CEA-Leti’s Systems Department is integrating artificial intelligence into predictive maintenance for its sensitive lab systems, aiming to extend machine lifespan, prevent unplanned downtime, and optimize maintenance in key sectors. The approach has gained recognition: at the 2025 International Conference on Prognostics and Health Management in Seattle, Guillaume Prevost, a PhD student in signal processing and AI, won a Best Paper Award for “Knowledge-Informed Symbolic Regression for New Features Discovery for Degradation Analysis of Rolling Bearings.”

The multidisciplinary team combines physical modeling expertise from Youssof with Prevost’s signal and data processing. PhD student Leila Merzak, specializing in modeling and signal processing, says a current use case focuses on developing digital twins for damage prediction and state-of-health estimation on mechanical structures, including knee prostheses. Research engineer Célestin Ott, who works on multiphysics modeling, explains that integrating digital twins with physics-informed AI improves the reliability of fault detection and degradation forecasting, making predictive maintenance more accurate and targeted. The team, whose exchanges Célestin calls “very constructive and engaging,” also ordered a milling machine to study tool state-of-health monitoring in rotating machinery using ultrasonic sensing.

​​​​​​​​Ongoing predictive maintenance is critical in CEA-Leti labs, whose sophisticated and sensitive systems support the vital work of research engineers. Effective maintenance extends the lifespan of machines, prevents unplanned downtime, and optimizes mainten​​ance in key sectors. In CEA-Leti's Systems Department, researchers are incorporating artificial intelligence with established practices.​

It is no surprise that these enhanced tools are being incorporated in multiple industrial sectors and are receiving recognition.

At the 2025 International Conference on Prognostics and Health Management in Seattle,Guillaume Prevost, a PhD student in signal processing and AI,presented a papertitled, “Knowledge-Informed Symbolic Regression for New Features Discovery for Degradation Analysis of Rolling Bearings." It won a Best Paper Award.​

Like most CEA-Leti projects, predictive maintenance involves multidisciplinary teams, for example expertise in physical modeling, which Youssof brings, and Guillaume's signal-and-data processing.

Team member Leila Merzak, a PhD student in modeling and signal processing, said one of the team's current use cases is focused on developing digital twins for damage prediction and state of health estimation on mechanical structures. For example, in the framework of her PhD research, on knee prostheses.

Célestin Ott, a research engineer-multiphysics modeling at CEA-Leti, explained that integrating digital twins with physics-informed artificial intelligence enables more accurate and targeted predictive maintenance by improving the reliability of fault detection and degradation forecasting.

Like all well-matched research teams, the members recognize an opportunity to share a humorous moment along with their “very constructive and engaging exchanges", as Célestin describes them.

As when Guillaume ordered a milling machine for the lab to conduct experimental studies on tools' state-of-health (SoH) monitoring in rotating machinery, using ultrasonic sensing to anticipate degradation for predictive-maintenance purposes.