic Information, Xihua University, 9999 Hongguang Road, Pidu District, Chengdu 610097, China"}]},{"given":"Liang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Southwest Jiaotong University, 999 Xi\u2019an Road, Chengdu 611756, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3923","DOI":"10.1007\/s10489-019-01483-8","article-title":"A multi-perspective architecture for high-speed train fault diagnosis based on variational mode decomposition and enhanced multi-scale structure","volume":"49","author":"Wu","year":"2019","journal-title":"Appl. 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