monitoring, system identification, change detection, diagnostics, on-line identification and detection algorithms, subspace-based algorithms, statistical hypotheses testing, sensors fusion, optimal sensors placement, vibration-based structural analysis and damage detection and localization, aeronautics, civil engineering

Structural Health Monitoring (SHM) is the whole process of the design, development and implementation of techniques for the detection, localization and estimation of damages, for
monitoring the integrity of structures and machines within the aerospace, civil and mechanical engineering infrastructures
,
. In addition to these key driving application areas, SHM is now
spreading over most transportation infrastructures and vehicles, within the naval, railway and automobile domains. Examples of structures or machines to be monitored include aircrafts, space
crafts, buildings, bridges, dams, ships, offshore platforms, on-shore and off-shore wind farms (wind energy systems), turbo-alternators and other heavy machineries,

The emergence of stronger safety and environmental norms, the need for early decision mechanisms, together with the widespread diffusion of sensors of all kinds, result in a thorough renewal of sensor information processing problems. This calls for new research investigations within the sensor data (signal and image) information processing community. In particular, efficient and robust methods for structural analysis, non destructive evaluation, integrity monitoring, damage diagnosis and localization, are necessary for fatigue and aging prevention, and for condition-based maintenance. Moreover, multidisciplinary research, mixing information science, engineering science and scientific computing, is mandatory. However, most of the SHM research investigations are conducted within mechanical, civil and aeronautical engineering departments, with little involvement of advanced data information processing specialists.

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 free Scilab toolbox, and an industrial software.

Industrial projects: with SNECMA (F.) and SVS (DK).

Multi–partners projects at European level: on exploitation of flight test data under natural excitation conditions (FliTE2 - Eurêka), on structural assessment, monitoring and control (SAMCO Association), on industrial risk reduction (IRIS CP-IP).

Academic research: national project on monitoring civil engineering structures (CONSTRUCTIF - ACI S&I), French Pôle de compétitivité ASTECH MODIPRO, European network on system identification (FP5 TMR), FWO research network on identification and control.

Prize : M. Döhler has received the IRiS PRize of Excellence 2011 in the context of european project FP7 IRIS.

In this section, the main features for the key monitoring issues, namely identification, detection, and diagnostics, are provided, and a particular instantiation relevant for vibration monitoring is described.

It should be stressed that the foundations for identification, detection, and diagnostics, are fairly general, if not generic. Handling high order linear dynamical systems, in connection with finite elements models, which call for using subspace-based methods, is specific to vibration-based SHM. Actually, one particular feature of model-based sensor information data processing as exercised in I4S, is the combined use of black-box or semi-physical models together with physical ones. Black-box and semi-physical models are, for example, eigenstructure parameterizations of linear MIMO systems, of interest for modal analysis and vibration-based SHM. Such models are intended to be identifiable. However, due to the large model orders that need to be considered, the issue of model order selection is really a challenge. Traditional advanced techniques from statistics such as the various forms of Akaike criteria (AIC, BIC, MDL, ...) do not work at all. This gives rise to new research activities specific to handling high order models.

Our approach to monitoring assumes that a model of the monitored system is available. This is a reasonable assumption, especially within the SHM areas. The main feature of our monitoring
method is its intrinsic ability to the early warning of small deviations of a system with respect to a reference (safe) behavior under usual operating conditions, namely without any artificial
excitation or other external action. Such a normal behavior is summarized in a reference parameter vector

The behavior of the monitored continuous system is assumed to be described by a parametric model
*estimating function*, for example of the form :

is such that

Assuming that

In many applications, such an approach must be improved in the following directions :

*Recursive estimation:*the ability to compute

*Adaptive estimation:*the ability to
*track*the true parameter

Our approach to on-board detection is based on the so-called asymptotic statistical local approach, which we have extended and adapted , , . It is worth noticing that these investigations of ours have been initially motivated by a vibration monitoring application example. It should also be stressed that, as opposite to many monitoring approaches, our method does not require repeated identification for each newly collected data sample.

For achieving the early detection of small deviations with respect to the normal behavior, our approach generates, on the basis of the reference parameter vector

The early detection of a slight mismatch between the model and the data;

A preliminary diagnostics and localization of the deviation(s);

The tradeoff between the magnitude of the detected changes and the uncertainty resulting from the estimation error in the reference model and the measurement noise level.

These indicators are computationally cheap, and thus can be embedded. This is of particular interest in some applications, such as flutter monitoring, as explained in module .

As in most fault detection approaches, the key issue is to design a
*residual*, which is ideally close to zero under normal operation, and has low sensitivity to noises and other nuisance perturbations, but high sensitivity to small deviations, before they
develop into events to be avoided (damages, faults, ...). The originality of our approach is to :

*Design*the residual basically as a
*parameter estimating function*,

*Evaluate*the residual thanks to a kind of central limit theorem, stating that the residual is asymptotically Gaussian and reflects the presence of a deviation in the parameter vector
through a change in its own mean vector, which switches from zero in the reference situation to a non-zero value.

This is actually a strong result, which transforms any detection problem concerning a parameterized stochastic
*process*into the problem of monitoring the mean of a Gaussian
*vector*.

The behavior of the monitored system is again assumed to be described by a parametric model

Given a new
*Does the new sample still correspond to the nominal model
${\mathbf{P}}_{{\theta}_{0}}$ ?*One manner to address this generally difficult question is the following. The asymptotic local approach consists in deciding between the nominal hypothesis and a

where

If the matrix

where the asymptotic covariance matrix

where

With this approach, it is possible to decide, with a quantifiable error level, if a residual value is significantly different from zero, for assessing whether a fault/damage has occurred. It
should be stressed that the residual and the sensitivity and covariance matrices
*not*necessary to re-identify the model, and the sensitivity and covariance matrices can be pre-computed off-line.

A further monitoring step, often called
*fault isolation*, consists in determining which (subsets of) components of the parameter vector

The question:
*which (subsets of) components of
$\theta $have changed ?*, can be addressed using either nuisance parameters elimination methods or a multiple hypotheses testing approach
. Here we only sketch two intuitively simple statistical nuisance
elimination techniques, which proceed by projection and rejection, respectively.

The fault vector

A rather intuitive statistical solution to the isolation problem, which can be called
*sensitivity*approach, consists in projecting the deviations in

where

Another statistical solution to the problem of isolating
*min-max approach*. It consists in replacing the nuisance parameter component

where

The properties and relationships of these two types of tests are investigated in .

In most SHM applications, a complex physical system, characterized by a generally non identifiable parameter vector

The isolation methods sketched above are possible solutions to the former. Our approach to the latter diagnosis problem is basically a detection approach again, and not a (generally
ill-posed) inverse problem estimation approach
. The basic idea is to note that the physical sensitivity matrix writes

It should be clear that the selection of a particular parameterization

As a summary, the machinery in modules
,
and
provides us with a generic framework for designing monitoring algorithms for
continuous structures, machines and processes. This approach assumes that a model of the monitored system is available. This is a reasonable assumption within the field of applications
described in module
, since most mechanical processes rely on physical principles which write in
terms of equations, providing us with models. These important
*modeling*and
*parameterization*issues are among the questions we intend to investigate within our research program.

The key issue to be addressed within each parametric model class is the residual generation, or equivalently the choice of the
*parameter estimating function*.

For reasons closely related to the vibrations monitoring applications described in module
, we have been investigating subspace-based methods, for both the identification
and the monitoring of the eigenstructure

namely the

The (canonical) parameter vector in that case is :

where

Subspace-based methods is the generic name for linear systems identification algorithms based on either time domain measurements or output covariance matrices, in which different subspaces
of Gaussian random vectors play a key role
. A contribution of ours, minor but extremely fruitful, has been to
write the output-only covariance-driven subspace identification method under a form that involves a parameter estimating function, from which we define a
*residual adapted to vibration monitoring*
. This is explained next.

Let

be the output covariance and Hankel matrices, respectively; and:

where:

are the observability and controllability matrices, respectively. The observation matrix

Since the actual model order is generally not known, this procedure is run with increasing model orders.

Choosing the eigenvectors of matrix

where

This property can be checked as follows. From the nominal

Matrix

Assume now that
*a reference
${\theta}_{0}$and a new sample
${Y}_{1},\cdots ,{Y}_{N}$are available.*For checking whether the data agree with

and to define the residual vector:

Let

It is our experience that this residual has highly interesting properties, both for damage detection and localization , and for flutter monitoring .

Factorization (
) is the key for a characterization of the canonical parameter vector

Proving consistency and robustness results ;

Designing an extension of covariance-driven subspace identification algorithm adapted to the presence and fusion of non-simultaneously recorded multiple sensors setups ;

Proving the consistency and robustness of this extension ;

Designing various forms of
*input-output*covariance-driven subspace identification algorithms adapted to the presence of both known inputs and unknown excitations
.

In this section, the problems we are faced with vibration-based monitoring and within our two major application domains are briefly described.

Detecting and localizing damages for monitoring the integrity of structural and mechanical systems is a topic of growing interest, due to the aging of many engineering constructions and
machines and to increased safety norms. Many current approaches still rely on visual inspections or
*local*non destructive evaluations performed manually. This includes acoustic, ultrasonic, radiographic or eddy-current methods; magnet or thermal field techniques,
*a priori*knowledge and the accessibility of a neighborhood of the damage location. Automatic
*global*vibration-based monitoring techniques have been recognized to be useful alternatives to those local evaluations
. However this has led to actual damage monitoring systems only in the
field of rotating machines.

A common feature of the structures to be monitored (e.g. civil engineering structures subject to hurricanes or earthquakes, but also swell, wind and rain; aircrafts subject to strength and
turbulences,

Classical modal analysis and vibration monitoring methods basically process data registered either on test beds or under specific excitation or rotation speed conditions. However there is a need for vibration monitoring algorithms devoted to the processing of data recorded in-operation, namely during the actual functioning of the considered structure or machine, without artificial excitation, speeding down or stopping.

Health monitoring techniques based on processing vibration measurements basically handle two types of characteristics: the
*structural parameters*(mass, stiffness, flexibility, damping) and the
*modal parameters*(modal frequencies, and associated damping values and mode-shapes); see
and references therein. A central question for monitoring is to compute
*changes*in those characteristics and to assess their
*significance*. For the
*frequencies*, crucial issues are then: how to compute the changes, to assess that the changes are significant, to handle
*correlations*among individual changes. A related issue is how to compare the changes in the frequencies obtained from experimental data with the sensitivity of modal parameters obtained
from an analytical model. Furthermore, it has been widely acknowledged that, whereas changes in frequencies bear useful information for damage
*detection*, information on changes in (the curvature of) mode-shapes is mandatory for performing damage
*localization*. Then, similar issues arise for the computation and the significance of the changes. In particular, assessing the significance of (usually small) changes in the mode-shapes,
and handling the (usually high) correlations among individual mode-shape changes are still considered as open questions
,
.

Controlling the computational complexity of the processing of the collected data is another standard monitoring requirement, which includes a limited use of an analytical model of the structure. Moreover, the reduction from the analytical model to the experimental model (truncated modal space) is known to play a key role in the success of model-based damage detection and localization.

The approach which we have been developing, based on the foundations in modules – , aims at addressing all the issues and overcoming the limitations above.

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 behavior 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.

These last ten years, monitoring the integrity of the civil infrastructure has been an active research topic, including in connected areas such as automatic control, for mastering either the aging of the bridges, as in America (US, Canada) and Great Britain, or the resistance to seismic events and the protection of the cultural heritage, as in Italy and Greece. The research effort in France seems to be more recent, maybe because a tendency of long term design without fatigue oriented inspections, as opposite to less severe design with planned mid-term inspections. One of the current thematic priorities of the Réseau de Génie Civil et Urbain (RGCU) is devoted to constructions monitoring and diagnostics. The picture in Asia (Japan, and also China) is somewhat different, in that the demand for automatic data processing for global SHM systems is much higher, because recent or currently built bridges are equipped with hundreds if not thousands of sensors, in particular the Hong Kong-Shenzen Western Corridor and Stonecutter Bridge projects.

Among the challenges for vibration-based bridges health monitoring, two major issues are the different kinds of (non measured) excitation sources and the environmental effects
. Typically the traffic on
*and*under the bridge, the wind and also the rain, contribute to excite the structure, and influence the measured dynamics. Moreover, the temperature is also known to affect the
eigenfrequencies and mode-shapes, to an extent which is significant w.r.t. the deviations to be monitored.

The aging of aerospace structures is a major current concern of civilian and military aircraft operators. Another key driving factor for SHM is to increase the operation and support efficiency of an air vehicle fleet. A SHM system is viewed as a component of a global integrated vehicle health management (IVHM) system. An overview of the users needs can be found in .

Improved safety and performance and reduced aircraft development and operating costs are other major concerns. One of the critical design objectives is to clear the aircraft from unstable aero-elastic vibrations (flutter) in all flight conditions. This 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). A better exploitation of flight test data can be achieved by using output-only system identification methods, which exploits data recorded under natural excitation conditions (e.g., turbulent), without resorting to artificial control surface excitation and other types of excitation inputs .

A crucial issue is to ensure that the newly designed airplane is stable throughout its operating range. A critical instability phenomenon, known under the name of
*“aero-elastic flutter, involves the unfavorable interaction of aerodynamic, elastic, and inertia forces on structures to produce an unstable oscillation that often results in structural
failure”*
. For preventing from this phenomenon, the airplane is submitted to a
flight flutter testing procedure, with incrementally increasing altitude and airspeed. The problem of predicting the speed at which flutter can occur is usually addressed with the aid of
identification methods achieving modal analysis from the in-flight data recorded during these tests. The rationale is that the damping coefficient reflects the rate of increase or decrease in
energy in the aero-servo-elastic system, and thus is a relevant measure of stability. Therefore, while frequencies and mode-shapes are usually the most important parameters in structural
analysis, the most critical ones in flutter analysis are the damping factors, for some critical modes. The mode-shapes are usually not estimated for flutter testing.

Until the late nineties, most approaches to flutter clearance have led to
*data-based*methods, processing different types of data. A
*combined data-based and model-based*method has been introduced recently under the name of flutterometer. Based on an aero-elastic state-space model and on frequency-domain transfer
functions extracted from sensor data under controlled excitation, the flutterometer computes on-line a robust flutter margin using the

Algorithms achieving the
*on-line in-flight*exploitation of flight test data are expected to allow a more direct exploration of the flight domain, with improved confidence and reduced costs. Among other
challenges, one important issue to be addressed on-line is the flight flutter monitoring problem, stated as the problem of monitoring some specific damping coefficients. On the other hand, it
is known, e.g. from Cramer-Rao bounds, that damping factors are difficult to estimate accurately. For improving the estimation of damping factors, and moreover for achieving this in real-time
during flight tests, one possible although unexpected route is to rely on detection algorithms able to decide whether some damping factor decreases below some critical value or not. The
rationale is that detection algorithms usually have a much shorter response time than identification algorithms.

With the help of former engineers, I4S team has developed and maintained a Scilabtoolbox devoted to modal analysis and vibration monitoring of structures or machines subjected to known or ambient (unknown) excitation. This software (COSMAD 3.64) has been registered at the APP under the number

IDDN.FR.001.210011.002.S.A.2003.000.20700

A list of test-cases (simulators, laboratory test-beds, real structures) for which COSMAD has been used is available on I4S website. The problem is to identify the eigenstructure (eigenvalues and observed components of the associated eigenvectors) of the state transition matrix of a linear dynamical system, using only the observation of some measured outputs summarized into a sequence of covariance matrices corresponding to successive time shifts. Other services are

*Output-only and Input/Ouptut subspace-based identification*,

*Automated on-line identification package*,

*Subspace-based identification through moving sensors data fusion*,

*Damage detection and monitoring*,

*Damage localization*,

The modules have been tested by different partners, especially the French industrial partners, EADS, Dassault and Sopemea, within the FLITE2 project, by partners from the past CONSTRUCTIF project, and within the framework of bilateral contracts with SNECMA and SVS.

Based on intensive internal evaluation of the toolbox, on both simulated and real data sets, EADS Space Transportation and CNES have been investigating how to use the toolbox for the exploitation of the Ariane 5 flight data sets.

This Scilabtoolbox continues to play the role of a programming and development environment for all our newly designed algorithms. Moreover, offering a
*maintained*Scilabplatform turns out to be a crucial factor in convincing industrial partners to undertake joint investigations with us. Just recently, SNECMA funded development for the
Cosmad toolbox in 2010.

Three software have been deposed to the Agency of Program Protection, i.e.

1/ Fast multi-order Stochastic Subspace Identification (FMO-SSI) IDDN.FR.001.100017.000.S.P.2011.000.20700

2/ Multi-setup Stochastic Subspace Identification (MS-SI) IDDN.FR.001.100016.000.S.P.2011.000.20700

3/ Multi-order confidence interval computation for single-setup and multi-setup Stochastic Subspace Identification (MOCI-SSI) IDDN.FR.001.100018.000.S.P.2011.000.20700

They will be transferred to partners and industrial contracts, starting in 2011.

In Operational Modal Analysis (OMA) of large structures it is often needed to process sensor data from multiple non-simultaneously recorded measurement setups, especially in the case of large structures.. In this work a new efficient variant of the PreGER algorithm is presented that avoids the numerical explosion of the calculation by using a modular approach, where the data from the measurement setups is processed setup by setup and not at the same time .

Stochastic subspace identification methods are an efficient tool for system identification of mechanical systems in Operational Modal Analysis (OMA), where modal parameters are estimated from measured vibrational data of a structure. System identification is usually done for many successive model orders, as the true system order is unknown and identification in results at different model orders need to be compared to distinguish true structural modes from spurious modes in so-called stabilization diagrams. An algorithm to estimate the system matrices at multiple model orders has been derived .

In Operational Modal Analysis, the modal parameters (natural frequencies, damping ratios and mode shapes) obtained from Stochastic Subspace Identification (SSI) of a structure, are afflicted with statistical uncertainty. A variant of this approach has been derived for the Eigenvalue-Realization-Algorithm (ERA) . Another version has been proposed for the merging subspace algorithm , . This approach has been validated on large scale examples .

Statistical methods using output-only data have been shown to offer a robust solution to the damage detection task. These techniques have also been combined with sensitivities extracted from

finite element models to offer information on the location of damage accounting for uncertainties in the

finite element sensitivities. In some applications, however, the formulation of the

finite element model makes implementation impractical and this motivates the search for model-free damage localization alternatives. One option is to use experimentally extracted sensitivities but their computation requires a set of constants (usually absorbed in the normalization of the eigenvectors) that are not available in output only identification. The noted limitation can be circumvented by adding a known perturbation to the mass distribution and repeating the output only identification, a procedure that can be practical in some cases. Linking a null-space based subspace damage index with experimentally extracted sensitivities allows us to infer on the position of damage without formulating a

finite element model and without the need for input measurements. The performance of the algorithm is illustrated on simulated data . Damage detdction has also been applied to a large scale example of an european project , .

Subspace methods enjoy some popularity, especially in mechanical engineering, where large model orders have to be considered. In the context of detecting changes in the structural properties and the modal parameters linked to them, some subspace based fault detection residual has been recently proposed and applied successfully. However, most works assume that the unmeasured ambient excitation level during measurements of the structure in the reference and possibly damaged condition stays constant, which is not possible in any application. This work addresses the problem of robustness of such fault detection methods. A subspace-based fault detection test is derived that is robust to excitation change but also to numerical instabilities that could arise easily in the computations .

In this work, an extension of the output-only subspace identification, to the class of linear periodically time-varying (LPTV) systems, is proposed. The goal is to identify a useful information about the system's stability using the Floquet theory which gives a necessary and sufficient condition for stability analysis .

Output only techniques rely on the presence on unknown turbulence, which may or may not be enough to excite the system. A new approach for applying artificial input to the system for maximizing detection and identifiability has been developed. This work considers the problem of auxiliary input design for subspace-based fault detection methods. In several real applications, particularly in the damage detection of mechanical structures and vibrating systems, environment noise is the only input to the system. In some applications, white noise produces low quality output data for the subspace-based fault detection method. In those methods, a residual is calculated to detect the fault based on the output information. However, some modes of the system may not influence the outputs and the residual appropriately if the input is not exciting enough for those modes. In this work, rotated inputs method is implemented to excite the system modes. In addition to produce a residual more sensitive to the weak modes, it is possible to detect system order changes due to the fault using the rotated inputs. Simulation results demonstrate the efficiency of injecting these auxiliary inputs to improve the subspace-based fault detection methodology . This work is funded by FP7-NMP Large Scale Integrated Project IRIS.

Annual agreement INRIA-SVS 2381 + contract 4329

SVS (Structural Vibration Solutions A/S) is a company located in Aalborg, Denmark, having strong connections with the Department of Civil Engineering of University of British Columbia, CA (Prof. Carlos Ventura).

SVS and I4S are investigating how to link the modal analysis software ARTeMIS of SVS and COSMAD. Through an annual agreement, I4S gets a license of ARTeMIS in exchange to offer support for integrating our damage detection software into SVS software and offerings. A contract has been signed, where I4S provides algorithms and expertise for integration within a damage detection structural health monitoring system and SVIBS does the implementation. This technology transfer has been funded by the ministry of transportation of British Columbia, Canada. The work is supervised by UBC, CA. The end product will be a web based structural health monitoring system for in operation bridges.

I4S is doing technology transfer towards SVS to implement I4S technologies into ARTEMIS Extractor Pro. This is done under a royalty agreement between INRIA and SVS . First achievements include the implementation of the so called Crystal Clear SSI, a subspace variant, with much lower signal to noise ratio, and whose interest in the mechanical engineering community is very high.This year, the merging algorithm and the fast implementation of subspace methods has been performed. Other I4S algorithms are currently under review to be integrated within ARTEMIS. SVS and I4S are also related in the related IAPP ISMS and the SIMS project.

Contracts INRIA signed in December 2009 (2009-alloc 4589) and July 2010 (2010-alloc 5110).

In 2007, I4S has investigated for SNECMA an identification case study on some undisclosed engine structure. Successful results yield to the delivery of the COSMAD toolbox for internal evaluation at SNECMA. The end goal is the use of COSMAD in the industrial process of SNECMA. Internal evaluation of COSMAD has been performed inhouse by SNECMA in 2008. A contract has been signed and some software package has been developed to suit SNECMA needs in 2011. Work on the SNECMA prototype has been performed in 2009 to 2011.

Following the FliTE2 project, a joint PhD thesis between INRIA and Dassault Aviation has been initiated. The thesis will pursue the work achieved in FliTE2 and started in June 2011 funded by Dassault Aviation and the CIFRE Agency.

Contract INRIA 4162

I4S is implied in a national project for aircraft SHM starting Fall 2009. This project will improve on monitoring procedures developed in previous projects to provide some algorithms for use in Dassault Aviation aircraft monitoring procedures. I4S works together with Qinghua Zhang of INRIA Rocquencourt, project team SISYPHE, on this topic.

I4S is related to the forthcoming project FUI SIPRIS (Systèmes d'Instrumentation pour la prévention des risques), lead by Advitam.

I4S has started a 2 year collaboration with EPI ALEA on using particular filtering in vibration analysis. A new engineer has been hired for that task, starting October 2010.

A new PhD student, Ahmed Jhinaoui has started a new thesis on helicopter instability. This thesis is codirected by professor Morlier from ISAE, France. This thesis is funded by FP7-NMP Large Scale Integrated Project IRIS.

Type: PEOPLE

Instrument: Industry-Academia Partnerships and Pathway (IAPP)

Duration: September 2010 - August 2013

Coordinator: SVS (Structural Vibrations Solutions) (Denmark)

Others partners: University of British Columbia, Canada

In 2009, a proposal has been submitted with SVS, University of British Columbia and I4S to develop a framework for handling structural health monitoring methods. This proposal implies some long stay of the concerned people, Laurent Mevel and Michael Döhler for I4S abroad. Palle Andersen and one of its engineer from SVS are assumed to stay 9 months at INRIA, for tighten integration of COSMAD and ARTEMIS software. The proposal has been rated 88/100 and ranked A in the final selection procedure. The project has been signed on August 1st 2010 and has been running from September 1st. Michael Döhler is spending 5 months in 2010-2011 in Danemark.

Type: Cooperation

Instrument: Collaborative project -Large Scale Integrating project

Duration: October 2008 - March 2012

Coordinator: VCE, Austria (Denmark)

Others partners: 40 partners

IIRIS (
*Integrated European Industrial Risk Reduction System*), which helds its kick off meeting in October 2008. This project has been elaborated within the framework of the SAMCO
association. I4S is involved in the online monitoring sub-project.

I4S is involved in the core consortium of this FP7-NMP Large Scale Integrated Project.

INRIA is involved in Group 3 about Structural Health Monitoring. I4S works with Sheffield University and BAM (Germany) for development of tools for structural damage detection for bridges and wind farms. Laurent Mevel is also member of the core IRIS Vision group, and is responsible of the scientific coherency of the project.

A new project called SIMS is currently ongoing on vibration analysis and monitoring in Canada. This project is funded by Ministry of Transport, British Columbia, Canada. It implies deep collaboration with University of British Columbia, Canada. This project has connexions with partners of IRIS project, including University of Tokyo, Japan.

This collaboration involves a new PhD student, Luciano Marin, and is involving Professor Bernal from University of Boston, USA. Professor Bernal visited us for one week in 2010.

D. Bernal of Northeastern University, Boston visited us in October 2011.

H. Vollesen was 6 months in 2011 in I4S during the ISMS project.

L. Mevel is part of the IOMAC organisation committee. He is also reviewer for numerous journals and conference boards.

PhD : Michael Döhler, Subspace based system identification and fault detection : Algorithms for large systems and application to structural vibration analysis, Université de Rennes 1, 10/10/11, L. Mevel .

PhD: Xuan-Binh Lam, Uncertainty quantification for Subspace methods, Université de Rennes 1, 26/10/11, L. Meve .

PhD in progress : Ambient diagnosis and early instability monitoring for helicopter rotor : Ahmed Jhinaoui, ,since June 2010, L.Mevel and J. Morlier (ISAE)

PhD in progress : Algorithms for monitoring and localization of damage. Luciano Marin, since October 2010, L.Mevel and D. Bernal (University of NorthEastern, Boston, USA)

PhD in progress : Aeroelastic instability early detection methods in frequency domain. Philippe Mellinger, since June 2011, L. Mevel and C. Meyer (Dassault Aviation)