EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 76 (2025) ITM Web Conf., 76 (2025) 01008 References
Open Access
Issue
ITM Web Conf.
Volume 76, 2025
Harnessing Innovation for Sustainability in Computing and Engineering Solutions (ICSICE-2025)
Article Number 01008
Number of page(s) 11
Section Artificial Intelligence & Machine Learning
DOI https://doi.org/10.1051/itmconf/20257601008
Published online 25 March 2025
  1. Zhang, W., Jin, Y., & Zhao, P. (2023). Predictive maintenance using deep learning: A review of recent advances and future directions. IEEE Transactions on Industrial Informatics, 19(4), 5678–5690. [Google Scholar]
  2. Li, H., Wang, X., & Chen, Y. (2022). A hybrid machine learning framework for real-time fault detection in industrial systems. Journal of Manufacturing Processes, 76, 112–124. [Google Scholar]
  3. Singh, R., Kumar, N., & Das, S. (2021). Anomaly detection in industrial equipment using unsupervised learning techniques. Computers in Industry, 132, 103–118. [Google Scholar]
  4. Gonzalez, R., & Smith, J. (2023). Reinforcement learning for predictive maintenance: Challenges and opportunities in smart manufacturing. Engineering Applications of Artificial Intelligence, 116, 104–122. [Google Scholar]
  5. Almeida, P., & Silva, M. (2024). Internet of Things (IoT) and machine learning for predictive maintenance: A case study in manufacturing. Sensors, 24(5), 2034. [Google Scholar]
  6. Patil, C. R., Jadhav, S. K., Bardiya, A. L., Davande, A. P., & Raverkar, M. P. (2023). Machine learning-based predictive maintenance of industrial machines. International Journal of Computer Trends and Technology, 71(3), 50–56. [Google Scholar]
  7. Okeke, C. N., Oluwatobi, O. F., Rita, U. U., Nwankwo, G. U., Chiadikobi, O. M., Michael, O. T., Olanrewaju, A. D., Mayowa, O. Q., & Samuel, U. A. (2023). Predictive maintenance of industrial equipment using machine learning in industrial environment of Awka Metropolis, Nigeria. International Journal of Science and Technology Research Archive, 5(2), 1–9. [Google Scholar]
  8. Fernandes, M., Corchado, J. M., & Marreiros, G. (2022). Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: A systematic literature review. Applied Intelligence, 52, 14246–14280. [Google Scholar]
  9. Ferreira, C., & Goncalves, G. (2022). Remaining useful life prediction and challenges: A literature review on the use of machine learning methods. Journal of Manufacturing Systems, 63, 550–562. [Google Scholar]
  10. Fong, S. (2022). Unsupervised methods for condition-based maintenance in non-stationary operating conditions (Doctoral dissertation). University of Waterloo, Waterloo, Ontario, Canada. [Google Scholar]
  11. Masmoudi, O., Jaoua, M., Jaoua, A., & Yacout, S. (2021). Data preparation in machine learning for condition-based maintenance. Journal of Computer Science, 17(6), 525–538. [Google Scholar]
  12. Nacchia, M., Fruggiero, F., Lambiase, A., & Bruton, K. (2021). A systematic mapping of the advancing use of machine learning techniques for predictive maintenance in the manufacturing sector. Applied Sciences, 11(6), 2546. [Google Scholar]
  13. Namuduri, S., Narayanan, B. N., Davuluru, V. S. P., Burton, L., & Bhansali, S. (2020). Deep learning methods for sensor-based predictive maintenance and future perspectives for electrochemical sensors. Journal of The Electrochemical Society, 167, 037552. [Google Scholar]
  14. Williams, J. A. N., & et al. (2022). Machine predictive maintenance system for industrial applications. International Journal of Current Research, 14(5), 21410–21412. [Google Scholar]
  15. Kane, A. P., & et al. (2022). Predictive maintenance using machine learning. arXiv preprint arXiv:2201.12345. [Google Scholar]
  16. Zufle, M., & et al. (2021). A predictive maintenance methodology: Predicting the time-to-failure of machines in Industry 4.0. IEEE 19th International Conference on Industrial Informatics (INDIN). 1–8. [Google Scholar]
  17. Raheem, A. Z. (2023). Potential of machine learning in predictive maintenance: A case study of an established maintenance company from a sustainability perspective (Master's thesis). KTH Royal Institute of Technology. Stockholm. Sweden. [Google Scholar]
  18. Zhao, Y.. Yang, J.. Wang, W.. Yang, H.. & Niyato, D. (2023). TranDRL: A transformer-driven deep reinforcement learning enabled prescriptive maintenance framework. arXiv preprint arXiv:2309.16935. [Google Scholar]
  19. Magena, C. (2024). Machine learning models for predictive maintenance in industrial engineering. International Journal of Computing and Engineering. 6(3). 1–14. [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.