- -

Requirements for an Intelligent Maintenance System for Industry 4.0

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Requirements for an Intelligent Maintenance System for Industry 4.0

Mostrar el registro sencillo del ítem

Ficheros en el ítem

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


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem