I4S Research team

Statistical Inference for Structural Health Monitoring

Team presentation

I4S is a team dedicated to the strong coupling between physical modelling and statistics, with application in civil engeneering and aeronautics. Structural health monitoring projects with in operational application under ambient excitation do require robust methods, robust to environmental changes and false alarms. Applying our statistical background to complex large scale is our objective. Damage detection and localization, but also aerodynamic unstability are our concerns. Challenges arise from the high dimensionality of the considered parameters, due to the complexity of the structures, the large number of measurement points, but also from the highly transient behavior of the monitored structure. This requires the development of realtime computationaly efficient robust to noise algorithms.

Research themes

  • develop physics driven statistical algorithms robust to noise and variation in the environment
  • consider the impact of uncertainties on the current available identification algorithms
  • develop efficient robust and fast implementation of such quantities
  • interact with industrial partners in both civil engineering and aeronautics to provide relevant tools, whose main objective is to find place among current engineering practice
  • develop relevant non trivial thermal models for usage in rejection based structural health monitoring mix numerical model, physic modeling and data
  • consider the application and extension of statistical algorithms for very large structures, whose size make irrelevant or inefficient the classical identification algorithms
  • pursue efforts in frequency domain based algorithms

International and industrial relations

  • FP7 NMP IRIS (Integrated European Industrial Risk Reduction System)
  • Projet avec Structural Vibration Solutions, The University of British Columbia (CA), et TVP Structural Vibration Specialists (CA)
  • Project MODIPRO within Pôle de Compétitivité Aérospatial ASTech Paris Région
  • Project with SNECMA
  • Collaboration with Katholieke Universiteit Leuven (B)
  • Collaboration with LCPC (Division Métrologie et Instrumentation)
  • Collaboration with LMS (BE)
  • Collaboration with SVIBS (DK)
  • Joint works with Vrije Universiteit Brussel (B), Minho University (PT), Harbin Institute of Technology (PRC), Aalborg University (DK), University of Porto (FUIP), BAM (Federal Institute for Materials Research and Testing) (All), University of Sheffield (UK), and Université Libre de Bruxelles (B)

Keywords: Statistics Structural health monitoring Civil engeneering Aeronautics

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