Section: New Results

Damage detection and linear state analysis

Vibration monitoring by eigenstructure change detection based on perturbation analysis

Participants : Michael Doehler, Qinghua Zhang, Laurent Mevel.

Vibration monitoring, notably in the fields of civil, mechanical and aeronautical engineering, aims at detecting damages at an early stage, in general by using output-only vibration measurements under ambient excitation. In this work, a new method is developed for the detection of small changes in the eigenstructure of such systems. The main idea is to transform the multiplicative eigenstructure change detection problem to an additive one, by means of perturbation analysis based on the assumption of small eigenstructure changes. Another transformation then further simplifies the detection problem into the framework of a linear regression subject to additive white Gaussian noises, leading to a numerically efficient solution of the considered problem. Compared to existing methods, it has the advantages of focusing on chosen system parameters and efficiently addressing random uncertainties. The results of this study have been presented in [31] .

Stochastic hybrid system actuator fault diagnosis by adaptive estimation

Participant : Qinghua Zhang.

Based on the interacting multiple model (IMM) estimator for hybrid system state estimation and on the adaptive Kalman filter for time varying system joint state-parameter estimation, a new algorithm, the adaptive IMM estimator, is developed in this work for actuator fault diagnosis in stochastic hybrid systems. The working modes of the considered hybrid systems are described by stochastic state-space models, and the mode transitions are characterized by a Markov model. Actuator faults are modeled as parameter changes, and the related fault diagnosis problem is solved by the proposed adaptive IMM estimator through joint state-parameter estimation. This study has been accomplished in the framework of the ITEA MODRIO project and the results have been presented in [40] .

Damage detection on real structures

Participants : Dominique Siegert, Laurent Mevel.

This article presents the feasibility study of a new structure for a 10-m-span bridge deck, taking into account the possibilities offered by new and high-strength materials and the advantages of a traditional environmental-friendly material. Small localized damages are hardly detected by global monitoring methods. The effectiveness of vibration-based detection depends on the accuracy of the modal parameter estimates and is limited by the low sensitivity of the modal parameters to a local stiffness reduction. This paper presents the application of SSDD to detect the change of the modal parameters of the investigated structure. Further analysis with a finite element model was conducted for assessing the consistency of the expected location and extent of the damaged elements. [15] .

Damage detection and simulated validation

Participants : Michael Doehler, Laurent Mevel, Saeid Allahdadian.

This section is devoted to the numerical and theoretical validation of stochastic subspace damage detection. Sample length and sensor noise robustness were investigated. [24] , [23] , [25] .

Damage quantification

Participants : Michael Doehler, Laurent Mevel.

Fault detection for structural health monitoring has been a topic of much research during the last decade. Localization and quantification of damages, which are linked to fault isolation, have proven to be more challenging, and at the same time of higher practical impact. While damage detection can be essentially handled as a data-driven approach, localization and quantification require a strong connection between data analysis and physical models. This paper builds upon a hypothesis test that checks if the mean of a Gaussian residual vector whose parameterization is linked to possible damage locations has become non-zero in the faulty state. It is shown how the damage location and extent can be inferred and robust numerical schemes for their estimation are derived based on QR decompositions and minmax approaches. Finally, the relevance of the approach is assessed in numerical simulations of two structures.[30] .

Optical fiber for damage detection

Participant : Dominique Siegert.

A technique has been developed to detect and quantify structural damages. It consists of updating the model parameters associated to the damage, i.e. Young modulus, from strain sensor outputs obtained by optical fiber. Early damage detection can be expected using the local information given by the strain measurement. The method has been applied to a 8 meter post-tensioned concrete beam under a static loading. The model updating problem can be formulated as a minimization problem, i.e minimize a data misfit functional. To solve this problem, we use a gradient-based method. The gradient of the functional is computed at a low computational cost by means of the adjoint state. The technique is able to detect the damaged area in a post-tensioned concrete beam and to estimate its level of damage. [38]