I4S is a team dedicated to the strong coupling between physical modelling and statistics, with application in civil engineering 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 structures is our objective. Damage detection and localization, but also aerodynamic instability 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 behaviour of the monitored structure. This requires the development of realtime computationaly efficient robust to noise algorithms.
In this context, and based on our background and results on model-based statistical
identification, change detection and vibration monitoring, our objectives are :
-Importing knowledge from engineering communities within our model-based information processing methods.
-Mixing statistical inference tools (identification, detection, rejection) with simplified models of aerodynamic effects, thermo-dynamical or other environmental effects.
-Involving nonlinearities in the models, algorithms and proofs of performances.
- Exporting our data processing algorithms within the SHM community, based on specific training actions, on a dedicated Scilab toolbox, and an industrial software.
To achieve this vision, I4S has structured its research according to the following three objectives:
Identification of linear systems
Damage monitoring of civil structures
In-flight monitoring of aircraft structures
Civil engineering :
Civil engineering is a currently renewing scientific research area, which can no longer be restricted to the single mechanical domain, with numerical codes as its central focus. Recent and significant advances in physics and physical chemistry have improved the understanding of the detailed mechanisms of the constitution and the behaviour of various materials (see e.g. the multi-disciplinary general agreement CNRS-Lafarge). Moreover, because of major economical and societal issues, such as durability and safety of infrastructures, buildings and networks, civil engineering is evolving towards a multi-disciplinary field, involving in particular information sciences and technologies and environmental sciences. Our originality is to address health monitoring of structures using statistical approaches, by ensuring robustness against environmental effects and awareness of uncertainties due to various sources, in a comprehensive way.
Improved safety and performance and reduced aircraft development and operating costs are major concerns in aeronautics industry. One critical design objective is to clear the aircraft from unstable aero-elastic vibrations (gutter) in all flight conditions. Opening of flight domain requires a careful exploration of the dynamical behavior of the structure subject to vibration and aero-servo-elastic forces. This is achieved via a combination of ground vibration tests and in flight tests. For both types of tests, various sensors data are recorded, and modal analyses are performed. Important challenges of the in-flight modal analyses are the limited choices for measured excitation inputs, and the presence of unmeasured natural excitation inputs (turbulence). Today, structural flight tests require controlled excitation by ailerons or other devices, stationary flight conditions (constant elevation and speed), and no turbulence. As a consequence, flight domain opening requires a lot of test flights and its costly. This is even worse for aircrafts having a large number of variants (business jets, military aircrafts). A key challenge is therefore to allow for exploiting more data under more conditions during flight tests: uncontrolled excitation, nonstationary conditions.
- Stochastic subspace-based modal analysis under nonstationary excitation.
- Multi-patch version of stochastic subspace modal analysis under nonstationary excitation.
- Comparing input/output and output-only modal analyses.Proving the convergence of subspace identification methods under nonstationary excitation.
Structural health monitoring
- From stochastic subspace modal analysis to structural monitoring.
- Comparing identification and detection approaches to monitoring.
- From stochastic subspace modal analysis to flight flutter monitoring.See also Rafik Zouari's Ph.D. thesis (in French).
- Combining subspace-based monitoring and substructuring for damage localization.
- Handling the temperature effect for monitoring civil engineering structures. See also Houssein Nasser's Ph.D. thesis (in French).
A list of previous test cases processed so far using the algorithms elaborated within Sisthem is available here.
I4S activity reports are available here.
Sisthem activity reports : 2005; 2006; 2007; 2008
The I4S team is one of the follow-up of SISTHEM.