Pronostic and predictive maintenance Theme
Discover the theme : “Pronostic and predictive maintenance”
In this page :
Theme presentation
Application Areas
- Construction 4. 0 and sustainable city
- Industry 5.0
Key Research Questions
- How to improve the accuracy of RUL prediction in a data frugal framework?
- How to improve the interpretability of RUL prediction models?
- How to improve the scalability of RUL prediction models, particularly in situations where large amounts of data are involved or where the model needs to be updated frequently?
Illustrations of Scientific Activity
Youness et al., 2023. An Explainable Artificial Intelligence Approach for Remaining Useful Life Prediction. Aerospace, 2023, 10 (5), pp.1-23. hal-04102284
Benatia et al., Toward Smart Alarm Management in Complex Manufacturing Systems: an Embedded Deep Learning Approach. Journal of Expert Systems with Applications.
Barry et al., 2023. Predictive Maintenance for Aircraft Engine: Embracing an Ontological-Data Approach. 20th ACS/IEEE International Conference on Computer Systems and Applications. hal-04369673
Barry et al., 2023. Predictive Maintenance for Aircraft Engine: Embracing an Ontological-Data Approach. 20th ACS/IEEE International Conference on Computer Systems and Applications. hal-04369673
Researchers
- AHMED-ALI Tarek, HDR
- BEN AYED Safa
- BENATIA Amin
- DAAJI Marwa
- GUIRAUD Mael
- HAFSI Meriem
- LOUIS Anne
- YOUNESS Genane
PhD Students
- AYADI Amal, Diagnostic et Pronostic à l’aide de l’IA dans le cadre de la maintenance industrielle, 2024
- HOSSINI Anas, Modèle formel pour la maintenance prédictive d’un Smart Building, 2025
- NDAO Lamine, Vers une intelligence artificielle explicable et équitable, 2025
- BOUALI Rym, Optimal resource planning and location for wildfire risk prevention, 2026