{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T16:22:19Z","timestamp":1778862139631,"version":"3.51.4"},"reference-count":30,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,2,22]],"date-time":"2022-02-22T00:00:00Z","timestamp":1645488000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"publisher","award":["NSFC: 52175465"],"award-info":[{"award-number":["NSFC: 52175465"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The axle box in the bogie system of subway trains is a key component connecting primary damper and the axle. In order to extract deep features and large-scale fault features for rapid diagnosis, a novel fault reconstruction characteristics classification method based on deep residual network with a multi-scale stacked receptive field for rolling bearings of a subway train axle box is proposed. Firstly, multi-layer stacked convolutional kernels and methods to insert them into ultra-deep residual networks are developed. Then, the original vibration signals of four fault characteristics acquired are reconstructed with a Gramian angular summation field and trainable large-scale 2D time-series images are obtained. In the end, the experimental results show that ResNet-152-MSRF has a low complexity of network structure, less trainable parameters than general convolutional neural networks, and no significant increase in network parameters and calculation time after embedding multi-layer stacked convolutional kernels. Moreover, there is a significant improvement in accuracy compared to lower depths, and a slight improvement in accuracy compared to networks than unembedded multi-layer stacked convolutional kernels.<\/jats:p>","DOI":"10.3390\/s22051705","type":"journal-article","created":{"date-parts":[[2022,2,22]],"date-time":"2022-02-22T22:35:00Z","timestamp":1645569300000},"page":"1705","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Bearing Fault Reconstruction Diagnosis Method Based on ResNet-152 with Multi-Scale Stacked Receptive Field"],"prefix":"10.3390","volume":"22","author":[{"given":"Hu","family":"Yu","sequence":"first","affiliation":[{"name":"School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing 211816, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5427-6550","authenticated-orcid":false,"given":"Xiaodong","family":"Miao","sequence":"additional","affiliation":[{"name":"School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing 211816, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hua","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing 211816, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1388","DOI":"10.1109\/TITS.2020.2970000","article-title":"Adaptive Iterative Learning Control for Subway Trains Using Multiple-Point-Mass Dynamic Model under Speed Constraint","volume":"22","author":"Liu","year":"2021","journal-title":"IEEE Trans. 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