{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T03:17:26Z","timestamp":1781234246244,"version":"3.54.1"},"reference-count":21,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2019,11,25]],"date-time":"2019-11-25T00:00:00Z","timestamp":1574640000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>A fault diagnosis of a train door system is carried out using the motor current signal that operates the door. A test rig is prepared, in which various fault modes are examined by applying extreme conditions, as well as the natural and artificial wears of critical components. Two approaches are undertaken toward the fault classification for comparative purposes: one is the traditional feature-based method that requires several steps for the processing features such as signal segmentation, the extraction of time-domain features, selection by Fisher\u2019s discrimination, and K-nearest neighbor. The other is the deep learning approach by employing the convolutional neural network (CNN) to skip the hand-crafted features extraction process. In the traditional approach, good accuracy is found only after the current signal is segmented into the three velocity regimes, which enhances the discrimination capability. In the CNN, superior accuracy is obtained even by the original raw signal, which is more convenient in terms of implementation. However, in view of practical applications, the traditional approach is more useful in that the features processing can be easily applied to assess the health state of each fault and monitor the progression over time in the real operation, which is not enabled by the deep learning approach.<\/jats:p>","DOI":"10.3390\/s19235160","type":"journal-article","created":{"date-parts":[[2019,11,25]],"date-time":"2019-11-25T11:12:21Z","timestamp":1574680341000},"page":"5160","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["A Comparative Study of Fault Diagnosis for Train Door System: Traditional versus Deep Learning Approaches"],"prefix":"10.3390","volume":"19","author":[{"given":"Seokju","family":"Ham","sequence":"first","affiliation":[{"name":"Department of Aerospace &amp; Mechanical Engineering, Korea Aerospace University, Goyang-City 10540, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seok-Youn","family":"Han","sequence":"additional","affiliation":[{"name":"Urban Transit Research Group, Korea Railroad Research Institute, Uiwang-City 16105, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1205-7736","authenticated-orcid":false,"given":"Seokgoo","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Aerospace &amp; Mechanical Engineering, Korea Aerospace University, Goyang-City 10540, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1081-3067","authenticated-orcid":false,"given":"Hyung Jun","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Aerospace &amp; Mechanical Engineering, Korea Aerospace University, Goyang-City 10540, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kee-Jun","family":"Park","sequence":"additional","affiliation":[{"name":"Urban Transit Research Group, Korea Railroad Research Institute, Uiwang-City 16105, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joo-Ho","family":"Choi","sequence":"additional","affiliation":[{"name":"School of Aerospace &amp; Mechanical Engineering, Korea Aerospace University, Goyang-City 10540, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,25]]},"reference":[{"key":"ref_1","unstructured":"Bai, H. (2010). A Generic Fault Detection and Diagnosis Approach for Pneumatic and Electric Driven Railway Assets. [Ph.D. Dissertation, University of Birmingham]."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Cauffriez, L., Copin, R., Caouder, N., Loslever, P., and Turgis, F. (2010). Design of a testing bench for simulating tightened-up operating conditions of train\u2019s passenger access. Reliability, Risk and Safety: Theory and Applications, Taylor & Francis Group.","DOI":"10.1201\/9780203859759.ch312"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"340","DOI":"10.2174\/1874155X01408010340","article-title":"Research on urban rail train passenger door system fault diagnosis using pca and rough set","volume":"8","author":"Lin","year":"2014","journal-title":"Open Mech. Eng. J."},{"key":"ref_4","first-page":"271","article-title":"Use of parameter estimation for the detection and diagnosis of faults on electric train door systems","volume":"223","author":"Dassanayake","year":"2009","journal-title":"Proc. Inst. Mech. Eng. Part O J. Risk Reliab."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.conengprac.2015.12.019","article-title":"Bond graph modeling for fault detection and isolation of a train door mechatronic system","volume":"49","author":"Cauffriez","year":"2016","journal-title":"Control Eng. Pract."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Sun, Y., Xie, G., Cao, Y., and Wen, T. (2019). Strategy for fault diagnosis on train plug doors using audio sensors. Sensors, 19.","DOI":"10.3390\/s19010003"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Long, J., Zhu, S., Zhang, S., Xu, Z., Han, G., and Lu, N. (2018, January 9\u201311). Health monitoring of railway vehicle door system based on movement resistance analysis. Proceedings of the 2018 Chinese Control and Decision Conference (CCDC), Shenyang, China.","DOI":"10.1109\/CCDC.2018.8408292"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"912","DOI":"10.1115\/1.1962019","article-title":"Degradation assessment and fault modes classification using logistic regression","volume":"127","author":"Yan","year":"2005","journal-title":"J. Manuf. Sci. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Alessi, A., La-Cascia, P., Lamoureux, B., Pugnaloni, M., and Dersin, P. (2016, January 3\u20136). Health Assessment of Railway Turnouts: A Case Study. Proceedings of the European Conference of the Prognostics and Health Management society, Dresden, Germany.","DOI":"10.36001\/phme.2016.v3i1.1641"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Lee, J., Choi, H., Park, D., Chung, Y., Kim, H.Y., and Yoon, S. (2016). Fault detection and diagnosis of railway point machines by sound analysis. Sensors, 16.","DOI":"10.3390\/s16040549"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1049\/el.2016.0206","article-title":"Fault diagnosis of railway point machines using dynamic time warping","volume":"52","author":"Kim","year":"2016","journal-title":"Electron. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1177\/1687814018811402","article-title":"Two-stage turnout fault diagnosis based on similarity function and fuzzy c-means","volume":"10","author":"Huang","year":"2018","journal-title":"Adv. Mech. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"106839","DOI":"10.1016\/j.csda.2019.106839","article-title":"Benchmark for filter methods for feature selection in high-dimensional classification data","volume":"143","author":"Bommert","year":"2020","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.patrec.2018.05.018","article-title":"A review of Convolutional-Neural-Network-based action recognition","volume":"118","author":"Yao","year":"2019","journal-title":"Pattern Recognit. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.ymssp.2018.05.050","article-title":"Deep learning and its applications to machine health monitoring","volume":"115","author":"Zhao","year":"2019","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhang, P., Zhang, G., Dong, W., Sun, X., and Ji, X. (2018, January 6\u20137). Fault diagnosis of high-speed railway turnout based on convolutional neural network. Proceedings of the 2018 24th International Conference on Automation and Computing (ICAC), Newcastle upon Tyne, UK.","DOI":"10.23919\/IConAC.2018.8749078"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"7067","DOI":"10.1109\/TIE.2016.2582729","article-title":"Real-time motor fault detection by 1-D convolutional neural networks","volume":"63","author":"Ince","year":"2016","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Lei, Y. (2016). Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery, Butterworth-Heinemann.","DOI":"10.1016\/B978-0-12-811534-3.00006-8"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Lee, J., Jin, C., Liu, Z., and Ardakani, H.D. (2017). Introduction to data-driven methodologies for prognostics and health management. Probabilistic Prognostics and Health Management of Energy Systems, Springer.","DOI":"10.1007\/978-3-319-55852-3_2"},{"key":"ref_20","unstructured":"Bishop, C.M. (2006). Pattern Recognition and Machine Learning, Springer."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1401","DOI":"10.1177\/1475921718805683","article-title":"Convolutional neural network for gear fault diagnosis based on signal segmentation approach","volume":"18","author":"Kim","year":"2019","journal-title":"Struct. Health Monit."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/23\/5160\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:37:22Z","timestamp":1760189842000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/23\/5160"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,25]]},"references-count":21,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["s19235160"],"URL":"https:\/\/doi.org\/10.3390\/s19235160","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,25]]}}}