Open Access
Issue
MATEC Web Conf.
Volume 413, 2025
International Conference on Measurement, AI, Quality and Sustainability (MAIQS 2025)
Article Number 07008
Number of page(s) 8
Section Advances in Quality Management
DOI https://doi.org/10.1051/matecconf/202541307008
Published online 01 October 2025
  1. S. Elkateb, A. Métwalli, A. Shendy, and A. E. B. Abu-Elanien, “Machine learning and IoT–Based predictive maintenance approach for industrial applications,” Alexandria Engineering Journal, vol. 88, pp. 298–309, 2024. doi:10.1016/j.aej.2023.12.065. [Google Scholar]
  2. F. Maulana, A. Starr, and A. P. Ompusunggu, “Explainable data-driven method combined with Bayesian filtering for remaining useful lifetime prediction of aircraft engines using NASA CMAPSS datasets,” Machines, vol. 11, no. 2, p. 163, 2023. doi:10.3390/machines11020163. [Google Scholar]
  3. O. Asif et al., “A deep learning model for remaining useful life prediction of aircraft turbofan engine on C-MAPSS dataset,” IEEE Access, vol. 10, pp. 95425–95440, 2022. doi:10.1109/ACCESS.2022.3203406. [Google Scholar]
  4. J. Wu et al., “Data-driven remaining useful life prediction via multiple sensor signals and deep long short-term memory neural network,” ISA Transactions, vol. 97, pp. 305–316, 2020. doi:10.1016/j.isatra.2019.07.004. [Google Scholar]
  5. A. Al-Dulaimi, S. Zabihi, A. Asif, and A. Mohammadi, “Hybrid deep neural network model for remaining useful life estimation,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., 2019. doi:10.1109/ICASSP.2019.8683763. [Google Scholar]
  6. B. Zraibi, C. Okar, H. Chaoui, and M. Mansouri, “Remaining useful life assessment for lithium-ion batteries using CNN-LSTM-DNN hybrid method,” IEEE Trans. Veh. Technol., vol. 70, no. 4, pp. 3665–3676, 2021. doi:10.1109/TVT.2021.3071622. [Google Scholar]
  7. A. Al-Dulaimi, S. Zabihi, A. Asif, and A. Mohammadi, “NBLSTM: Noisy and hybrid convolutional neural network and BLSTM-based deep architecture for remaining useful life estimation,” J. Comput. Inf. Sci. Eng., vol. 20, no. 6, 2020. doi:10.1115/1.4045491. [Google Scholar]
  8. H. Mo, F. Lucca, J. Malacarne, and G. Iacca, “Multi-head CNN-LSTM with prediction error analysis for remaining useful life prediction,” in Proc. Conf. Open Innovations Assoc. FRUCT, 2020. doi:10.23919/fruct49677.2020.9211058. [Google Scholar]
  9. U. Amin and K. Kumar, “Remaining useful life prediction of aircraft engines using hybrid model based on artificial intelligence techniques,” in Proc. Int. Conf. Prognostics Health Manag., 2021. doi:10.1109/ICPHM51084.2021.9486500. [Google Scholar]
  10. A. Saxena and K. Goebel, “Turbofan engine degradation simulation data set,” NASA Ames Prognostics Data Repository, 2008. [Google Scholar]
  11. A. Saxena, K. Goebel, D. Simon, and N. Eklund, “Damage propagation modeling for aircraft engine run-to-failure simulation,” in Proc. Int. Conf. Prognostics and Health Management (PHM), 2008. [Google Scholar]
  12. Y. Alomari, M. Andó, and M. Baptista, “Advancing aircraft engine RUL predictions: An interpretable integrated approach of feature engineering and aggregated feature importance,” Scientific Reports, vol. 13, 2023. doi:10.1038/s41598-023-40315-1. [Google Scholar]
  13. U. Thakkar and H. Chaoui, “Remaining Useful Life Prediction of an Aircraft Turbofan Engine Using Deep Layer Recurrent Neural Networks,” Actuators, vol. 11, no. 3, article 67, 2022. doi:10.3390/act11030067. [Google Scholar]
  14. D. Liu, Y. Lei, N. Li, and M. J. Zuo, “A review of data-driven prognostics and health management for engineering systems,” IEEE Transactions on Industrial Electronics, vol. 66, no. 10, pp. 8871–8882, 2019. [Google Scholar]
  15. C. Cummins, J. Burns, and M. Keane, “Explainable AI in industrial predictive maintenance: A systematic review,” Applied Sciences, vol. 14, no. 2, p. 465, 2024. [Google Scholar]
  16. J. Zhao, Y. Zhu, L. Gong, and Q. Zhang, “Remaining useful life prediction of turbofan engines using one-dimensional fully convolutional and LSTM hybrid networks,” in Proc. IEEE Int. Conf. Prognostics Health Manag. (PHM), 2023, [Google Scholar]
  17. Y. Deng and Y. Zhou, “Prediction of Remaining Useful Life of Aero-Engines Based on CNN-LSTM-Attention,” Int. J. Comput. Intell. Syst., vol. 17, no. 1, pp. 362–375, 2024. doi:10.1007/s44196-024-00639-w [Google Scholar]
  18. Y. Xu, X. Yang, H. Huang, C. Peng, Y. Ge, H. Wu, J. Wang, G. Xiong and Y. Yi, “Extreme gradient boosting model has a better performance in predicting the risk of 90-day readmissions in patients with ischaemic stroke,” J. Stroke Cerebrovasc. Dis., 28, 104441 (2019). doi:10.1016/j.jstrokecerebrovasdis.2019.104441 [Google Scholar]
  19. M. Nunes, P. Ferreira, and F. Belo, “Benchmarking data-driven predictive maintenance: Challenges and future directions,” Journal of Manufacturing Systems, vol. 67, pp. 302–313, 2023. [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.