{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T16:26:10Z","timestamp":1783009570551,"version":"3.54.5"},"reference-count":22,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2023,10,13]],"date-time":"2023-10-13T00:00:00Z","timestamp":1697155200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Land, Infrastructure and Transport","award":["22LTSM-B156031-03"],"award-info":[{"award-number":["22LTSM-B156031-03"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The wheels of railway vehicles are of paramount importance in relation to railroad operations and safety. Currently, the management of railway vehicle wheels is restricted to post-event inspections of the wheels whenever physical phenomena, such as abnormal vibrations and noise, occur during the operation of railway vehicles. To address this issue, this paper proposes a method for predicting abnormalities in railway wheels in advance and enhancing the learning and prediction performance of machine learning algorithms. Data were collected during the operation of Line 4 of the Busan Metro in South Korea by directly attaching sensors to the railway vehicles. Through the analysis of key factors in the collected data, factors that can be used for tire condition classification were derived. Additionally, through data distribution analysis and correlation analysis, factors for classifying tire conditions were identified. As a result, it was determined that the z-axis of acceleration has a significant impact, and machine learning techniques such as SVM (Linear Kernel, RBF Kernel) and Random Forest were utilized based on acceleration data to classify tire conditions into in-service and defective states. The SVM (Linear Kernel) yielded the highest recognition rate at 98.70%.<\/jats:p>","DOI":"10.3390\/s23208455","type":"journal-article","created":{"date-parts":[[2023,10,14]],"date-time":"2023-10-14T14:59:59Z","timestamp":1697295599000},"page":"8455","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Study on Wheel Member Condition Recognition Using Machine Learning (Support Vector Machine)"],"prefix":"10.3390","volume":"23","author":[{"given":"Jin-Han","family":"Lee","sequence":"first","affiliation":[{"name":"Busan Transportation Corporation, Busan 47353, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"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"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kwang-Su","family":"Yun","sequence":"additional","affiliation":[{"name":"Busan Transportation Corporation, Busan 47353, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3004-1001","authenticated-orcid":false,"given":"Han Byeol","family":"Bae","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Kunsan National University, Gunsan 54150, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6189-0268","authenticated-orcid":false,"given":"Sun Young","family":"Kim","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Kunsan National University, Gunsan 54150, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6371-8007","authenticated-orcid":false,"given":"Jae-Hoon","family":"Jeong","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Kunsan National University, Gunsan 54150, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin-Pyung","family":"Kim","sequence":"additional","affiliation":[{"name":"Global Bridge Co., Ltd., Incheon 21990, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Shim, J., Koo, J., Park, Y., and Kim, J. 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