Predictive maintenance is crucial in CEA-Leti’s labs, where sensitive systems underpin research engineering, and extending equipment lifespan, avoiding downtime, and streamlining maintenance are top priorities. The institute’s Systems Department is integrating AI into these established workflows, a approach gaining traction across multiple industrial sectors. At the 2025 International Conference on Prognostics and Health Management in Seattle, PhD student Guillaume Prevost earned a Best Paper Award for “Knowledge-Informed Symbolic Regression for New Features Discovery for Degradation Analysis of Rolling Bearings.” Prevost’s work blends signal processing and AI with physical modeling from colleague Youssof, reflecting the multidisciplinary ethos of CEA-Leti projects. Fellow PhD student Leila Merzak noted the team is developing digital twins for damage prediction and state-of-health estimation on mechanical structures, including her research on knee prostheses. Research engineer Célestin Ott explained that coupling digital twins with physics-informed AI sharpens predictive maintenance by boosting fault detection and degradation forecasting reliability. In a lighter vein, Prevost recently ordered a milling machine to carry out experimental ultrasonic sensing studies on tool condition monitoring in rotating machinery, underscoring the lab’s hands-on drive to anticipate wear.

​​​​​​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.