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dc.contributor.author | Garcia, Emilia | es_ES |
dc.contributor.author | Araujo, Angelo | es_ES |
dc.contributor.author | Palanca Cámara, Javier | es_ES |
dc.contributor.author | Giret Boggino, Adriana Susana | es_ES |
dc.contributor.author | Julian Inglada, Vicente Javier | es_ES |
dc.contributor.author | Botti, V. | es_ES |
dc.date.accessioned | 2021-03-25T07:19:08Z | |
dc.date.available | 2021-03-25T07:19:08Z | |
dc.date.issued | 2019-10-04 | es_ES |
dc.identifier.isbn | 978-3-030-27476-4 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/164235 | |
dc.description | comprobación paso "titulo publicación " - Service Oriented, Holonic and Multi-agent Manufacturing Systems for Industry of the Future | es_ES |
dc.description.abstract | [EN] Recent advances in the development of technological devices and software for Industry 4.0 have pushed a change in the maintenance management systems and processes. Nowadays, in order to maintain a company competitive, a computerised management system is required to help in its maintenance tasks. This paper presents an analysis of the complexities and requirements for maintenance of Industry 4.0. It focuses on intelligent systems that can help to improve the intelligent management of maintenance. Finally, it presents a summary of lessons learned specified as guidelines for the design of such intelligent systems that can be applied horizontally to any company in the Industry. | es_ES |
dc.description.sponsorship | This work is supported by the FEDER/Ministry of Science, Innovation and Universities - State Research Agency RTC-2017-6401-7 | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Springer | es_ES |
dc.relation.ispartof | Service Oriented, Holonic and Multi-agent Manufacturing Systems for Industry of the Future. Proceedings of SOHOMA 2019 | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject | Mantainance management | es_ES |
dc.subject | Predictive maintenance | es_ES |
dc.subject | Intelligent mantainance system | es_ES |
dc.subject | Industry 4.0 | es_ES |
dc.subject.classification | BIBLIOTECONOMIA Y DOCUMENTACION | es_ES |
dc.subject.classification | LENGUAJES Y SISTEMAS INFORMATICOS | es_ES |
dc.title | Requirements for an Intelligent Maintenance System for Industry 4.0 | es_ES |
dc.type | Comunicación en congreso | es_ES |
dc.type | Capítulo de libro | es_ES |
dc.identifier.doi | 10.1007/978-3-030-27477-1_26 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI//RTC-2017-6401-7/ES/Plataforma horizontal de smart data y deep learning para la industria y aplicación al sector manufacturere, PLATINUM/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació | es_ES |
dc.description.bibliographicCitation | Garcia, E.; Araujo, A.; Palanca Cámara, J.; Giret Boggino, AS.; Julian Inglada, VJ.; Botti, V. (2019). Requirements for an Intelligent Maintenance System for Industry 4.0. Springer. 340-351. https://doi.org/10.1007/978-3-030-27477-1_26 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.conferencename | 9th Workshop on Service Oriented, Holonic and Multi-agent Manufacturing Systems for Industry of the Future (SOHOMA 19) | es_ES |
dc.relation.conferencedate | Octubre 03-04,2019 | es_ES |
dc.relation.conferenceplace | Valencia, Spain | es_ES |
dc.relation.publisherversion | https://doi.org/10.1007/978-3-030-27477-1_26 | es_ES |
dc.description.upvformatpinicio | 340 | es_ES |
dc.description.upvformatpfin | 351 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.relation.pasarela | S\400147 | es_ES |
dc.contributor.funder | Agencia Estatal de Investigación | es_ES |
dc.description.references | CEN, European Committee for Standardization: EN 13306:2017. Maintenance Terminology. European Standard (2017) | es_ES |
dc.description.references | Chen, B., Wan, J., Shu, L., Li, P., Mukherjee, M., Yin, B.: Smart factory of Industry 4.0: key technologies, application case, and challenges. IEEE Access 6, 6505–6519 (2018). https://doi.org/10.1109/access.2017.2783682 | es_ES |
dc.description.references | Crespo Marquez, A., Gupta, J.N.: Contemporary maintenance management: process, framework and supporting pillars. Omega 34(3), 313–326 (2006). https://doi.org/10.1016/j.omega.2004.11.003 | es_ES |
dc.description.references | Ferreira, L.L., Albano, M., Silva, J., Martinho, D., Marreiros, G., di Orio, G., Malo, P., Ferreira, H.: A pilot for proactive maintenance in Industry 4.0. In: 2017 IEEE 13th International Workshop on Factory Communication Systems (WFCS). IEEE (2017). https://doi.org/10.1109/wfcs.2017.7991952 | es_ES |
dc.description.references | Goh, K., Tjahjono, B., Baines, T., Subramaniam, S.: A review of research in manufacturing prognostics. In: 2006 IEEE International Conference on Industrial Informatics, Singapore, pp. 417–422. IEEE (2006). https://doi.org/10.1109/INDIN.2006.275836 | es_ES |
dc.description.references | Hashemian, H.M., Bean, W.C.: State-of-the-art predictive maintenance techniques. IEEE Trans. Instrum. Meas. 60(10), 3480–3492 (2011). https://doi.org/10.1109/TIM.2009.2036347 | es_ES |
dc.description.references | Lee, W.J., Wu, H., Yun, H., Kim, H., Jun, M.B., Sutheralnd, J.W.: Predictive maintenance of machine tool systems using artificial intelligence techniques applied to machine condition data. Procedia CIRP 80, 506–511 (2019) | es_ES |
dc.description.references | Lu, B., Durocher, D., Stemper, P.: Predictive maintenance techniques. IEEE Ind. Appl. Mag. 15(6), 52–60 (2009). https://doi.org/10.1109/MIAS.2009.934444 | es_ES |
dc.description.references | Mrugalska, B., Wyrwicka, M.K.: Towards lean production in Industry 4.0. Procedia Eng. 182, 466–473 (2017). https://doi.org/10.1016/j.proeng.2017.03.135 | es_ES |
dc.description.references | O’Donoghue, C., Prendergast, J.: Implementation and benefits of introducing a computerised maintenance management system into a textile manufacturing company. J. Mater. Process. Technol. 153, 226–232 (2004) | es_ES |
dc.description.references | Paolanti, M., Romeo, L., Felicetti, A., Mancini, A., Frontoni, E., Loncarski, J.: Machine learning approach for predictive maintenance in Industry 4.0. In: 2018 14th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA). IEEE (2018). https://doi.org/10.1109/mesa.2018.8449150 | es_ES |
dc.description.references | Patil, R.B., Mhamane, D.A., Kothavale, P.B., Kothavale, B.: Fault tree analysis: a case study from machine tool industry. Available at SSRN 3382241 (2018) | es_ES |
dc.description.references | Potes Ruiz, P.A., Kamsu-Foguem, B., Noyes, D.: Knowledge reuse integrating the collaboration from experts in industrial maintenance management. Knowl. Based Syst. 50, 171–186 (2013). https://doi.org/10.1016/j.knosys.2013.06.005 | es_ES |
dc.description.references | Razmi-Farooji, A., Kropsu-Vehkaperä, H., Härkönen, J., Haapasalo, H.: Advantages and potential challenges of data management in e-maintenance. J. Qual. Maint. Eng. (2019) | es_ES |
dc.description.references | Rüßmann, M., Lorenz, M., Gerbert, P., Waldner, M., Justus, J., Harnisch, M.: Industry 4.0: the future of productivity and growth in manufacturing industries. Boston Consult. Group 9(1), 54–89 (2015) | es_ES |
dc.description.references | Wan, J., Tang, S., Li, D., Wang, S., Liu, C., Abbas, H., Vasilakos, A.V.: A manufacturing big data solution for active preventive maintenance. IEEE Trans. Ind. Inform. 13(4), 2039–2047 (2017). https://doi.org/10.1109/tii.2017.2670505 | es_ES |