{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T15:20:16Z","timestamp":1781018416769,"version":"3.54.1"},"reference-count":19,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2021,1,20]],"date-time":"2021-01-20T00:00:00Z","timestamp":1611100800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2021,10,14]]},"abstract":"<jats:p>With the development of intelligent transportation system, the maintenance of railway turnout is an essential daily task which was required to be efficiency and automatically. This paper presents an intelligent diagnosis method based on deep learning curve segmentation and the Support Vector Machine. Firstly, we studied the curve segmentation approach of the real-time monitoring power data collected form turnout, for which is an essential step and do a great help to improve the diagnose accuracy. Then based on the well pre-processed data sets, the SVM algorithm was applied to classify the samples and report the health states of the turnout which under testing. At last, the experiments were taken on the power data curve collected from the real turnouts, during which we compared the new diagnose method with conventional ones, and the results showed that the diagnose accuracy of proposed method can averaged to 98.5%. Compared with traditional SVM based frameworks, the proposed diagnosis method dramatically improves the accuracy which is more suitable for railway turnout.<\/jats:p>","DOI":"10.3233\/jifs-189688","type":"journal-article","created":{"date-parts":[[2021,1,22]],"date-time":"2021-01-22T17:31:55Z","timestamp":1611336715000},"page":"4275-4285","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":21,"title":["An intelligent fault diagnosis method based on curve segmentation and SVM for rail transit turnout"],"prefix":"10.1177","volume":"41","author":[{"given":"Wenjiang","family":"Ji","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Xi\u2019an University of Technology, Xi\u2019an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Xi\u2019an University of Technology, Xi\u2019an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guo","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Xi\u2019an University of Technology, Xi\u2019an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Xi\u2019an University of Technology, Xi\u2019an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yichuan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Xi\u2019an University of Technology, Xi\u2019an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Long","family":"Pan","sequence":"additional","affiliation":[{"name":"Shenzhen Tencent Computer System Co., Ltd, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinhong","family":"Hei","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Xi\u2019an University of Technology, Xi\u2019an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2021,1,20]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2018.02.016"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1155\/2017\/3192967"},{"issue":"4","key":"e_1_3_2_4_2","first-page":"1","article-title":"Support vector machine","volume":"1","author":"Saunders C.","year":"2002","unstructured":"SaundersC., StitsonM.O., WestonJ., et al., Support vector machine, Computer Science1(4) (2002), 1\u201328.","journal-title":"Computer Science"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2015.10.007"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2019.106587"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.3390\/app9235129"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1177\/1748006X18823932"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2018.03.010"},{"key":"e_1_3_2_10_2","first-page":"1","article-title":"Machine health indicator construction framework for failure diagnostics and prognostics","volume":"3","author":"Atamuradov V.","year":"2020","unstructured":"AtamuradovV., MedjaherK., CamciF., et al., Machine health indicator construction framework for failure diagnostics and prognostics, Journal of Signal Processing Systems3 (2020), 1\u201319.","journal-title":"Journal of Signal Processing Systems"},{"key":"e_1_3_2_11_2","first-page":"327","article-title":"A survey on Deep Learning based bearing fault diagnosis","volume":"335","author":"Duy T.H.","year":"2019","unstructured":"DuyT.H. and HeeJ.K., A survey on Deep Learning based bearing fault diagnosis, Neuro Computing335 (2019), 327\u2013335.","journal-title":"Neuro Computing"},{"issue":"3","key":"e_1_3_2_12_2","first-page":"289","article-title":"SVM based diagnostics on railway turnouts","volume":"8","author":"Eker O.F.","year":"2012","unstructured":"EkerO.F., CamciF. and KumarU., SVM based diagnostics on railway turnouts, International Journal of Performability Engineering8(3) (2012), 289\u2013298.","journal-title":"International Journal of Performability Engineering"},{"issue":"5","key":"e_1_3_2_13_2","first-page":"74","article-title":"Research on fault feature extraction and diagnosis based on turnout action current","volume":"2019","author":"Zhou L.J.","unstructured":"ZhouL.J., DangJ.W., WangY.X., et al., Research on fault feature extraction and diagnosis based on turnout action current, Journal of Lanzhou Jiaotong University2019(5), 74\u201381.","journal-title":"Journal of Lanzhou Jiaotong University"},{"issue":"2","key":"e_1_3_2_14_2","first-page":"43","article-title":"Research on current feature identification of turnout faults based on a PCA-WT approach","volume":"37","author":"Zhang X.","year":"2018","unstructured":"ZhangX. and WeiW.J., Research on current feature identification of turnout faults based on a PCA-WT approach, Journal of Lanzhou Jiaotong University37(2) (2018), 43\u201348.","journal-title":"Journal of Lanzhou Jiaotong University"},{"issue":"8","key":"e_1_3_2_15_2","first-page":"98","article-title":"Turnout fault diagnosis method based on hidden Markov model","volume":"40","author":"Xu Q.Y.","year":"2018","unstructured":"XuQ.Y., LiuZ.T. and ZhaoH.B., Turnout fault diagnosis method based on hidden Markov model, Journal of the China Railway Society40(8) (2018), 98\u2013106.","journal-title":"Journal of the China Railway Society"},{"issue":"7","key":"e_1_3_2_16_2","first-page":"139","article-title":"Research on satellite monitoring method to the transfer force of point machine","volume":"35","author":"Wang A.","year":"2012","unstructured":"WangA., LuoS.G. and JiaoM.P., Research on satellite monitoring method to the transfer force of point machine, Modern electronics technique35(7) (2012), 139\u2013144.","journal-title":"Modern electronics technique"},{"key":"e_1_3_2_17_2","unstructured":"AnC.L. GanF.C. LuoW. et al. Method of speed-up turnout fault diagnosis usingwavelet packet energy entropy Journal of Railway Science and Engineering (2) (2015) 269\u2013274."},{"issue":"12","key":"e_1_3_2_18_2","first-page":"70","article-title":"CMOS plane based location and detection of switch gaps","volume":"38","author":"Zhong Z.W.","year":"2016","unstructured":"ZhongZ.W. and ChenJ.Y., CMOS plane based location and detection of switch gaps, Journal of the China Railway Society38(12) (2016), 70\u201375.","journal-title":"Journal of the China Railway Society"},{"issue":"8","key":"e_1_3_2_19_2","first-page":"53","article-title":"Text mining based fault diagnosis for vehicle on-board equipment of high speed railway signal system","volume":"37","author":"Zhao Y.","year":"2015","unstructured":"ZhaoY. and XuT.H., Text mining based fault diagnosis for vehicle on-board equipment of high speed railway signal system, Journal of the China Railway Society37(8) (2015), 53\u201359.","journal-title":"Journal of the China Railway Society"},{"issue":"7","key":"e_1_3_2_20_2","first-page":"80","article-title":"Research on fault feature extraction and diagnosis of railway switches based on PLSA and SVM","volume":"40","author":"Zhong Z.W.","year":"2018","unstructured":"ZhongZ.W., TangT. and WangF., Research on fault feature extraction and diagnosis of railway switches based on PLSA and SVM, Journal of the China railway society40(7) (2018), 80\u201387.","journal-title":"Journal of the China railway society"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-189688","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-189688","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-189688","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:43:19Z","timestamp":1777455799000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-189688"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,20]]},"references-count":19,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2021,10,14]]}},"alternative-id":["10.3233\/JIFS-189688"],"URL":"https:\/\/doi.org\/10.3233\/jifs-189688","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,20]]}}}