{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T20:20:35Z","timestamp":1776198035504,"version":"3.50.1"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031433658","type":"print"},{"value":"9783031433665","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-43366-5_4","type":"book-chapter","created":{"date-parts":[[2023,9,26]],"date-time":"2023-09-26T23:03:39Z","timestamp":1695769419000},"page":"62-77","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Remaining Useful Life Estimation for Railway Gearbox Bearings Using Machine Learning"],"prefix":"10.1007","author":[{"given":"Lodiana","family":"Beqiri","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zeinab","family":"Bakhshi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sasikumar","family":"Punnekkat","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Antonio","family":"Cicchetti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,27]]},"reference":[{"key":"4_CR1","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.ress.2012.03.017","volume":"104","author":"M Macchi","year":"2012","unstructured":"Macchi, M., et al.: Maintenance management of railway infrastructures based on reliability analysis. Reliab. Eng. Syst. Saf. 104, 71\u201383 (2012)","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"4_CR2","doi-asserted-by":"publisher","first-page":"109166","DOI":"10.1016\/j.measurement.2021.109166","volume":"175","author":"D Yao","year":"2021","unstructured":"Yao, D., et al.: Remaining useful life prediction of roller bearings based on improved 1D-CNN and simple recurrent unit. Measurement 175, 109166 (2021)","journal-title":"Measurement"},{"issue":"10","key":"4_CR3","doi-asserted-by":"publisher","first-page":"847","DOI":"10.3390\/en9100847","volume":"9","author":"M Cao","year":"2016","unstructured":"Cao, M., et al.: Study of wind turbine fault diagnosis based on unscented Kalman filter and SCADA data. Energies 9(10), 847 (2016)","journal-title":"Energies"},{"issue":"5","key":"4_CR4","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1049\/iet-rpg.2015.0160","volume":"10","author":"Y Qiu","year":"2016","unstructured":"Qiu, Y., et al.: Applying thermophysics for wind turbine drivetrain fault diagnosis using SCADA data. IET Renew. Power Gener. 10(5), 661\u2013668 (2016)","journal-title":"IET Renew. Power Gener."},{"issue":"3","key":"4_CR5","doi-asserted-by":"publisher","first-page":"360","DOI":"10.1002\/we.2290","volume":"22","author":"J Carroll","year":"2019","unstructured":"Carroll, J., et al.: Wind turbine gearbox failure and remaining useful life prediction using machine learning techniques. Wind Energy 22(3), 360\u2013375 (2019)","journal-title":"Wind Energy"},{"key":"4_CR6","doi-asserted-by":"crossref","unstructured":"Zang, Y., et al.: Hybrid remaining useful life prediction method. A case study on railway D-cables. Reliab. Eng. Syst. Saf. 213, 107746 (2021)","DOI":"10.1016\/j.ress.2021.107746"},{"key":"4_CR7","doi-asserted-by":"publisher","first-page":"107788","DOI":"10.1016\/j.measurement.2020.107788","volume":"159","author":"M Hou","year":"2020","unstructured":"Hou, M., Pi, D., Li, B.: Similarity-based deep learning approach for remaining useful life prediction. Measurement 159, 107788 (2020)","journal-title":"Measurement"},{"issue":"6","key":"4_CR8","first-page":"574","volume":"12","author":"A Zaher","year":"2009","unstructured":"Zaher, A., et al.: Online wind turbine fault detection through automated SCADA data analysis. Wind Energy Int. J. Prog. Appl. Wind Power Convers. Technol. 12(6), 574\u2013593 (2009)","journal-title":"Wind Energy Int. J. Prog. Appl. Wind Power Convers. Technol."},{"issue":"5","key":"4_CR9","doi-asserted-by":"publisher","first-page":"728","DOI":"10.1002\/we.1521","volume":"16","author":"Y Feng","year":"2013","unstructured":"Feng, Y., et al.: Monitoring wind turbine gearboxes. Wind Energy 16(5), 728\u2013740 (2013)","journal-title":"Wind Energy"},{"issue":"8","key":"4_CR10","doi-asserted-by":"publisher","first-page":"1421","DOI":"10.1002\/we.2102","volume":"20","author":"P Bangalore","year":"2017","unstructured":"Bangalore, P., et al.: An artificial neural network-based condition monitoring method for wind turbines, with application to the monitoring of the gearbox. Wind Energy 20(8), 1421\u20131438 (2017)","journal-title":"Wind Energy"},{"issue":"14","key":"4_CR11","doi-asserted-by":"publisher","first-page":"3092","DOI":"10.3390\/s19143092","volume":"19","author":"F Elasha","year":"2019","unstructured":"Elasha, F., et al.: Prognosis of a wind turbine gearbox bearing using supervised machine learning. Sensors 19(14), 3092 (2019)","journal-title":"Sensors"},{"issue":"2","key":"4_CR12","first-page":"689","volume":"28","author":"W Shao","year":"2021","unstructured":"Shao, W., Hao, Y., et al.: Study on preventive maintenance strategies of filling equipment based on reliability-cantered maintenance. Tehni\u010dki vjesnik 28(2), 689\u2013697 (2021)","journal-title":"Tehni\u010dki vjesnik"},{"issue":"3","key":"4_CR13","doi-asserted-by":"publisher","first-page":"812","DOI":"10.1109\/TII.2014.2349359","volume":"11","author":"GA Susto","year":"2014","unstructured":"Susto, G.A., et al.: Machine learning for predictive maintenance: a multiple classifier approach. IEEE Trans. Ind. Inform. 11(3), 812\u2013820 (2014)","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"11","key":"4_CR14","doi-asserted-by":"publisher","first-page":"425","DOI":"10.3390\/geosciences10110425","volume":"10","author":"J Xie","year":"2020","unstructured":"Xie, J., et al.: Systematic literature review on data-driven models for predictive maintenance of railway track: implications in geotechnical engineering. Geosciences 10(11), 425 (2020)","journal-title":"Geosciences"},{"issue":"4","key":"4_CR15","doi-asserted-by":"publisher","first-page":"041403","DOI":"10.1115\/1.3209132","volume":"131","author":"F Sadeghi","year":"2009","unstructured":"Sadeghi, F., et al.: A review of rolling contact fatigue. J. Tribol. 131(4), 041403 (2009)","journal-title":"J. Tribol."},{"issue":"1","key":"4_CR16","doi-asserted-by":"publisher","first-page":"14","DOI":"10.3390\/lubricants11010014","volume":"11","author":"H Peng","year":"2022","unstructured":"Peng, H., et al.: A review of research on wind turbine bearings\u2019 failure analysis and fault diagnosis. Lubricants 11(1), 14 (2022)","journal-title":"Lubricants"},{"issue":"3","key":"4_CR17","doi-asserted-by":"publisher","first-page":"1468","DOI":"10.1109\/TMECH.2020.2978136","volume":"25","author":"M Rezamand","year":"2020","unstructured":"Rezamand, M., et al.: An integrated feature-based failure prognosis method for wind turbine bearings. IEEE\/ASME Trans. Mechatron. 25(3), 1468\u20131478 (2020)","journal-title":"IEEE\/ASME Trans. Mechatron."},{"issue":"1","key":"4_CR18","doi-asserted-by":"publisher","first-page":"32","DOI":"10.3390\/en10010032","volume":"10","author":"W Teng","year":"2016","unstructured":"Teng, W., et al.: Prognosis of the remaining useful life of bearings in a wind turbine gearbox. Energies 10(1), 32 (2016)","journal-title":"Energies"},{"issue":"7","key":"4_CR19","doi-asserted-by":"publisher","first-page":"5864","DOI":"10.1109\/TIE.2017.2767551","volume":"65","author":"M Elforjani","year":"2017","unstructured":"Elforjani, M., Shanbr, S.: Prognosis of bearing acoustic emission signals using supervised machine learning. IEEE Trans. Ind. Electron. 65(7), 5864\u20135871 (2017)","journal-title":"IEEE Trans. Ind. Electron."},{"key":"4_CR20","doi-asserted-by":"publisher","first-page":"106024","DOI":"10.1016\/j.cie.2019.106024","volume":"137","author":"TP Carvalho","year":"2019","unstructured":"Carvalho, T.P., et al.: A systematic literature review of machine learning methods applied to predictive maintenance. Comput. Ind. Eng. 137, 106024 (2019)","journal-title":"Comput. Ind. Eng."},{"key":"4_CR21","doi-asserted-by":"crossref","unstructured":"Amruthnath, N., Gupta, T.: A research study on unsupervised machine learning algorithms for early fault detection in predictive maintenance. In: 2018 5th International Conference on Industrial Engineering and Applications (ICIEA), pp. 355\u2013361. IEEE (2018)","DOI":"10.1109\/IEA.2018.8387124"},{"key":"4_CR22","doi-asserted-by":"crossref","unstructured":"Amruthnath, N., Gupta, T.: Fault class prediction in unsupervised learning using model-based clustering approach. In: 2018 International Conference on Information and Computer Technologies (ICICT). IEEE (2018)","DOI":"10.1109\/INFOCT.2018.8356831"},{"key":"4_CR23","doi-asserted-by":"publisher","first-page":"1795","DOI":"10.1007\/s10845-017-1357-8","volume":"30","author":"P Kundu","year":"2019","unstructured":"Kundu, P., Chopra, S., Lad, B.K.: Multiple failure behaviors identification and remaining useful life prediction of ball bearings. J. Intell. Manuf. 30, 1795\u20131807 (2019)","journal-title":"J. Intell. Manuf."},{"issue":"2","key":"4_CR24","first-page":"695","volume":"17","author":"S Hong","year":"2015","unstructured":"Hong, S., et al.: Bearing remaining life prediction using gaussian process regression with composite Kernel functions. J. Vibroengineering 17(2), 695\u2013704 (2015)","journal-title":"J. Vibroengineering"},{"issue":"12","key":"4_CR25","doi-asserted-by":"publisher","first-page":"7762","DOI":"10.1109\/TIE.2015.2455055","volume":"62","author":"N Li","year":"2015","unstructured":"Li, N., et al.: An improved exponential model for predicting remaining useful life of rolling element bearings. IEEE Trans. Ind. Electron. 62(12), 7762\u20137773 (2015)","journal-title":"IEEE Trans. Ind. Electron."},{"key":"4_CR26","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.ress.2018.02.003","volume":"184","author":"W Ahmad","year":"2019","unstructured":"Ahmad, W., et al.: A reliable technique for remaining useful life estimation of rolling element bearings using dynamic regression models. Reliab. Eng. Syst. Saf. 184, 67\u201376 (2019)","journal-title":"Reliab. Eng. Syst. Saf."},{"issue":"1","key":"4_CR27","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1109\/TIM.2010.2047662","volume":"60","author":"HM Hashemian","year":"2010","unstructured":"Hashemian, H.M.: State-of-the-art predictive maintenance techniques. IEEE Trans. Instrum. Meas. 60(1), 226\u2013236 (2010)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"4_CR28","doi-asserted-by":"crossref","unstructured":"Butte, S., Prashanth, A.R., Patil, S.: Machine learning based predictive maintenance strategy: a super learning approach with deep neural networks. In: 2018 IEEE Workshop on Microelectronics and Electron Devices (WMED), pp. 1\u20135. IEEE (2018)","DOI":"10.1109\/WMED.2018.8360836"},{"issue":"6","key":"4_CR29","doi-asserted-by":"publisher","first-page":"581","DOI":"10.11648\/j.ajtas.20150406.30","volume":"4","author":"GD Wambui","year":"2015","unstructured":"Wambui, G.D., Waititu, G.A., Wanjoya, A.: The power of the pruned exact linear time (PELT) test in multiple changepoint detection. Am. J. Theor. Appl. Stat. 4(6), 581\u2013586 (2015)","journal-title":"Am. J. Theor. Appl. Stat."},{"issue":"4","key":"4_CR30","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1108\/13552511111180186","volume":"17","author":"M-A Mortada","year":"2011","unstructured":"Mortada, M.-A., Yacout, S., Lakis, A.: Diagnosis of rotor bearings using logical analysis of data. J. Qual. Maintenance Eng. 17(4), 371\u2013397 (2011)","journal-title":"J. Qual. Maintenance Eng."},{"key":"4_CR31","doi-asserted-by":"publisher","unstructured":"Wohlin, C., et al.: Experimentation in Software Engineering. Springer, Cham (2012). https:\/\/doi.org\/10.1007\/978-3-642-29044-2","DOI":"10.1007\/978-3-642-29044-2"}],"container-title":["Lecture Notes in Computer Science","Reliability, Safety, and Security of Railway Systems. Modelling, Analysis, Verification, and Certification"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43366-5_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,26]],"date-time":"2023-09-26T23:03:50Z","timestamp":1695769430000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43366-5_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031433658","9783031433665"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43366-5_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"27 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"RSSRail","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Reliability, Safety, and Security of Railway Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Berlin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"rssrail2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/rssr2023.ebuef.de\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easy Chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"25","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"13","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"52% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1.85","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}