{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T02:47:00Z","timestamp":1774579620448,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2020,8,31]],"date-time":"2020-08-31T00:00:00Z","timestamp":1598832000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51675064"],"award-info":[{"award-number":["51675064"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Axle-box bearings are one of the most critical mechanical components of the high-speed train. Vibration signals collected from axle-box bearings are usually nonlinear and nonstationary, caused by the complicated operating conditions. Due to the high reliability and real-time requirement of axle-box bearing fault diagnosis for high-speed trains, the accuracy and efficiency of the bearing fault diagnosis method based on deep learning needs to be enhanced. To identify the axle-box bearing fault accurately and quickly, a novel approach is proposed in this paper using a simplified shallow information fusion-convolutional neural network (SSIF-CNN). Firstly, the time domain and frequency domain features were extracted from the training samples and testing samples before been inputted into the SSIF-CNN model. Secondly, the feature maps obtained from each hidden layer were transformed into a corresponding feature sequence by the global convolution operation. Finally, those feature sequences obtained from different layers were concatenated into one-dimensional as the fully connected layer to achieve the fault identification task. The experimental results showed that the SSIF-CNN effectively compressed the training time and improved the fault diagnosis accuracy compared with a general CNN.<\/jats:p>","DOI":"10.3390\/s20174930","type":"journal-article","created":{"date-parts":[[2020,8,31]],"date-time":"2020-08-31T08:11:19Z","timestamp":1598861479000},"page":"4930","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Fault Diagnosis for High-Speed Train Axle-Box Bearing Using Simplified Shallow Information Fusion Convolutional Neural Network"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3114-5686","authenticated-orcid":false,"given":"Honglin","family":"Luo","sequence":"first","affiliation":[{"name":"The State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China"}]},{"given":"Lin","family":"Bo","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China"}]},{"given":"Chang","family":"Peng","sequence":"additional","affiliation":[{"name":"National Engineering Laboratory for High-Speed Train, CRRC Qingdao Sifang Co. Ltd., Qingdao 266000, China"}]},{"given":"Dongming","family":"Hou","sequence":"additional","affiliation":[{"name":"School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.engfailanal.2015.02.008","article-title":"Observing early stage rail axle bearing damage","volume":"56","author":"Symonds","year":"2015","journal-title":"Eng Fail. Anal."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1016\/j.trpro.2016.05.313","article-title":"Novel Efficient Technologies in Europe for Axle Bearing Condition Monitoring \u2013 the MAXBE Project","volume":"14","author":"Vale","year":"2016","journal-title":"Transp. Res. 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