{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T15:47:01Z","timestamp":1764604021081,"version":"build-2065373602"},"reference-count":20,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2023,11,29]],"date-time":"2023-11-29T00:00:00Z","timestamp":1701216000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007694","name":"Korea Agency for Infrastructure Technology Advancement (KAIA)","doi-asserted-by":"publisher","award":["22LTSM-B156031-03"],"award-info":[{"award-number":["22LTSM-B156031-03"]}],"id":[{"id":"10.13039\/501100007694","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The condition of a railway vehicle\u2019s wheels is an essential factor for safe operation. However, the current inspection of railway vehicle wheels is limited to periodic major and minor maintenance, where physical anomalies such as vibrations and noise are visually checked by maintenance personnel and addressed after detection. As a result, there is a need for predictive technology concerning wheel conditions to prevent railway vehicle damage and potential accidents due to wheel defects. Insufficient predictive technology for railway vehicle\u2019s wheel conditions forms the background for this study. In this research, a real-time tire wear classification system for light-rail rubber tires was proposed to reduce operational costs, enhance safety, and prevent service delays. To perform real-time condition classification of rubber tires, operational data from railway vehicles, including temperature, pressure, and acceleration, were collected. These data were processed and analyzed to generate training data. A 1D\u2013CNN model was employed to classify tire conditions, and it demonstrated exceptionally high performance with a 99.4% accuracy rate.<\/jats:p>","DOI":"10.3390\/s23239501","type":"journal-article","created":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T00:34:10Z","timestamp":1701304450000},"page":"9501","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Study on Wheel Member Condition Recognition Using 1D\u2013CNN"],"prefix":"10.3390","volume":"23","author":[{"given":"Jin-Han","family":"Lee","sequence":"first","affiliation":[{"name":"Busan Transportation Corporation, Busan 47353, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8136-8254","authenticated-orcid":false,"given":"Jun-Hee","family":"Lee","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Kunsan National University, Gunsan 54150, Republic of Korea"}]},{"given":"Chang-Jae","family":"Lee","sequence":"additional","affiliation":[{"name":"Busan Transportation Corporation, Busan 47353, Republic of Korea"}]},{"given":"Seung-Lok","family":"Lee","sequence":"additional","affiliation":[{"name":"Busan Transportation Corporation, Busan 47353, Republic of Korea"}]},{"given":"Jin-Pyung","family":"Kim","sequence":"additional","affiliation":[{"name":"Global Bridge Co., Ltd., Incheon 21990, Republic of Korea"}]},{"given":"Jae-Hoon","family":"Jeong","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Kunsan National University, Gunsan 54150, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Shim, J., Koo, J., Park, Y., and Kim, J. 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