Open Access
Issue
E3S Web Conf.
Volume 351, 2022
10th International Conference on Innovation, Modern Applied Science & Environmental Studies (ICIES’2022)
Article Number 01036
Number of page(s) 5
DOI https://doi.org/10.1051/e3sconf/202235101036
Published online 24 May 2022
  1. A. Al-Fuqaha, M. Guisani, M. Mohammadi, M. Aledhari, and M. Ayyash, Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications. IEEE Commun. Surv. Tutor, 17, 4, pp. 2347–2376 (2015). [CrossRef] [Google Scholar]
  2. C. Okoh, R. Roy, and J. Mehnen, Predictive Maintenance Modelling for Through-Life Engineering Services, Procedia CIRP, 59,196–201 (2017). [Google Scholar]
  3. L. Silvestri, A. Forcina, V. Introna, A. Santolamazza, and V. Cesarotti, Maintenance transformation through Industry 4.0 technologies: A systematic literature review, Comput. Ind., 123, 103335 (2020). [CrossRef] [Google Scholar]
  4. B. Einabadi, A. Baboli, and M. Ebrahimi, Dynamic Predictive Maintenance in industry 4.0 based on real time information: Case study in automotive industries. IFAC-Pap., 52, 13, 1069–1074 (2019). [Google Scholar]
  5. T. Zonta, C. A. da Costa, R. da Rosa de Lima Righi, M. J., E.S. da Trindade, and G. P. Li, Predictive maintenance in the industry 4.0: A systematic literature review, Comput. Ind. Eng., 150, 106889 (2020). [CrossRef] [Google Scholar]
  6. A. El Kihel, Y. El Kihel, A. Bakdid, H. Gziri, I. Manssouri, D. Amegouz, Optimization of industrial energy efficiency by intelligent predictive maintenance tools case of misalignment of an industrial system, in IEEE 2nd International Conference on Electronics, Control, Optimization and Computer Science, ICECOCS, 9314505 (2020). [Google Scholar]
  7. A. Elkihel, I. Derouiche, Y. Elkihel, A. Bakdid, and H. Gziri, Artificial intelligence based on the neurons networks at the service predictive bearing”, in 6th International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS-2020), Morocco, Fez (2020). (to be published) [Google Scholar]
  8. R. K. Mobley, An introduction to predictive maintenance 2nd ed, Amsterdam, New York: Butterworth-Heinemann (2002). [Google Scholar]
  9. A. Abu-Samah, M. K. Shahzad, E. Zamai, and A. B. Said, Failure Prediction Methodology for Improved Proactive Maintenance using Bayesian Approach, IFAC-Pap., 48, 844–851, (2015). [Google Scholar]
  10. S. Selcuk, Predictive maintenance, its implementation and latest trends, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 9, 1670–1679, (2016). [Google Scholar]
  11. T. P. Carvalho, F. A. A. M. N. Soares, R. Vita, R.P. da Francisco, J. P. Basto, and S. G. S. Alcala, A systematic literature review of machine learning methods applied to predictive maintenance, Comput. Ind. Eng., 137, 106024 (2019) [CrossRef] [Google Scholar]
  12. J. Wan et al., A Manufacturing Big Data Solution for Active Preventive Maintenance, IEEE Trans. Ind. Inform., 13, vol. 4, pp. 2039–2047, 2017. [CrossRef] [Google Scholar]
  13. L. Atzori, A. Iera, and G. Morabito, The Internet of Things: A survey, Comput. Netw., 54, 2787–2805 (2010) [Google Scholar]
  14. D. G. S. Pivoto, L. F. F. de Almeida, R. da Rosa Righi, J. J. P. C. Rodrigues, A. B. Lugli, and A. M. Alberti, Cyber-physical systems architectures for industrial internet of things applications in Industry 4.0: A literature review, J. Manuf. Syst., 58,176–192 (2021). [CrossRef] [Google Scholar]
  15. T. P. Carvalho, F. A. A. M. N. Soares, R. Vita, R. da P. Francisco, J. P. Basto, and S. G. S. Alcala, A systematic literature review of machine learning methods applied to predictive maintenance, Comput. Ind. Eng., 137, 106024 (2019). [CrossRef] [Google Scholar]
  16. J. Wan et al., A Manufacturing Big Data Solution for Active Preventive Maintenance, IEEE Trans. Ind. Inform., 13, 2039–2047, (2017). [CrossRef] [Google Scholar]

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