Open Access
| Issue |
EPJ Web Conf.
Volume 355, 2026
4th International Conference on Sustainable Technologies and Advances in Automation, Aerospace and Robotics (STAAAR 2025)
|
|
|---|---|---|
| Article Number | 04002 | |
| Number of page(s) | 11 | |
| Section | Thermofluids, Aerodynamics and CFD Simulation | |
| DOI | https://doi.org/10.1051/epjconf/202635504002 | |
| Published online | 03 March 2026 | |
- Chen, W., Liu, F., Peng, Z. (2021). A hybrid model combining physics-based and data-driven methods for aeroengine performance degradation prognosis. Aerospace Science and Technology, 109, 106513. [Google Scholar]
- J. Lu, K. Yang, P. Zhang, W. Wu, S. Li, A trend forecasting method for the vibration signals of aircraft engines combining enhanced slice-level adaptive normalization using long short-term memory under multi-operating conditions. Sens. 25 (7), 2066 (2025). https://doi.org/10.3390/s25072066. [Google Scholar]
- Z. Wang, Y. Wang, X. Wang, K. Yang, Y. Zhao, A novel digital twin framework for aeroengine performance diagnosis. Aerospace 10 (9), 789 (2023). https://doi.org/10.3390/aerospace10090789. [Google Scholar]
- Wang, Y., Wang, J., Sun, X. (2023). A deep learning-based approach for aeroengine fault diagnosis using sensor data fusion. Aerospace Science and Technology, 136, 107911. [Google Scholar]
- Li, H., Zhou, J., Gao, R.X. (2022). Multivariate sensor data fusion and fault diagnosis for aeroengines using convolutional neural networks. Aerospace Science and Technology, 122, 107481. [Google Scholar]
- S. Fu, S. Zhong, L. Lin, M. Zhao, A re-optimized deep auto-encoder for gas turbine un-supervised anomaly detection. Eng. Appl. Artif. Intell. 101, 104199 (2021). https://doi.org/10.1016/j.engappai.2021.104199. [Google Scholar]
- Z. Yan, J. Sun, Y. Yi, C. Yang, J. Sun, Data-driven anomaly detection framework for complex degradation monitoring of aero-engine. Int. J. Turbomach. Propuls. Power 8, 3 (2023). https://doi.org/10.3390/ijtpp8010003. [Google Scholar]
- O. Asif, M. Kamran, S. Naqvi, J. Zaki, K. Kwak, S.M.R. Islam, A deep learning model for remaining useful life prediction of aircraft turbofan engine on C-MAPSS dataset. IEEE Access PP, 1 (2022). https://doi.org/10.1109/ACCESS.2022.3203406. [Google Scholar]
- T. Ravichandran, B. Cui, S. Namachchivaya, A. Kumar, A. Srivatsava, Ensemble learning-based convolutional neural networks for remaining useful life prediction of aircraft engines. Proc. Annu. Conf. PHM Soc. 15 (1), (2023). https://doi.org/10.36001/phmconf.2023.v15i1.3517. [Google Scholar]
- L. Wang, Y. Chen, X. Zhao, J. Xiang, Predictive maintenance scheduling for aircraft engines based on remaining useful life prediction. IEEE Internet Things J. PP, 1 (2024). https://doi.org/10.1109/JIOT.2024.3376715. [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.

