{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T20:56:25Z","timestamp":1762376185493,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2017,1,29]],"date-time":"2017-01-29T00:00:00Z","timestamp":1485648000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Small &amp; Medium Business Administration","award":["S2312692"],"award-info":[{"award-number":["S2312692"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Detecting replacement conditions of railway point machines is important to simultaneously satisfy the budget-limit and train-safety requirements. In this study, we consider classification of the subtle differences in the aging effect\u2014using electric current shape analysis\u2014for the purpose of replacement condition detection of railway point machines. After analyzing the shapes of after-replacement data and then labeling the shapes of each before-replacement data, we can derive the criteria that can handle the subtle differences between \u201cdoes-not-need-to-be-replaced\u201d and \u201cneeds-to-be-replaced\u201d shapes. On the basis of the experimental results with in-field replacement data, we confirmed that the proposed method could detect the replacement conditions with acceptable accuracy, as well as provide visual interpretability of the criteria used for the time-series classification.<\/jats:p>","DOI":"10.3390\/s17020263","type":"journal-article","created":{"date-parts":[[2017,1,30]],"date-time":"2017-01-30T11:36:30Z","timestamp":1485776190000},"page":"263","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Replacement Condition Detection of Railway Point Machines Using an Electric Current Sensor"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6470-3341","authenticated-orcid":false,"given":"Jaewon","family":"Sa","sequence":"first","affiliation":[{"name":"Department of Computer and Information Science, Korea University, Sejong 30019, Korea"}]},{"given":"Younchang","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Science, Korea University, Sejong 30019, Korea"}]},{"given":"Yongwha","family":"Chung","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Science, Korea University, Sejong 30019, Korea"}]},{"given":"Hee-Young","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Applied Statistics, Korea University, Sejong 30019, Korea"}]},{"given":"Daihee","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Science, Korea University, Sejong 30019, Korea"}]},{"given":"Sukhan","family":"Yoon","sequence":"additional","affiliation":[{"name":"Sehwa R&amp;D Center, Techno 2-ro, Yuseong-gu, Daejeon 34026, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2017,1,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2501","DOI":"10.1109\/TIE.2016.2522944","article-title":"Diagnosis and Prognosis for Complicated Industrial Systems\u2014Part 1","volume":"63","author":"Yin","year":"2016","journal-title":"IEEE Trans. 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