EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 622 (2025) E3S Web Conf., 622 (2025) 01010 References
Open Access
Issue
E3S Web Conf.
Volume 622, 2025
2nd International Conference on Environment, Green Technology, and Digital Society (INTERCONNECTS 2024)
Article Number 01010
Number of page(s) 12
Section Engineering and Technology
DOI https://doi.org/10.1051/e3sconf/202562201010
Published online 04 April 2025
  1. Setiyo, M.; Rochman, M.L. The Role of Mechanical Engineering in the Era of Industry 4.0 and Society 5.0. Mechanical Engineering for Society and Industry 2023, 3, 54–56, doi: 10.31603/mesi.10786. [Google Scholar]
  2. Ben-Daya, M.; Kumar, U.; Murthy, D.N.P. Introduction to Maintenance Engineering: Modelling, Optimization, and Management; 1st ed.; Wiley: West Sussex, 2016; Vol. 1;. [Google Scholar]
  3. Dhillon, B.S. Engineering Maintenance: A Modern Approach; 1st ed.; CRC Press: Boca Raton, Florida, 2002; Vol. 1;. [Google Scholar]
  4. Basri, E.I.; Abdul Razak, I.H.; Ab-Samat, H.; Kamaruddin, S. Preventive Maintenance (PM) Planning: A Review. J Qual Maint Eng 2017, 23, 114–143, doi: 10.1108/JQME-04-2016-0014. [Google Scholar]
  5. Bousdekis, A.; Lepenioti, K.; Apostolou, D.; Mentzas, G. Decision Making in Predictive Maintenance: Literature Review and Research Agenda for Industry 4.0. IFAC- PapersOnLine 2019, 52, 607–612, doi: 10.1016/j.ifacol.2019.11.226. [Google Scholar]
  6. Sakib, N.; Wuest, T. Challenges and Opportunities of Condition-Based Predictive Maintenance: A Review. Procedía CIRP 2018, 78, 267–272, doi: 10.1016/j.procir.2018.08.318. [Google Scholar]
  7. Nunes, P.; Santos, J.; Rocha, E. Challenges in Predictive Maintenance - A Review. CIRP JManuf Sci Technol 2023, 40, 53–67, doi: 10.1016/j.cirpj.2022.11.004. [CrossRef] [Google Scholar]
  8. Vachtsevanos, G.; Lewis, F.; Roemer, M.; Hess, A.; Wu, B. Intelligent Fault Diagnosis and Prognosis for Engineering Systems. 2006, doi: 10.1002/9780470117842. [Google Scholar]
  9. Rajkumar, R. Raj; Lee, I.; Sha, L.; Stankovic, J. Cyber-Physical Systems: The Computing Revolution. In Proceedings of the Proceedings of the 47th Design Automation Conference; ACM: New York, NY, USA, June 13 2010; pp. 731–736. [Google Scholar]
  10. Li, Y. Nonlinear Prediction and Analysis of the Precision Remaining Useful Life of the Key Meta-Action Unit of CNC Machine Tools with Incomplete Maintenance. Comput Ind Eng 2023, 183, doi: 10.1016/j.cie.2023.109460. [Google Scholar]
  11. Shen, G. Determination of the Average Maintenance Time of CNC Machine Tools Based on Type II Failure Correlation. Eksploatacja i Niezawodnosc 2017, 19, 604–614, doi: 10.17531/ein.2017.4.15. [Google Scholar]
  12. Chen, C.; Liu, Y.; Sun, X.; Di Cairano-Gilfedder, C.; Titmus, S. Automobile Maintenance Prediction Using Deep Learning with GIS Data. In Proceedings of the Procedía CIRP; Elsevier B.V., 2019; Vol. 81, pp. 447–452. [Google Scholar]
  13. Zhang, W.; Yang, D.; Wang, H. Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey. IEEE Syst J 2019, 13, 2213–2227, doi: 10.1109/JSYST.2019.2905565. [Google Scholar]
  14. Wang, S. The Intelligent Operation and Maintenance Method and Experimental Research of CNC Machine Tool Bearings. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering 2024, doi: 10.1177/09544089241284483. [Google Scholar]
  15. Lee, H. A STUDY ON THE PREDICTIVE MAINTENANCE SYSTEM FOR CNC FACILITIES BASED ON INTELLIGENT CUTTING TOOL LOAD PREDICTION. ICICExpress Letters 2022, 16, 265–273, doi: 10.24507/icicel.16.03.265. [Google Scholar]
  16. Biyrouti, S. Arduino-Based Machine Learning Approach for CNC Machine Predictive Maintenance. Lecture Notes in Networks and Systems 2023, 745, 3–11, doi: 10.1007/978-3-031-38274-1_1. [Google Scholar]
  17. Al-Naggar, Y.M. Condition Monitoring Based on IoT for Predictive Maintenance of CNC Machines. Procedia CIRP 2021, 102, 314–318, doi: 10.1016/j.procir.2021.09.054. [Google Scholar]
  18. Luo, W. A Hybrid Predictive Maintenance Approach for CNC Machine Tool Driven by Digital Twin. Robot ComputIntegr Manuf 2020, 65, doi: 10.1016/j.rcim.2020.101974. [Google Scholar]
  19. Kothona, D.; Panapakidis, I.P.; Christoforidis, G.C. Development of Prescriptive Maintenance Methodology for Maintenance Cost Minimization of Photovoltaic Systems. Solar Energy 2024, 271, 112402, doi: 10.1016/j.solener.2024.112402. [Google Scholar]
  20. Osborne, J.W. Best Practices in Data Cleaning: A Complete Guide to Everything You Need to Do Before and After Collecting Your Data; 1st ed.; SAGE Publications, Inc, 2012; Vol. 1;. [Google Scholar]
  21. Chawla, N. V.; Bowyer, K.W.; Hall, L.O.; Kegelmeyer, W.P. SMOTE: Synthetic Minority Over-Sampling Technique. Journal of Artificial Intelligence Research 2002, 16, 321–357, doi: 10.1613/jair.953. [CrossRef] [Google Scholar]
  22. Hair, J.F. Multivariate Data Analysis; Prentice Hall, 2010; [Google Scholar]
  23. Barlow, R.E.; Proschan, F. Mathematical Theory of Reliability; Society of Industrial and Applied Mathematics: Philadelphia, 1996; [Google Scholar]
  24. Iskandar, B.P.; Husniah, H. Optimal Preventive Maintenance for a Two Dimensional Lease Contract. Comput Ind Eng 2017, 113, 693–703, doi: 10.1016/j.cie.2017.09.028. [Google Scholar]
  25. Alifin, F.I.; Iskandar, B.P.; Fasa, N.; Debora, F. Warranty Cost Models for a Repairable Multi-Component Product Protected by Lemon Laws with Failure Interaction. International Journal of Industrial Engineering & Production Research 2024, 35, 1–20, doi: 10.22068/IJIEPR.35.2.2014. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.