Operational Modal Analysis on laminated glass beams

Laminated glass is a sandwich element consisting of two or more glass sheets, with one or more interlayers of polyvinyl butyral (PVB). The dynamic response of laminated glass beams and plates can be predicted using analytical or numerical models in which the glass and the PVB are usually modelled as linear-elastic and linear viscoelastic materials, respectively. In this work the dynamic behavior of laminated glass beams are predicted using a finite element model and the analytical model of Ross-Kerwin-Ungar. The numerical and analytical results are compared with those obtained by operational modal analysis performed at different temperatures.

An approach to operational modal analysis using the expectation maximization algorithm

This paper presents the Expectation Maximization algorithm (EM) applied to operational modal analysis of structures. The EM algorithm is a general-purpose method for maximum likelihood estimation (MLE) that in this work is used to estimate state space models. As it is well known, the MLE enjoys some optimal properties from a statistical point of view, which make it very attractive in practice. However, the EM algorithm has two main drawbacks: its slow convergence and the dependence of the solution on the initial values used. This paper proposes two different strategies to choose initial values for the EM algorithm when used for operational modal analysis: to begin with the parameters estimated by Stochastic Subspace Identification method (SSI) and to start using random points. The effectiveness of the proposed identification method has been evaluated through numerical simulation and measured vibration data in the context of a benchmark problem. Modal parameters (natural frequencies, damping ratios and mode shapes) of the benchmark structure have been estimated using SSI and the EM algorithm. On the whole, the results show that the application of the EM algorithm starting from the solution given by SSI is very useful to identify the vibration modes of a structure, discarding the spurious modes that appear in high order models and discovering other hidden modes. Similar results are obtained using random starting values, although this strategy allows us to analyze the solution of several starting points what overcome the dependence on the initial values used.

Modal contribution and state space order selection in operational modal analysis

The estimation of modal parameters of a structure from ambient measurements has attracted the attention of many researchers in the last years. The procedure is now well established and the use of state space models, stochastic system identification methods and stabilization diagrams allows to identify the modes of the structure. In this paper the contribution of each identified mode to the measured vibration is discussed. This modal contribution is computed using the Kalman filter and it is an indicator of the importance of the modes. Also the variation of the modal contribution with the order of the model is studied. This analysis suggests selecting the order for the state space model as the order that includes the modes with higher contribution. The order obtained using this method is compared to those obtained using other well known methods, like Akaike criteria for time series or the singular values of the weighted projection matrix in the Stochastic Subspace Identification method. Finally, both simulated and measured vibration data are used to show the practicability of the derived technique. Finally, it is important to remark that the method can be used with any identification method working in the state space model.

Maximum likelihood estimation of new state space models for operational modal analysis

The modal analysis of a structural system consists on computing its vibrational modes. The experimental way to estimate these modes requires to excite the system with a measured or known input and then to measure the system output at different points using sensors. Finally, system inputs and outputs are used to compute the modes of vibration. When the system refers to large structures like buildings or bridges, the tests have to be performed in situ, so it is not possible to measure system inputs such as wind, traffic, . . .Even if a known input is applied, the procedure is usually difficult and expensive, and there are still uncontrolled disturbances acting at the time of the test. These facts led to the idea of computing the modes of vibration using only the measured vibrations and regardless of the inputs that originated them, whether they are ambient vibrations (wind, earthquakes, . . . ) or operational loads (traffic, human loading, . . . ). This procedure is usually called Operational Modal Analysis (OMA), and in general consists on to fit a mathematical model to the measured data assuming the unobserved excitations are realizations of a stationary stochastic process (usually white noise processes). Then, the modes of vibration are computed from the estimated model. The first issue investigated in this thesis is the performance of the Expectation- Maximization (EM) algorithm for the maximum likelihood estimation of the state space model in the field of OMA. The algorithm is described in detail and it is analysed how to apply it to vibration data. After that, it is compared to another well known method, the Stochastic Subspace Identification algorithm. The maximum likelihood estimate enjoys some optimal properties from a statistical point of view what makes it very attractive in practice, but the most remarkable property of the EM algorithm is that it can be used to address a wide range of situations in OMA. In this work, three additional state space models are proposed and estimated using the EM algorithm: • The first model is proposed to estimate the modes of vibration when several tests are performed in the same structural system. Instead of analyse record by record and then compute averages, the EM algorithm is extended for the joint estimation of the proposed state space model using all the available data. • The second state space model is used to estimate the modes of vibration when the number of available sensors is lower than the number of points to be tested. In these cases it is usual to perform several tests changing the position of the sensors from one test to the following (multiple setups of sensors). Here, the proposed state space model and the EM algorithm are used to estimate the modal parameters taking into account the data of all setups. • And last, a state space model is proposed to estimate the modes of vibration in the presence of unmeasured inputs that cannot be modelled as white noise processes. In these cases, the frequency components of the inputs cannot be separated from the eigenfrequencies of the system, and spurious modes are obtained in the identification process. The idea is to measure the response of the structure corresponding to different inputs; then, it is assumed that the parameters common to all the data correspond to the structure (modes of vibration), and the parameters found in a specific test correspond to the input in that test. The problem is solved using the proposed state space model and the EM algorithm. Resumen El análisis modal de un sistema estructural consiste en calcular sus modos de vibración. Para estimar estos modos experimentalmente es preciso excitar el sistema con entradas conocidas y registrar las salidas del sistema en diferentes puntos por medio de sensores. Finalmente, los modos de vibración se calculan utilizando las entradas y salidas registradas. Cuando el sistema es una gran estructura como un puente o un edificio, los experimentos tienen que realizarse in situ, por lo que no es posible registrar entradas al sistema tales como viento, tráfico, . . . Incluso si se aplica una entrada conocida, el procedimiento suele ser complicado y caro, y todavía están presentes perturbaciones no controladas que excitan el sistema durante el test. Estos hechos han llevado a la idea de calcular los modos de vibración utilizando sólo las vibraciones registradas en la estructura y sin tener en cuenta las cargas que las originan, ya sean cargas ambientales (viento, terremotos, . . . ) o cargas de explotación (tráfico, cargas humanas, . . . ). Este procedimiento se conoce en la literatura especializada como Análisis Modal Operacional, y en general consiste en ajustar un modelo matemático a los datos registrados adoptando la hipótesis de que las excitaciones no conocidas son realizaciones de un proceso estocástico estacionario (generalmente ruido blanco). Posteriormente, los modos de vibración se calculan a partir del modelo estimado. El primer problema que se ha investigado en esta tesis es la utilización de máxima verosimilitud y el algoritmo EM (Expectation-Maximization) para la estimación del modelo espacio de los estados en el ámbito del Análisis Modal Operacional. El algoritmo se describe en detalle y también se analiza como aplicarlo cuando se dispone de datos de vibraciones de una estructura. A continuación se compara con otro método muy conocido, el método de los Subespacios. Los estimadores máximo verosímiles presentan una serie de propiedades que los hacen óptimos desde un punto de vista estadístico, pero la propiedad más destacable del algoritmo EM es que puede utilizarse para resolver un amplio abanico de situaciones que se presentan en el Análisis Modal Operacional. En este trabajo se proponen y estiman tres modelos en el espacio de los estados: • El primer modelo se utiliza para estimar los modos de vibración cuando se dispone de datos correspondientes a varios experimentos realizados en la misma estructura. En lugar de analizar registro a registro y calcular promedios, se utiliza algoritmo EM para la estimación conjunta del modelo propuesto utilizando todos los datos disponibles. • El segundo modelo en el espacio de los estados propuesto se utiliza para estimar los modos de vibración cuando el número de sensores disponibles es menor que vi Resumen el número de puntos que se quieren analizar en la estructura. En estos casos es usual realizar varios ensayos cambiando la posición de los sensores de un ensayo a otro (múltiples configuraciones de sensores). En este trabajo se utiliza el algoritmo EM para estimar los parámetros modales teniendo en cuenta los datos de todas las configuraciones. • Por último, se propone otro modelo en el espacio de los estados para estimar los modos de vibración en la presencia de entradas al sistema que no pueden modelarse como procesos estocásticos de ruido blanco. En estos casos, las frecuencias de las entradas no se pueden separar de las frecuencias del sistema y se obtienen modos espurios en la fase de identificación. La idea es registrar la respuesta de la estructura correspondiente a diferentes entradas; entonces se adopta la hipótesis de que los parámetros comunes a todos los registros corresponden a la estructura (modos de vibración), y los parámetros encontrados en un registro específico corresponden a la entrada en dicho ensayo. El problema se resuelve utilizando el modelo propuesto y el algoritmo EM.

Operational Modal Analysis using Expectation Maximization Algorithm

This paper presents a time-domain stochastic system identification method based on Maximum Likelihood Estimation and the Expectation Maximization algorithm. The effectiveness of this structural identification method is evaluated through numerical simulation in the context of the ASCE benchmark problem on structural health monitoring. Modal parameters (eigenfrequencies, damping ratios and mode shapes) of the benchmark structure have been estimated applying the proposed identification method to a set of 100 simulated cases. The numerical results show that the proposed method estimates all the modal parameters reasonably well in the presence of 30% measurement noise even. Finally, advantages and disadvantages of the method have been discussed.

Calibration of calculation models of wooden guardrails with Operational Modal Analysis

This study analyses the differences between two calculation models for guardrails on building sites that use wooden boards and tubular steel posts. Wood was considered an isotropic material in one model and an orthotropic material in a second model. The elastic constants of the wood were obtained with ultrasound. Frequencies and vibration modes were obtained for both models through linear analysis using the finite element method. The two models were experimentally calibrated through operational modal analysis. The results obtained show that for the three types of wood under analysis, the model which considered them as an orthotropic material fitted the experimental results better than the model which considered them as an isotropic material.

Modal analysis of buildings : quantification of uncertainties and model updating

Una estructura vibra con la suma de sus infinitos modos de vibración, definidos por sus parámetros modales (frecuencias naturales, formas modales y coeficientes de amortiguamiento). Estos parámetros se pueden identificar a través del Análisis Modal Operacional (OMA). Así, un equipo de investigación de la Universidad Politécnica de Madrid ha identificado las propiedades modales de un edificio de hormigón armado en Madrid con el método Identificación de los sub-espacios estocásticos (SSI). Para completar el estudio dinámico de este edificio, se ha desarrollado un modelo de elementos finitos (FE) de este edificio de 19 plantas. Este modelo se ha calibrado a partir de su comportamiento dinámico obtenido experimentalmente a través del OMA. Los objetivos de esta tesis son; (i) identificar la estructura con varios métodos de SSI y el uso de diferentes ventanas de tiempo de tal manera que se cuantifican incertidumbres de los parámetros modales debidos al proceso de estimación, (ii) desarrollar FEM de este edificio y calibrar este modelo a partir de su comportamiento dinámico, y (iii) valorar la bondad del modelo. Los parámetros modales utilizados en esta calibración han sido; espesor de las losas, densidades de los materiales, módulos de elasticidad, dimensiones de las columnas y las condiciones de contorno de la cimentación. Se ha visto que el modelo actualizado representa el comportamiento dinámico de la estructura con una buena precisión. Por lo tanto, este modelo puede utilizarse dentro de un sistema de monitorización estructural (SHM) y para la detección de daños. En el futuro, podrá estudiar la influencia de los agentes medioambientales, tales como la temperatura o el viento, en los parámetros modales. A structure vibrates according to the sum of its vibration modes, defined by their modal parameters (natural frequencies, damping ratios and modal shapes). These parameters can be identified through Operational Modal Analysis (OMA). Thus, a research team of the Technical University of Madrid has identified the modal properties of a reinforced-concrete-frame building in Madrid using the Stochastic Subspace Identification (SSI) method and a time domain technique for the OMA. To complete the dynamic study of this building, a finite element model (FE) of this 19-floor building has been developed throughout this thesis. This model has been updated from its dynamic behavior identified by the OMA. The objectives of this thesis are to; (i) identify the structure with several SSI methods and using different time blocks in such a way that uncertainties due to the modal parameter estimation are quantified, (ii) develop a FEM of this building and tune this model from its dynamic behavior, and (iii) Assess the quality of the model, the modal parameters used in this updating process have been; thickness of slabs, material densities, modulus of elasticity, column dimensions and foundation boundary conditions. It has been shown that the final updated model represents the structure with a very good accuracy. Thus, this model might be used within a structural health monitoring framework (SHM). The study of the influence of changing environmental factors (such as temperature or wind) on the model parameters might be considered as a future work.

State space models for multi-setup operational modal analysis

Operational Modal Analysis consists on estimate the modal parameters of a structure (natural frequencies, damping ratios and modal vectors) from output-only vibration measurements. The modal vectors can be only estimated where a sensor is placed, so when the number of available sensors is lower than the number of tested points, it is usual to perform several tests changing the position of the sensors from one test to the following (multiple setups of sensors): some sensors stay at the same position from setup to setup, and the other sensors change the position until all the tested points are covered. The permanent sensors are then used to merge the mode shape estimated at each setup (or partial modal vectors) into global modal vectors. Traditionally, the partial modal vectors are estimated independently setup by setup, and the global modal vectors are obtained in a postprocess phase. In this work we present two state space models that can be used to process all the recorded setups at the same time, and we also present how these models can be estimated using the maximum likelihood method. The result is that the global mode shape of each mode is obtained automatically, and subsequently, a single value for the natural frequency and damping ratio of the mode is computed. Finally, both models are compared using real measured data.

Operational modal analisys using joint statistical analysis of multiple records

In Operational Modal Analysis (OMA) of a structure, the data acquisition process may be repeated many times. In these cases, the analyst has several similar records for the modal analysis of the structure that have been obtained at di�erent time instants (multiple records). The solution obtained varies from one record to another, sometimes considerably. The differences are due to several reasons: statistical errors of estimation, changes in the external forces (unmeasured forces) that modify the output spectra, appearance of spurious modes, etc. Combining the results of the di�erent individual analysis is not straightforward. To solve the problem, we propose to make the joint estimation of the parameters using all the records. This can be done in a very simple way using state space models and computing the estimates by maximum-likelihood. The method provides a single result for the modal parameters that combines optimally all the records.

Estimation of modal parameters in structures using multiple time history records

In Operational Modal Analysis of structures we often have multiple time history records of vibrations measured at different time instants. This work presents a procedure for estimating the modal parameters of the structure processing all the records, that is, using all available information to obtain a single estimate of the modal parameters. The method uses Maximum Likelihood Estimation and the Expectation Maximization algorithm. Finally, it has been applied to various problems for both simulated and real structures and the results show the advantage of the joint analysis proposed.

Joint statistical analysis of multiple measurement setups for the estimation of vibrational modes

Computing the modal parameters of large structures in Operational Modal Analysis often requires to process data from multiple non simultaneously recorded setups of sensors. These setups share some sensors in common, the so-called reference sensors that are fixed for all the measurements, while the other sensors are moved from one setup to the next. One possibility is to process the setups separately what result in different modal parameter estimates for each setup. Then the reference sensors are used to merge or glue the different parts of the mode shapes to obtain global modes, while the natural frequencies and damping ratios are usually averaged. In this paper we present a state space model that can be used to process all setups at once so the global mode shapes are obtained automatically and subsequently only a value for the natural frequency and damping ratio of each mode is computed. We also present how this model can be estimated using maximum likelihood and the Expectation Maximization algorithm. We apply this technique to real data measured at a footbridge.

Uncertainties in the modal estimation from output-only data in civil structures

Cualquier estructura vibra según unas frecuencias propias definidas por sus parámetros modales (frecuencias naturales, amortiguamientos y formas modales). A través de las mediciones de la vibración en puntos clave de la estructura, los parámetros modales pueden ser estimados. En estructuras civiles, es difícil excitar una estructura de manera controlada, por lo tanto, las técnicas que implican la estimación de los parámetros modales sólo registrando su respuesta son de vital importancia para este tipo de estructuras. Esta técnica se conoce como Análisis Modal Operacional (OMA). La técnica del OMA no necesita excitar artificialmente la estructura, atendiendo únicamente a su comportamiento en servicio. La motivación para llevar a cabo pruebas de OMA surge en el campo de la Ingeniería Civil, debido a que excitar artificialmente con éxito grandes estructuras no sólo resulta difícil y costoso, sino que puede incluso dañarse la estructura. Su importancia reside en que el comportamiento global de una estructura está directamente relacionado con sus parámetros modales, y cualquier variación de rigidez, masa o condiciones de apoyo, aunque sean locales, quedan reflejadas en los parámetros modales. Por lo tanto, esta identificación puede integrarse en un sistema de vigilancia de la integridad estructural. La principal dificultad para el uso de los parámetros modales estimados mediante OMA son las incertidumbres asociadas a este proceso de estimación. Existen incertidumbres en el valor de los parámetros modales asociadas al proceso de cálculo (internos) y también asociadas a la influencia de los factores ambientales (externas), como es la temperatura. Este Trabajo Fin de Máster analiza estas dos fuentes de incertidumbre. Es decir, en primer lugar, para una estructura de laboratorio, se estudian y cuantifican las incertidumbres asociadas al programa de OMA utilizado. En segundo lugar, para una estructura en servicio (una pasarela de banda tesa), se estudian tanto el efecto del programa OMA como la influencia del factor ambiental en la estimación de los parámetros modales. Más concretamente, se ha propuesto un método para hacer un seguimiento de las frecuencias naturales de un mismo modo. Este método incluye un modelo de regresión lineal múltiple que permite eliminar la influencia de estos agentes externos. A structure vibrates according to some of its vibration modes, defined by their modal parameters (natural frequencies, damping ratios and modal shapes). Through the measurements of the vibration at key points of the structure, the modal parameters can be estimated. In civil engineering structures, it is difficult to excite structures in a controlled manner, thus, techniques involving output-only modal estimation are of vital importance for these structure. This techniques are known as Operational Modal Analysis (OMA). The OMA technique does not need to excite artificially the structure, this considers its behavior in service only. The motivation for carrying out OMA tests arises in the area of Civil Engineering, because successfully artificially excite large structures is difficult and expensive. It also may even damage the structure. The main goal is that the global behavior of a structure is directly related to their modal parameters, and any variation of stiffness, mass or support conditions, although it is local, is also reflected in the modal parameters. Therefore, this identification may be within a Structural Health Monitoring system. The main difficulty for using the modal parameters estimated by an OMA is the uncertainties associated to this estimation process. Thus, there are uncertainties in the value of the modal parameters associated to the computing process (internal) and the influence of environmental factors (external), such as the temperature. This Master’s Thesis analyzes these two sources of uncertainties. That is, firstly, for a lab structure, the uncertainties associated to the OMA program used are studied and quantified. Secondly, for an in-service structure (a stress-ribbon footbridge), both the effect of the OMA program and the influence of environmental factor on the modal parameters estimation are studied. More concretely, a method to track natural frequencies of the same mode has been proposed. This method includes a multiple linear regression model that allows to remove the influence of these external agents.

Computational Methods for Identification of Vibrating Structures

System identification deals with the problem of building mathematical models of dynamical systems based on observed data from the system" [1]. In the context of civil engineering, the system refers to a large scale structure such as a building, bridge, or an offshore structure, and identification mostly involves the determination of modal parameters (the natural frequencies, damping ratios, and mode shapes). This paper presents some modal identification results obtained using a state-of-the-art time domain system identification method (data-driven stochastic subspace algorithms [2]) applied to the output-only data measured in a steel arch bridge. First, a three dimensional finite element model was developed for the numerical analysis of the structure using ANSYS. Modal analysis was carried out and modal parameters were extracted in the frequency range of interest, 0-10 Hz. The results obtained from the finite element modal analysis were used to determine the location of the sensors. After that, ambient vibration tests were conducted during April 23-24, 2009. The response of the structure was measured using eight accelerometers. Two stations of three sensors were formed (triaxial stations). These sensors were held stationary for reference during the test. The two remaining sensors were placed at the different measurement points along the bridge deck, in which only vertical and transversal measurements were conducted (biaxial stations). Point estimate and interval estimate have been carried out in the state space model using these ambient vibration measurements. In the case of parametric models (like state space), the dynamic behaviour of a system is described using mathematical models. Then, mathematical relationships can be established between modal parameters and estimated point parameters (thus, it is common to use experimental modal analysis as a synonym for system identification). Stable modal parameters are found using a stabilization diagram. Furthermore, this paper proposes a method for assessing the precision of estimates of the parameters of state-space models (confidence interval). This approach employs the nonparametric bootstrap procedure [3] and is applied to subspace parameter estimation algorithm. Using bootstrap results, a plot similar to a stabilization diagram is developed. These graphics differentiate system modes from spurious noise modes for a given order system. Additionally, using the modal assurance criterion, the experimental modes obtained have been compared with those evaluated from a finite element analysis. A quite good agreement between numerical and experimental results is observed.

Análisis del acoplamiento vibro-acústico en resonadores de membrana

El presente Trabajo Fin de Máster pretende llevar a cabo el análisis del comportamiento vibratorio de resonadores de membrana, consistentes en un panel delgado y ligero montado a cierta distancia de un elemento constructivo rígido y pesado. Este tipo de sistemas resonantes son empleados habitualmente como absorbentes de media-baja frecuencia en aplicaciones de acondicionamiento acústico de salas. El análisis hará especial hincapié en la influencia del acoplamiento mecánico-acústico entre la placa vibrante (estructura) y el colchón de aire (fluido) encerrado entre la misma y la pared rígida. En primer lugar, realizaremos el análisis modal experimental del resonador objeto de ensayo a partir de las mediciones de su respuesta vibratoria, con el fin de caracterizar su comportamiento en base a sus primeros modos propios acoplados de flexión. El análisis de las señales vibratorias en el dominio de la frecuencia para la identificación de dicho modos se realizará en el entorno de programación MATLAB, haciendo uso de una herramienta propia que implementa los métodos de cálculo y los algoritmos necesarios para tal fin. Asimismo, simularemos el comportamiento del resonador mediante el método de elementos finitos (FEM), utilizando las aplicaciones ANSYS y SYSNOISE, considerando diferentes condiciones frontera en el modelo generado. Los resultados aquí obtenidos serán de utilidad para complementar aquellos obtenidos de forma experimental a la hora de extraer conclusiones prácticas del análisis realizado. SUMMARY. This Master's Thesis intends to carry out the analysis of the vibratory behaviour of resonance absorbers, consisting of a thin and lightweight panel mounted at a distance from a rigid wall. Such systems are commonly used as sound absorption systems for mid-low frequency in room acoustics applications. The analysis will emphasize the influence of mechanical-acoustic coupling between the vibrating plate (structure) and the air cushion (acoustic element) enclosed behind it. First of all, we are performing the experimental modal analysis of the resonance absorber under test from the vibrational response measurements, in order to characterize its behaviour based on its first bending coupled-modes. The analysis of vibration signals in the frequency domain for the identification of such modes will be made in MATLAB programming environment, using a proprietary tool that implements the calculation methods and algorithms needed for this purpose. Furthermore, we are simulating the behaviour of the resonance absorber applying the Finite Element Method (FEM) – using ANSYS and SYSNOISE applications - considering different boundary conditions in the model created. The results from the simulation will be useful to complement those obtained experimentally when drawing practical conclusions from this analysis.

Metodología de Ejecución y Explotación de los Ensayos en Vuelo de Flameo

El flameo o flutter es un fenómeno vibratorio debido a la interacción de fuerzas inerciales, elásticas y aerodinámicas. Consiste en un intercambio de energía, que se puede observar en el cambio de amortiguamientos, entre dos o más modos estructurales, denominados modos críticos, cuyas frecuencias tienden a acercarse (coalescencia de frecuencias). Los ensayos en vuelo de flameo suponen un gran riesgo debido a la posibilidad de una perdida brusca de estabilidad aeroelástica (flameo explosivo) con la posibilidad de destrucción de la aeronave. Además existen otros fenómenos asociados que pueden aparecer como el LCO (Limit Cycle Oscillation) y la interacción con los mandos de vuelo. Debido a esto, se deben llevar a cabo análisis exhaustivos, que incluyen GVT (vibraciones en tierra), antes de comenzar los ensayos en vuelo, y estos últimos deben ser ejecutados con robustos procedimientos. El objetivo de los ensayos es delimitar la frontera de estabilidad sin llegar a ella, manteniéndose siempre dentro de la envolvente estable de vuelo. Para lograrlo se necesitan métodos de predicción, siendo el “Flutter Margin”, el más utilizado. Para saber cuánta estabilidad aeroelástica tiene el avión y lo lejos que está de la frontera de estabilidad (a través de métodos de predicción) los parámetros modales, en particular la frecuencia y el amortiguamiento, son de vital importancia. El ensayo en vuelo consiste en la excitación de la estructura a diferentes condiciones de vuelo, la medición de la respuesta y su análisis para obtener los dos parámetros mencionados. Un gran esfuerzo se dedica al análisis en tiempo real de las señales como un medio de reducir el riesgo de este tipo de ensayos. Existen numerosos métodos de Análisis Modal, pero pocos capaces de analizar las señales procedentes de los ensayos de flameo, debido a sus especiales características. Un método novedoso, basado en la Descomposición por Valores Singulares (SVD) y la factorización QR, ha sido desarrollado y aplicado al análisis de señales procedentes de vuelos de flameo del F-18. El método es capaz de identificar frecuencia y amortiguamiento de los modos críticos. El algoritmo se basa en la capacidad del SVD para el análisis, modelización y predicción de series de datos con características periódicas y en su capacidad de identificar el rango de una matriz, así como en la aptitud del QR para seleccionar la mejor base vectorial entre un conjunto de vectores para representar el campo vectorial que forman. El análisis de señales de flameo simuladas y reales demuestra, bajo ciertas condiciones, la efectividad, robustez, resistencia al ruido y capacidad de automatización del método propuesto. ABSTRACT Flutter involves the interaction between inertial, elastic and aerodynamic forces. It consists on an exchange of energy, identified by change in damping, between two or more structural modes, named critical modes, whose frequencies tend to get closer to each other (frequency coalescence). Flight flutter testing involves high risk because of the possibility of an abrupt lost in aeroelastic stability (hard flutter) that may lead to aircraft destruction. Moreover associated phenomena may happen during the flight as LCO (Limit Cycle Oscillation) and coupling with flight controls. Because of that, intensive analyses, including GVT (Ground Vibration Test), have to be performed before beginning the flights test and during them consistent procedures have to be followed. The test objective is to identify the stability border, maintaining the aircraft always inside the stable domain. To achieve that flutter speed prediction methods have to be used, the most employed being the “Flutter Margin”. In order to know how much aeroelastic stability remains and how far the aircraft is from the stability border (using the prediction methods), modal parameters, in particular frequency and damping are paramount. So flight test consists in exciting the structure at various flight conditions, measuring the response and identifying in real-time these two parameters. A great deal of effort is being devoted to real-time flight data analysis as an effective way to reduce the risk. Numerous Modal Analysis algorithms are available, but very few are suitable to analyze signals coming from flutter testing due to their special features. A new method, based on Singular Value Decomposition (SVD) and QR factorization, has been developed and applied to the analysis of F-18 flutter flight-test data. The method is capable of identifying the frequency and damping of the critical aircraft modes. The algorithm relies on the capability of SVD for the analysis, modelling and prediction of data series with periodic features and also on its power to identify matrix rank as well as QR competence for selecting the best basis among a set of vectors in order to represent a given vector space of such a set. The analysis of simulated and real flutter flight test data demonstrates, under specific conditions, the effectiveness, robustness, noise-immunity and the capability for automation of the method proposed.