Statistical subspace-based damage detection with estimated reference

International audienceThe statistical subspace-based damage detection technique has shown promising theoretical and practical results for vibration-based structural health monitoring. It evaluates a subspace-based residual function with efficient hypothesis testing tools, and has the ability of detecting small changes in chosen system parameters. In the residual function, a Hankel matrix of output covariances estimated from test data is confronted to its left null space associated to a reference model. The hypothesis test takes into account the covariance of the residual for decision making. Ideally, the reference model is assumed to be perfectly known without any uncertainty, which is not a realistic assumption. In practice, the left null space is usually estimated from a reference data set to avoid model errors in the residual computation. Then, the associated uncertainties may be non-negligible, in particular when the available reference data is of limited length. In this paper, it is investigated how the statistical distribution of the residual is affected when the reference null space is estimated. The asymptotic residual distribution is derived, where its refined covariance term considers also the uncertainty related to the reference null space estimate. The associated damage detection test closes a theoretical gap for real-world applications and leads to increased robustness of the method in practice. The importance of including the estimation uncertainty of the reference null space is shown in a numerical study and on experimental data of a progressively damaged steel frame

Stochastic Subspace-Based Damage Detection with Uncertainty in the Reference Null Space

International audienceThis paper deals with uncertainty considerations in damage diagnosis using the stochas-tic subspace-based damage detection technique. With this method, a model is estimated from data in a (healthy) reference state and confronted to measurement data from the possibly damaged state in a hypothesis test. Previously, only the uncertainty related to the measurement data was considered in this test, whereas the uncertainty in the estimation of the reference model has not been considered. We derive a new test framework, which takes into account both the uncertainties in the estimation of the reference model as well as the uncertainties related to the measurement data. Perturbation theory is applied to obtain the relevant covariances. In a numerical study the effect of the new computation is shown, when the reference model is estimated with different accuracies, and the performance of the hypothesis tests is evaluated for small damages. Using the derived covariance scheme increases the probability of detection when the reference model estimate is subject to high uncertainty, leading to a more reliable test

Fault detection for linear parameter varying systems under changes in the process noise covariance

International audienceDetecting changes in the eigenstructure of linear systems is a comprehensively investigated subject. In particular, change detection methods based on hypothesis testing using Gaussian residuals have been developed previously. In such residuals, a reference model is confronted to data from the current system. In this paper, linear output-only systems depending on a varying external physical parameter are considered. These systems are driven by process noise, whose covariance may also vary between measurements. To deal with the varyingparameter, an interpolation approach is pursued, where a limited number of reference models { each estimated from data measured in a reference state { are interpolated to approximate an adequate reference model for the current parameter. The problem becomes more complex whenthe different points of interpolation correspond to dierent noise conditions. Then conicts may arise between the detection of changes in the eigenstructure due to a fault and the detection of changes due to dierent noise conditions. For this case, a new change detection approach is developed based on the interpolation of the eigenstructure at the reference points. The resulting approach is capable of change detection when both the external physical parameter and the process noise conditions are varying. This approach is validated on a numerical simulation of a mechanical system

Asymptotic analysis of subspace-based data-driven residual for fault detection with uncertain reference

SAFEPROCESS 2018, 10th IFAC Symposium on Fault Detection, Diagnosis and Safety of Technical Processes, Varsovie, POLOGNE, 29-/08/2018 - 31/08/2018International audienceThe local asymptotic approach is promising for vibration-based fault diagnosis when associated to a subspace-based residual function and efficient hypothesis testing tools. It has the ability of detecting small changes in some chosen system parameters. In the residual function, the left null space of the observability matrix associated to a reference model is confronted to the Hankel matrix of output covariances estimated from test data. When this left null space is not perfectly known from a model, it should be replaced by an estimate from data to avoid model errors in the residual computation. In this paper, the asymptotic distribution of the resulting data-driven residual is analyzed and its covariance is estimated, which includes also the covariance related to the reference null space estimate. The importance of including the covariance of the reference null space estimate is shown in a numerical study

Subspace-based damage detection with rejection of the temperature effect and uncertainty in the reference

International audienceTemperature variation can be a nuisance that perturbs vibration based structural health monitoring (SHM) approaches for civil engineering structures. In this paper, temperature affected vibration data is evaluated within a stochastic damage detection framework, which relies on a null space based residual. Besides two existing temperature rejection approaches-building a reference state from an averaging method or a piecewise method-a new approach is proposed, using model interpolation. In this approach, a general reference model is obtained from data in the reference state at several known reference temperatures. Then, for a particular tested temperature, a local reference model is derived from the general reference model. Thus, a well fitting reference null space for the formulation of a residual is available when new data is tested for damage detection at an arbitrary temperature. Particular attention is paid to the computation of the residual covariance, taking into account the uncertainty related to the null space matrix estimate. This improves the test performance, contrary to prior methods, for local and global damages, resulting in a higher probability of detection (PoD) for the new interpolation approach compared to previous approaches

Stochastic subspace-based damage detection of a temperature affected beam structure

International audienceStructural health monitoring (SHM) of civil structures often is limited due to changing environmental conditions, as those changes affect the structural dynamical properties in a similar way like damages can do. In this article, an approach for damage detection under changing temperatures is presented and applied to a beam structure. The used stochastic subspace-based algorithm relies on a reference null space estimate, which is confronted to data from the testing state in a residual function. For damage detection the residual is evaluated by means of statistical hypothesis tests. Changes of the system due to temperature effects are handled with a model interpolation approach from linear parameter varying system theory. From vibration data measured in the undamaged state at some few reference temperatures, a model of the dynamic system valid for the current testing temperature is interpolated. The reference null space and the covariance matrix for the hypothesis test is computed from this interpolated model. This approach has been developed recently and was validated in an academic test case on simulations of a mass-spring-damper. In this paper, the approach is validated experimentally on a beam structure under varying temperature conditions in a climate chamber. Compared to other approaches, the interpolation approach leads to significantly less false positive alarms in the reference state when the structure is exposed to different temperatures, while faults can still be detected reliably

Détection statistique d’endommagements pour les ouvrages d’art par méthodes sous-espaces sous conditions environnementales

La détection automatisée d’endommagements basée sur les mesures vibratoires est pertinente pour la surveillance de l’intégrité des ouvrages d'art. Dans ce contexte, la détection basée sur les techniques sous-espace (SSDD) compare statistiquement les mesures à un modèle de référence. Dans cette thèse, une nouvelle approche est proposée pour améliorer la robustesse de la SSDD pour un usage applicatif réaliste. Tout d'abord, un test statistique est formulé tenant compte des incertitudes statistiques liées aux erreurs sur le modèle obtenu sur des données de référence. Cela conduit à une description précise des propriétés statistiques du test et des seuils d’alarme. Deuxièmement, une approche a été développée pour tenir compte des effets environnementaux sur la SSDD. A partir de mesures de référence dans plusieurs conditions environnementales différentes, un test est proposé détectant un défaut par rapport à une référence obtenue par interpolation. Les méthodes développées sont validées par des simulations numériques et appliquées à des données expérimentales obtenues en laboratoire et sur structures installées en extérieur.Automated vibration-based damage detection is of increasing interest for structural health monitoring of engineering structures. In this context, stochastic subspace-based damage detection (SSDD) compares measurements from a testing state to a data-driven reference model in a statistical framework. In this thesis theoretical developments have been proposed to improve the robustness of SSDD for realistic applications conditions. First, a statistical test has been proposed considering the statistical uncertainties about the model obtained from the reference data. This leads to a precise description of the test's distribution properties and damage detection thresholds. Second, an approach has been developed to account for environmental effects in SSDD. Based on reference measurements at few different environmental conditions, a test is derived with respect to an adequate interpolated reference. The proposed methods are validated in numerical simulations and applied to experimental data from the laboratory and outdoor structures

Cordycepin Induces Apoptosis of Cgth W-2 Thyroid Carcinoma Cells through the Calcium-Calpain-Caspase 7-Parp Pathway

Cordycepin, a nucleoside isolated from Cordyceps sinensis, is an inhibitor of polyadenylation and has an antitumor effect. We used CGTH W-2 , a follicular thyroid carcinoma cell line, to study the mechanism of the anticancer effect of cordycepin. Cordycepin decreased cell viability and resulted in apoptosis but not necrosis. Cordycepin increased intracellular calcium levels triggering calpain activation, which led to apoptosis. BAPTA/AM and calpeptin inhibited the cordycepin-induced cleavage of caspase 7 and poly (ADP- ribose) polymerase (PARP), implying an upstream role of calcium and calpain. CGTH W-2 cells expressed four subtypes of adenosine receptors (AR), A(1)AR, A(2A)AR, A(2B)AR, and A (3)AR. Specific antagonists to AR subtypes all blocked cordycepin-induced apoptosis to different degrees. Small interfering RNA for A(1)AR and A(3)AR abrogated cordycepin- induced apoptosis. In conclusion, the cordycepin-induced apoptosis of CGTH W-2 cells is mediated by the calcium- calpain-caspase 7- PARP pathway, and ARs are involved in the apoptotic effect of cordycepin

Output-only subspace and transfer matrix-based damage localization and quantification

International audienceThis paper deals with vibration-based damage localization and quantification from output-only measurements. We describe an approach which operates on a data-driven residual vector that is statistically evaluated using information from a finite element model, without updating the parameters of the model. First, the damaged elements are detected in statistical tests, and second, the damage is quantified only for the damaged elements. We propose a new residual vector in this context that is based on the transfer matrix difference between reference and damaged states, and compare it with a previously introduced subspace-based residual. We show localization and quantification on both residuals in simulations