{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T06:23:55Z","timestamp":1743143035192,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031100468"},{"type":"electronic","value":"9783031100475"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-10047-5_52","type":"book-chapter","created":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T22:02:49Z","timestamp":1656712969000},"page":"587-598","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Deep Learning Approach for\u00a0Data-Driven Predictive Maintenance of\u00a0Rolling Bearings"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1712-5454","authenticated-orcid":false,"given":"Domicio","family":"Neto","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4622-474X","authenticated-orcid":false,"given":"Jorge","family":"Henriques","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0937-4044","authenticated-orcid":false,"given":"Paulo","family":"Gil","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9396-1211","authenticated-orcid":false,"given":"C\u00e9sar","family":"Teixeira","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1824-1075","authenticated-orcid":false,"given":"Alberto","family":"Cardoso","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,7,2]]},"reference":[{"issue":"5","key":"52_CR1","doi-asserted-by":"publisher","first-page":"1095","DOI":"10.1177\/1475921717736226","volume":"17","author":"D An","year":"2018","unstructured":"An, D., Choi, J.H., Kim, N.H.: Remaining useful life prediction of rolling element bearings using degradation feature based on amplitude decrease at specific frequencies. Struct Health Monit. Int. J. 17(5), 1095\u20131109 (2018)","journal-title":"Struct Health Monit. Int. J."},{"issue":"18","key":"52_CR2","doi-asserted-by":"publisher","first-page":"3224","DOI":"10.1177\/0954406217734885","volume":"232","author":"M Behzad","year":"2018","unstructured":"Behzad, M., Arghand, H.A., Bastami, A.R.: Remaining useful life prediction of ball-bearings based on high-frequency vibration features. Proc. Instit. Mech. Eng. Part C-J. Mech. Eng. Sci. 232(18), 3224\u20133234 (2018)","journal-title":"Proc. Instit. Mech. Eng. Part C-J. Mech. Eng. Sci."},{"key":"52_CR3","doi-asserted-by":"crossref","unstructured":"Cao, Q., et al.: KSPMI: a knowledge-based system for predictive maintenance in industry 4.0. Robot. Comput.-Integr. Manuf. 74, 102281 (2022)","DOI":"10.1016\/j.rcim.2021.102281"},{"key":"52_CR4","doi-asserted-by":"crossref","unstructured":"Carvalho, T.P., Soares, F.A., Vita, R., Francisco, R.D.P., Basto, J.P., Alcal\u00e1, S.G.: A systematic literature review of machine learning methods applied to predictive maintenance. Comput. Ind. Eng. 137, 106024 (2019)","DOI":"10.1016\/j.cie.2019.106024"},{"key":"52_CR5","doi-asserted-by":"crossref","unstructured":"Coble, J., Wesley Hines, J.: Identifying optimal prognostic parameters from data: a genetic algorithms approach. In: Annual Conference of the Prognostics and Health Management Society, PHM 2009, pp. 1\u201311 (2009)","DOI":"10.1109\/PHM.2008.4711456"},{"issue":"4","key":"52_CR6","doi-asserted-by":"publisher","first-page":"799","DOI":"10.21629\/JSEE.2019.04.17","volume":"30","author":"Y Li","year":"2019","unstructured":"Li, Y., Si, S., Liu, Z., Liang, X.: Review of local mean decomposition and its application in fault diagnosis of rotating machinery. J. Syst. Eng. Electron. 30(4), 799\u2013814 (2019)","journal-title":"J. Syst. Eng. Electron."},{"key":"52_CR7","doi-asserted-by":"publisher","first-page":"5470","DOI":"10.1016\/j.ijhydene.2018.10.042","volume":"44","author":"J Liu","year":"2019","unstructured":"Liu, J., Li, Q., Chen, W., Yan, Y., Qiu, Y., Cao, T.: Remaining useful life prediction of PEMFC based on long short-term memory recurrent neural networks. Int. J. Hydrogen Energy 44, 5470\u20135480 (2019)","journal-title":"Int. J. Hydrogen Energy"},{"issue":"12","key":"52_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1177\/1687814018817184","volume":"10","author":"W Mao","year":"2018","unstructured":"Mao, W., He, J., Tang, J., Li, Y.: Predicting remaining useful life of rolling bearings based on deep feature representation and long short-term memory neural network. Adv. Mech. Eng. 10(12), 1\u201318 (2018)","journal-title":"Adv. Mech. Eng."},{"key":"52_CR9","unstructured":"Mazenko, E.: Preventive vs. Predictive Maintenance: Pros and Cons (2016). https:\/\/www.betterbuys.com\/cmms\/preventive-vs-predictive-maintenance\/"},{"key":"52_CR10","volume-title":"An Introduction to Predictive Maintenance","author":"RK Mobley","year":"2002","unstructured":"Mobley, R.K.: An Introduction to Predictive Maintenance, 2nd edn. Butterworth Heinemann, Oxford (2002)","edition":"2"},{"key":"52_CR11","unstructured":"Nectoux, P., et al.: PRONOSTIA: an experimental platform for bearings accelerated degradation tests. In: IEEE International Conference on Prognostics and Health Management, PHM 2012, pp. 1\u20138 (2012). http:\/\/hal-obspm.ccsd.cnrs.fr\/UNIV-BM\/hal-00719503"},{"key":"52_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.apacoust.2017.01.005","volume":"120","author":"L Saidi","year":"2017","unstructured":"Saidi, L., Ben Ali, J., Bechhoefer, E., Benbouzid, M.: Wind turbine high-speed shaft bearings health prognosis through a spectral Kurtosis-derived indices and SVR. Appl. Acoust. 120, 1\u20138 (2017)","journal-title":"Appl. Acoust."},{"key":"52_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2020.103380","volume":"125","author":"S Schwendemann","year":"2021","unstructured":"Schwendemann, S., Amjad, Z., Sikora, A.: A survey of machine-learning techniques for condition monitoring and predictive maintenance of bearings in grinding machines. Comput. Ind. 125, 103380 (2021)","journal-title":"Comput. Ind."},{"key":"52_CR14","first-page":"2951","volume":"4","author":"J Snoek","year":"2012","unstructured":"Snoek, J., Larochelle, H., Adams, R.P.: Practical Bayesian optimization of machine learning algorithms. Adv. Neural. Inf. Process. Syst. 4, 2951\u20132959 (2012)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"September","key":"52_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2021.107413","volume":"95","author":"H Sun","year":"2021","unstructured":"Sun, H., Xia, M., Hu, Y., Lu, S., Liu, Y., Wang, Q.: A new sorting feature-based temporal convolutional network for remaining useful life prediction of rotating machinery. Comput. Electr. Eng. 95(September), 107413 (2021)","journal-title":"Comput. Electr. Eng."},{"key":"52_CR16","doi-asserted-by":"crossref","unstructured":"Thomas, D.S.: The Costs and Benefits of Advanced Maintenance in Manufacturing. National Institute of Standards and Technology (2018)","DOI":"10.6028\/NIST.AMS.100-18"},{"key":"52_CR17","doi-asserted-by":"crossref","unstructured":"Wen, Y., Fashiar Rahman, M., Xu, H., Tseng, T.L.B.: Recent advances and trends of predictive maintenance from data-driven machine prognostics perspective. Meas. J. Int. Meas. Confed. 187, 110276 (2022)","DOI":"10.1016\/j.measurement.2021.110276"},{"key":"52_CR18","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/j.isatra.2019.07.004","volume":"97","author":"J Wu","year":"2020","unstructured":"Wu, J., Hua, K., Cheng, Y., Zhu, H., Shao, X., Wang, Y.H.: Data-driven remaining useful life prediction via multiple sensor signals and deep long short-term memory neural network. ISA Trans. 97, 241\u2013250 (2020)","journal-title":"ISA Trans."},{"key":"52_CR19","doi-asserted-by":"publisher","first-page":"106617","DOI":"10.1016\/j.ymssp.2020.106617","volume":"139","author":"L Xu","year":"2020","unstructured":"Xu, L., Pennacchi, P., Chatterton, S.: Remaining useful life prediction of PEMFC based on long short-term memory recurrent neural networks. Mech. Syst. Sig. Process. 139, 106617 (2020)","journal-title":"Mech. Syst. Sig. Process."},{"key":"52_CR20","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1016\/j.measurement.2019.06.004","volume":"146","author":"S Zhao","year":"2019","unstructured":"Zhao, S., Zhang, Y., Wang, S., Zhou, B., Cheng, C.: A recurrent neural network approach for remaining useful life prediction utilizing a novel trend features construction method. Measurement 146, 279\u2013288 (2019)","journal-title":"Measurement"},{"key":"52_CR21","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/j.mechmachtheory.2015.04.009","volume":"91","author":"Z Zheng","year":"2015","unstructured":"Zheng, Z., Jiang, W., Wang, Z., Zhu, Y., Yang, K.: Gear fault diagnosis method based on local mean decomposition and generalized morphological fractal dimensions. Mech. Mach. Theory 91, 151\u2013167 (2015)","journal-title":"Mech. Mach. Theory"},{"key":"52_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2020.106889","volume":"150","author":"T Zonta","year":"2020","unstructured":"Zonta, T., da Costa, C.A., da Rosa Righi, R., de Lima, M.J., da Trindade, E.S., Li, G.P.: Predictive maintenance in the Industry 4.0: a systematic literature review. Comput. Ind. Eng. 150, 106889 (2020)","journal-title":"Comput. Ind. Eng."}],"container-title":["Lecture Notes in Electrical Engineering","CONTROLO 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-10047-5_52","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,28]],"date-time":"2024-09-28T09:17:13Z","timestamp":1727515033000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-10047-5_52"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031100468","9783031100475"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-10047-5_52","relation":{},"ISSN":["1876-1100","1876-1119"],"issn-type":[{"type":"print","value":"1876-1100"},{"type":"electronic","value":"1876-1119"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"2 July 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CONTROLO","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"APCA International Conference on Automatic Control and Soft Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Caparica","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 July 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 July 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"controlo2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/controlo2022.deec.fct.unl.pt\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}