Sensor Validation for On-line Vibration Monitoring

For a reliable on-line vibration monitoring of structures, it is necessary to have accurate sensor information. However, sensors may sometimes be faulty or may even become unavailable due to failure or maintenance activities. The problem of sensor validation is therefore a critical part or structural identification. The objective of the present study is to present a procedure based on principal component analysis, which is able to perform detection, isolation and reconstruction of a faulty sensor. Its e ciency is assessed using an experimental application

Actuator / Sensor Placement and Experimental Modal Analysis on Piezo-Structures

Few works have been performed in the field of experimental modal analysis by means of piezoelectric distributed elements. Piezoelectric laminates, initially intended for the monitoring and the control of smart structures, could also be dedicated to the experimental modal identification of an open-loop structure. A pole-residue development of the open-loop piezo-structure shows that conventional algorithms and a piezoelectric pseudo-collocated actuator/sensor may be used to estimate the mechanical modal parameters. When an initial mathematical model of the structure is available, the 'best' excitation position is determined by checking the H2 norm of the transfer function of the fully observed system. The same methodology can be applied for the selection of the monitoring locations. In most cases, experimental testing with the selected sensors set, gives acceptable information to identify target modes. These data, coupled with electrical sensing at the piezoelectric element level, can then be used to perform the modal analysis of the piezo-structure. A light clamped-free plate instrumented with piezo-laminates is used to illustrate this experimental approach

Identification of Electromechanical Coupling in Piezo-Structures

An identification method based on a structural model updating procedure may be used to improve the knowledge of a piezoelectric tested structure and to determine the coupling coefficients of the piezoelectric material. This procedure starts with the modal analysis of the open-loop instrumented structure. Let the target modes be a subset of the model modes; a selection of sensor (generally accelerometers) locations is then performed by determining the smaller subset such that the H2 modal norm is as close as possible to the modal norm of the original full set. In most cases, experimental testing with the selected sensor set will give acceptable information to identify target modes. These data, coupled with electrical sensing at the piezoelectric element level, will then be used to perform modal analysis of the piezo-structure. A pole-residue development of the open-loop piezo-structure shows that conventional algorithms may be used to estimate the mechanical modal parameters and the electromechanical coupling matrix. The second step of the procedure is to perform model updating itself. The initial finite element piezoelectric model will be improved stiffness corrections to the global stiffness matrix.. The corrections are split in their mechanical and electromechanical contributions. It is then possible to separate mechanical modelling errors from electromechanical coupling errors. The problem becomes a classical model updating problem which may be solved using well established techniques. This will result not only in a model behaving like the measures, but also in an improved knowledge of the structure behaviour without loosing physical insight. From a numerical point of view, it will be shown that ill-conditioning inherent to the presence of piezoelectric elements presents some difficulties at different steps of the model correction procedure. A clamped-free plate instrumented with piezo-laminates is used to illustrate the model updating approach. The selection of measurement points, using the modal norm criteria, is also presented. Experimental identification data will then be used as inputs for the model correction procedure and the behaviour of the updated model will be compared with the initial model dynamics

Damage Localisation Using Principal Component Analysis of Distributed Sensor Array

The spatial information given by the distributed sensors (e.g., piezoelectric laminates) can be used to forecast structural damage on localised critical spot. It is well known that a localised structural damage with relative small amplitude does not affect significantly the modal response of the structure, at least at low frequencies. Nevertheless, a local de-lamination or electrode deterioration at the distributed sensor level will show significant changes on the response of the sensor by modifying its apparent electromechanical coupling. Assuming that the number of sensors is greater than the number of involved structural modes, a local structural damage, with relative small amplitude, will only affect a particular distributed sensor without affecting significantly the response of the others. By applying a principal component analysis (PCA) on the sensor time responses, it is possible to see that any change of one particular sensor electromechanical coupling factor will affect the subspace generated by the complete sensor response set. The subspace generated with the damaged structure can then be compared with the subspace of an initial state in order to diagnose damage or not

Strategies for Distributed Piezoelectric Actuator / Sensor Placement by Noise Effect Minimisation and Modal Controllability / Observability

This paper investigates the problem of placement procedure for distributed piezoelectric actuators and sensors. Two placement techniques are proposed. The first one is based on the controllability and observability Grammians of the system expressed in a modal state-space coordinates. The controllability Grammian is then able to quantify how structural modes are controllable with a set of predefined actuators, while the observability Grammian expresses how much structural modes can be observed from a set of predefined sensors. The second placement technique is based on the selection of the best sensor sets which, for each selected structural modes, have the best signal to noise ratio. The sensor selection is performed by inspecting the Fisher information matrix. The number of sensors is then reduced, in an iterative manner, by eliminating locations that do not contribute significantly to the linear independence of the target modal partitions

Structural damage diagnosis based on stochastic subspace identification, Kalman model, and principal component analysis.

Cet article porte sur l’utilisation de techniques d’analyse de processus statistiques pour la détection et la localisation d’endommagements structuraux à partir de mesures vibratoires. La première approche proposée dans ce travail se base sur la méthode d’identification des sous-espaces stochastiques pour construire un modèle de Kalman représentatif de l’état initial (de référence) de la structure. Ce modèle est alors utilisé pour réaliser une prédiction des réponses nouvellement mesurées. L’analyse statistique de l’erreur de reconstruction du modèle permet de définir un critère de détection de l’apparition d’un défaut. L’intérêt de cette méthode est que seule l’identification du moèle pour les données de référence est nécessaire. La détection de l’endommagement peut alors être effectuée de manière automatique par surveillance de la structure sans nécessiter de nouvelle identification. Dans la seconde approche, l’analyse en composantes principales des réponses est utilisée pour extraire les directions principales (les caractéristiques) permettant de définir un sous-espace représentatif du comportement dynamique de la structure. Le moindre changement dans la réponse d’un capteur affecte l’espace sous-tendu par l’ensemble de tous les capteurs. Par conséquent, la comparaison entre les sous-espaces correspondant respectivement à la structure saine et la structure actuelle (potentiellement endommagée) permet de détecter l’apparition éventuelle d’un endommagement. L’analyse en composantes principales peut également être réalisée sur un sous-ensemble de capteurs dans le but de localiser le(s) capteur(s) responsable(s) de l’apparition du défaut, et par conséquent, la sous-structure endommagée.This paper deals with the application of statistical process control techniques for damage diagnosis based on vibration measurements. The first approach considered in this work is based the Stochastic Subspace Identification (SSI) algorithm, from which a Kalman model is constructed to fit the measured response histories of the undamaged (reference) structure. This model may be used to make a prediction of the newly measured responses. The residual error between the model predictions and the actual measurements is defined as a damage-sensitive feature. Outlier statistics provides then a quantitative indicator of damage. The advantage of the method is that model extraction has to be performed only once using the reference data and that no further modal identification is needed. Thus on-line structural health monitoring may easily be realized. In the second approach, principal component analysis (PCA) of the sensor time-responses is used to extract principal directions (i.e. features), which define a subspace that is representative of the dynamics of the instrumented structure. Any change in the response of a single sensor affects the subspace spanned by the complete sensor response set. It follows that the subspace corresponding to the current state of the structure can be compared to the subspace of the initial state of the structure, assumed to be healthy, in order to diagnose possible damage. Principal component analysis may also be performed for every potential subset of damaged sensors in order to identify the involved sensor, and, therefore, the damaged substructure

Substructure Damage Detection by Principal Component Analysis : Application to Environmental Vibration Testing

This paper deals with the application of statistical process control techniques based on principal component analysis to vibration-based damage diagnosis of structures. It is well known that localized structural damages with relative small amplitude may not much affect the global modal response of the structure, at least at low frequencies. Nevertheless, it can be expected that the local dynamic behavior of the damaged structural component is significantly affected. By applying principal component analysis on the sensor time responses, it may be shown that any change of a particular sensor will affect the subspace spanned by the response of the complete sensor set. The subspace corresponding to the damaged structure can then be compared with the subspace of an initial state in order to diagnose possible damages. The problem of structural damage detection is addressed in the case of a fatigue test by means of an electro-dynamic shaker. In this example, monitoring of the structural responses is performed during a qualification test in order to detect any structural damage

Structural integral monitoring by vibration measurements

peer reviewedThis paper presents a comparative study on several approaches of structural damage diagnosis based on vibration measurements. Stochastic subspace identification method is used to identify modal parameters and to generate a Kalman prediction model, which are taken as damage-sensitive features for structural damage detection. A statistical process control technique based on principal component analysis (PCA) is also presented. An improvement and enhancement of PCA are proposed. It is assumed that without damage, structural responses should remain approximately in a hyperplane defined by the principal directions of data. Damage localization is explored with these methods. As only the measured output signals are needed, the methods are convenient for an on-line monitoring. The efficiency and limitation of the proposed methods are illustrated by a practical application

Structural Integrity Monitoring by Vibration Measurements

This paper presents a comparative study on several approaches of structural dam-age diagnosis based on vibration meas-urements. Stochastic subspace identifica-tion method is used to identify modal pa-rameters and to generate a Kalman predic-tion model, which are taken as damage-sensitive features for structural damage detection. A statistical process control technique based on principal component analysis (PCA) is also presented. An im-provement and enhancement of PCA is proposed. It is assumed that without dam-ages, structural responses should remain approximately in a hyperplane defined by the principal directions of data. Damage localization is explored with these meth-ods. As only the measured output signals are needed, the methods are convenient for on-line monitoring. The efficiency and limitation of the proposed methods are illustrated by numerical and practical ap-plications

Principal component analysis of Piezo-Sensor Array for damage localization

This paper focuses on applying statistical process control techniques, based on principal component analysis, to vibration-based damage diagnosis. It is well known that localized structural damages with relative small amplitude do not affect much the global modal response of the structure, at least at low frequencies. Nevertheless, it can be expected that the local dynamic behavior of a damaged structural subcomponent is significantly affected. Assuming that each structural subcomponent is monitored, local structural damage, with relative small amplitude, will only affect a particular sensor without affecting significantly the response of the others. By applying a principal component analysis on the sensor time responses, it is possible to see that any change of one particular sensor will affect the subspace spanned by the complete sensor response set. The subspace corresponding to the damaged structure can then be compared with the subspace of an initial state in order to diagnose possible damage. The principal component analysis may also be performed for every potential subset of damaged sensors in order to identify the involved sensor, and, therefore, the damaged structural component. The spatial information given by the distributed sensors (e.g. piezoelectric laminates) can be used to forecast structural damages on a critical area but damage localization is also possible with classical sensors (e.g. accelerometers). The damage may be located as the errors attain the maximum at the sensors instrumented in the damaged substructures