Post-Doctoral Research Visit F/M Robust optimisation methods for sparse-sensor placement for fault detection in vibrating mechanical structures

Created in 2008, the Inria Saclay Center is located at the heart of the Paris-Saclay scientific and technological excellence cluster, which alone accounts for 15% of French research. Serving the development of the Université Paris-Saclay and the Institut Polytechnique de Paris, the Inria Saclay center employs 80 people in research support services and 500 scientists of 54 nationalities.

Benefiting from continuous growth, the center now has a total of 42 project-teams and two in the process of being created, including 21 jointly with the Institut Polytechnique de Paris, 16 with the Université Paris-Saclay, as well as 7 Inria EPs, including one in collaboration with Onera and one with the Pôle Universitaire Centre Val de Loire. These research teams are spread over more than ten sites.

Environment

The work will be conducted in the Platon team, a joint research group between Ecole Polytechnique and CNRS, hosted by the Center for Applied Mathematics (CMAP) of Ecole Polytechnique. The Platon project-team focuses on developing innovative methods and algorithms for uncertainty management in numerical models, including advanced calibration strategies from data (observations, measurements, other model predictions) and uncertainty reduction. The work of the postdoc will be supervised by E. Denimal Goy (CR Inria) and P. M. Congedo (DR Inria), both experts in uncertainty quantification methods. The project is funded by French research agency (ANR JCJC MeMoRa).

Scientific context

Monitoring operating mechanical structures, in particular for energy production, is critical to ensure their integrity. Using vibration measures from a few sensors, the objective is to detect, localise and quantify the appearance of a fault (a crack e.g.) as soon as possible in order to reduce the maintenance costs and accidents [1]. The accuracy of detecting when a defect first appears depends on several factors, particularly the placement of the sensors. Indeed, they must be sensitive and selective w.r.t. faults. However, this task becomes very challenging when uncertainties are present as the impact of the fault can be masked by the different uncertainties [2]. Therefore, developing a robust framework to optimize sensor placement is a central objective of this postdoctoral position.

The objective of the postdoc is to develop new robust optimisation methods for sensor placement, while considering numerous sources of uncertainty. The team has identified new fault-detection features which will be used here in the context of the postdoc. One of the main challenges is the control of the numerical cost coming from the numerical solver and the high number of the uncertainties. Specifically, methods using surrogate-modelling based optimisation will be investigated to reduce the cost of the numerical solver [3,4,5]. More specifically, the different objectives are the following:

• Literature review on robust optimisation methods for sensor placement for vibration-based monitoring while considering model uncertainties
• Implementation of state-of-the art methods on an academic test case
• Develop a new robust optimisation approach for sensor placement while considering model uncertainties
• Compare the new approach to state-of-the-art methods to assess its performances
• Extension of the method on a complex test case (aircraft blade, wind turbine blade e.g.)

References:

[1] Cadoret, A., Denimal-Goy, E., Leroy, J. M., Pfister, J. L., & Mevel, L. (2025). Damage detection and localization method for wind turbine rotor based on Operational Modal Analysis and anisotropy tracking. Mechanical Systems and Signal Processing, 224, 111982.

[2] Sinou, J. J., & Denimal, E. (2022). Reliable crack detection in a rotor system with uncertainties via advanced simulation models based on kriging and Polynomial Chaos Expansion. European Journal of Mechanics-A/Solids, 92, 104451.

[3] Denimal, E., & Sinou, J. J. (2021). Advanced kriging-based surrogate modelling and sensitivity analysis for rotordynamics with uncertainties. European Journal of Mechanics-A/Solids, 90, 104331.

[4] Gallia, M., Arizmendi Gutiérrez, B., Gori, G., Guardone, A., & Congedo, P. M. (2024). Robust optimization of a thermal anti-ice protection system in uncertain cloud conditions. Journal of Aircraft, 61(1), 43-57.

[5] Rivier, M., & Congedo, P. M. (2022). Surrogate-assisted bounding-box approach applied to constrained multi-objective optimisation under uncertainty. Reliability Engineering & System Safety, 217, 108039.

• Literature review
• Design and analysis of numerical methods
• Prototyping, validation, numerical investigation
• Manipulation of mechanical models
• Paper and report writing
• Oral presentations: national and international conferences, team meetings, supervision meetings

Applicants should have a PhD in mechanical engineering or in applied maths, with either skill in vibrations, in robust optimisation and/or uncertainty quantification

Expected skills

• Proficiency in Matlab/Python/Julia
• Oral presentation skills: progress meetings, team meetings
• Good writing skills: report writing, article writing
• Ability to work in an international team

• Subsidized meals
• Partial reimbursement of public transport costs
• Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
• Possibility of teleworking and flexible organization of working hours
• Professional equipment available (videoconferencing, loan of computer equipment, etc.)
• Social, cultural and sports events and activities
• Access to vocational training

Gross salary : 2 788 €