Effects of wireless sensor network uncertainties on output-only modal analysis employing merged data of multiple tests

The use of Wireless Sensor Networks (WSNs) for Structural Health Monitoring (SHM) has become a promising approach due to many advantages such as low cost, fast and flexible deployment. However, inherent technical issues such as data synchronization error and data loss have prevented these distinct systems from being extensively used. Recently, several SHM-oriented WSNs have been proposed and believed to be able to overcome a large number of technical uncertainties. Nevertheless, there is limited research verifying the applicability of those WSNs with respect to demanding SHM applications like modal analysis and damage identification. This paper first presents a brief review of the most inherent uncertainties of the SHM-oriented WSN platforms and then investigates their effects on outcomes and performance of the most robust Output-only Modal Analysis (OMA) techniques when employing merged data from multiple tests. The two OMA families selected for this investigation are Frequency Domain Decomposition (FDD) and Data-driven Stochastic Subspace Identification (SSI-data) due to the fact that they both have been widely applied in the past decade. Experimental accelerations collected by a wired sensory system on a large-scale laboratory bridge model are initially used as clean data before being contaminated by different data pollutants in sequential manner to simulate practical SHM-oriented WSN uncertainties. The results of this study show the robustness of FDD and the precautions needed for SSI-data family when dealing with SHM-WSN uncertainties. Finally, the use of the measurement channel projection for the time-domain OMA techniques and the preferred combination of the OMA techniques to cope with the SHM-WSN uncertainties is recommended.

Effects of wireless sensor network uncertainties on output-only modal analysis employing merged data of multiple tests

The use of Wireless Sensor Networks (WSNs) for vibration-based Structural Health Monitoring (SHM) has become a promising approach due to many advantages such as low cost, fast and flexible deployment. However, inherent technical issues such as data asynchronicity and data loss have prevented these distinct systems from being extensively used. Recently, several SHM-oriented WSNs have been proposed and believed to be able to overcome a large number of technical uncertainties. Nevertheless, there is limited research verifying the applicability of those WSNs with respect to demanding SHM applications like modal analysis and damage identification. Based on a brief review, this paper first reveals that Data Synchronization Error (DSE) is the most inherent factor amongst uncertainties of SHM-oriented WSNs. Effects of this factor are then investigated on outcomes and performance of the most robust Output-only Modal Analysis (OMA) techniques when merging data from multiple sensor setups. The two OMA families selected for this investigation are Frequency Domain Decomposition (FDD) and data-driven Stochastic Subspace Identification (SSI-data) due to the fact that they both have been widely applied in the past decade. Accelerations collected by a wired sensory system on a large-scale laboratory bridge model are initially used as benchmark data after being added with a certain level of noise to account for the higher presence of this factor in SHM-oriented WSNs. From this source, a large number of simulations have been made to generate multiple DSE-corrupted datasets to facilitate statistical analyses. The results of this study show the robustness of FDD and the precautions needed for SSI-data family when dealing with DSE at a relaxed level. Finally, the combination of preferred OMA techniques and the use of the channel projection for the time-domain OMA technique to cope with DSE are recommended.

Effects of wireless sensor network uncertainties on output-only modal-based damage identification

The use of Wireless Sensor Networks (WSNs) for Structural Health Monitoring (SHM) has become a promising approach due to many advantages such as low cost, fast and flexible deployment. However, inherent technical issues such as data synchronization error and data loss have prevented these distinct systems from being extensively used. Recently, several SHM-oriented WSNs have been proposed and believed to be able to overcome a large number of technical uncertainties. Nevertheless, there is limited research examining effects of uncertainties of generic WSN platform and verifying the capability of SHM-oriented WSNs, particularly on demanding SHM applications like modal analysis and damage identification of real civil structures. This article first reviews the major technical uncertainties of both generic and SHM-oriented WSN platforms and efforts of SHM research community to cope with them. Then, effects of the most inherent WSN uncertainty on the first level of a common Output-only Modal-based Damage Identification (OMDI) approach are intensively investigated. Experimental accelerations collected by a wired sensory system on a benchmark civil structure are initially used as clean data before being contaminated with different levels of data pollutants to simulate practical uncertainties in both WSN platforms. Statistical analyses are comprehensively employed in order to uncover the distribution pattern of the uncertainty influence on the OMDI approach. The result of this research shows that uncertainties of generic WSNs can cause serious impact for level 1 OMDI methods utilizing mode shapes. It also proves that SHM-WSN can substantially lessen the impact and obtain truly structural information without having used costly computation solutions.

Output only based modal analysis of a reduced scale building using digital camera images

The article discusses a proposal of displacement measurement using a unique digital camera aiming at to exploit its feasibility for Modal Analysis applications. The proposal discusses a non-contact measuring approach able to measure multiple points simultaneously by using a unique digital camera. A modal analysis of a reduced scale lab building structure based only at the responses of the structure measured with the camera is presented. It focuses at the feasibility of using a simple ordinary camera for performing the output only modal analysis of structures and its advantage. The modal parameters of the structure are estimated from the camera data and also by using ordinary experimental modal analysis based on the Frequency Response Function (FRF) obtained by using the usual sensors like accelerometer and force cell. The comparison of the both analysis showed that the technique is promising noncontact measuring tool relatively simple and effective to be used in structural modal analysis

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.

Innovative damage assessment of steel truss bridges using modal strain energy correlation

As a part of vital infrastructure and transportation network, bridge structures must function safely at all times. Bridges are designed to have a long life span. At any point in time, however, some bridges are aged. The ageing of bridge structures, given the rapidly growing demand of heavy and fast inter-city passages and continuous increase of freight transportation, would require diligence on bridge owners to ensure that the infrastructure is healthy at reasonable cost. In recent decades, a new technique, structural health monitoring (SHM), has emerged to meet this challenge. In this new engineering discipline, structural modal identification and damage detection have formed a vital component. Witnessed by an increasing number of publications is that the change in vibration characteristics is widely and deeply investigated to assess structural damage. Although a number of publications have addressed the feasibility of various methods through experimental verifications, few of them have focused on steel truss bridges. Finding a feasible vibration-based damage indicator for steel truss bridges and solving the difficulties in practical modal identification to support damage detection motivated this research project. This research was to derive an innovative method to assess structural damage in steel truss bridges. First, it proposed a new damage indicator that relies on optimising the correlation between theoretical and measured modal strain energy. The optimisation is powered by a newly proposed multilayer genetic algorithm. In addition, a selection criterion for damage-sensitive modes has been studied to achieve more efficient and accurate damage detection results. Second, in order to support the proposed damage indicator, the research studied the applications of two state-of-the-art modal identification techniques by considering some practical difficulties: the limited instrumentation, the influence of environmental noise, the difficulties in finite element model updating, and the data selection problem in the output-only modal identification methods. The numerical (by a planer truss model) and experimental (by a laboratory through truss bridge) verifications have proved the effectiveness and feasibility of the proposed damage detection scheme. The modal strain energy-based indicator was found to be sensitive to the damage in steel truss bridges with incomplete measurement. It has shown the damage indicator's potential in practical applications of steel truss bridges. Lastly, the achievement and limitation of this study, and lessons learnt from the modal analysis have been summarised.

Análise modal baseada apenas na resposta: decomposição no domínio da frequência

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

SHM through flexible vibration sensing technologies and robust safety evaluation paradigm

This research has successfully developed a novel synthetic structural health monitoring system model that is cost-effective and flexible in sensing and data acquisition; and robust in the structural safety evaluation aspect for the purpose of long-term and frequent monitoring of large-scale civil infrastructure during their service lives. Not only did it establish a real-world structural monitoring test-bed right at the heart of QUT Gardens Point Campus but it can also facilitate reliable and prompt protection for any built infrastructure system as well as the user community involved.

Überwachung der Vorspannkraft Externer Spannglieder mit Hilfe der Modalanalyse

Aufgrund ihrer Vorteile hinsichtlich Dauerhaftigkeit und Bauwerkssicherheit ist in Deutschland seit 1998 die externe Vorspannung in Hohlkastenbrücken zur Regelbauweise geworden. Durch Verwendung der austauschbaren externen Vorspannung verspricht man sich im Brückenbau weitere Verbesserungen der Robustheit und damit eine Verlängerung der Lebensdauer. Trotz des besseren Korrosionsschutzes im Vergleich zur internen Vorspannung mit Verbund sind Schäden nicht völlig auszuschließen. Um die Vorteile der externen Vorspannung zu nutzen, ist daher eine periodische Überwachung der Spanngliedkräfte, z. B. während der Hauptprüfung des Bauwerks, durchzuführen. Für die Überwachung der Spanngliedkräfte bei Schrägseilbrücken haben sich die Schwingungsmessmethoden als wirtschaftlich und leistungsfähig erwiesen. Für die Übertragung der Methode auf den Fall der externen Vorspannung, wo kürzere Schwingungslängen vorliegen, waren zusätzliche Untersuchungen hinsichtlich der effektiven Schwingungslänge, der Randbedingungen sowie der effektiven Biegesteifigkeit erforderlich. Im Rahmen der vorliegenden Arbeit wurde das Modellkorrekturverfahren, basierend auf der iterativen Anpassung eines F.E.-Modells an die identifizierten Eigenfrequenzen und Eigenformen des Spanngliedes, für die Bestimmung der Spanngliedkräfte verwendet. Dieses Verfahren ermöglicht die Berücksichtigung der Parameter (Schwingungslänge, Randbedingungen und effektive Biegesteifigkeit) bei der Identifikation der effektiven Spanngliedkräfte. Weiterhin ist eine Modellierung jeder beliebigen Spanngliedausbildung, z. B. bei unterschiedlichen Querschnitten in den Verankerungs- bzw. Umlenkbereichen, gewährleistet. Zur Anwendung bei der Ermittlung der Spanngliedkräfte wurde eine spezielle Methode, basierend auf den besonderen dynamischen Eigenschaften der Spannglieder, entwickelt, bei der die zuvor genannten Parameter innerhalb jedes Iterationsschrittes unabhängig korrigiert werden, was zur Robustheit des Identifikationsverfahrens beiträgt. Das entwickelte Verfahren ist in einem benutzerfreundlichen Programmsystem implementiert worden. Die erzielten Ergebnisse wurden mit dem allgemeinen Identifikationsprogramm UPDATE_g2 verglichen; dabei ist eine sehr gute Übereinstimmung festgestellt worden. Beim selbst entwickelten Verfahren wird die benötigte Rechenzeit auf ca. 30 % reduziert [100 sec à 30 sec]. Es bietet sich daher für die unmittelbare Auswertung vor Ort an. Die Parameteridentifikationsverfahren wurden an den Spanngliedern von insgesamt sechs Brücken (vier unterschiedliche Spannverfahren) angewendet. Die Anzahl der getesteten Spannglieder beträgt insgesamt 340. Die Abweichung zwischen den durch Schwingungs-messungen identifizierten und gemessenen (bei einer Brücke durch eine Abhebekontrolle) bzw. aufgebrachten Spanngliedkräften war kleiner als 3 %. Ferner wurden die Auswirkungen äußerer Einflüsse infolge Temperaturschwankungen und Verkehr bei den durchgeführten Messungen untersucht. Bei der praktischen Anwendung sind Besonderheiten aufgetreten, die durch die Verwendung des Modellkorrekturverfahrens weitgehend erfasst werden konnten. Zusammenfassend lässt sich sagen, dass die Verwendung dieses Verfahrens die Genauigkeit im Vergleich mit den bisherigen Schwingungsmessmethoden beachtlich erhöht. Ferner wird eine Erweiterung des Anwendungsbereiches auch auf Spezialfälle (z. B. bei einem unplanmäßigen Anliegen) gewährleistet.

Identificação dos parâmetros modais utilizando apenas as respostas da estrutura: identificação no domínio do tempo

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Programa computacional com interface gráfica para identificação estocástica de parâmetros modais de estruturas civis: aplicação em pontes e torres de linha de transmissão

O interesse no comportamento dinâmico de estruturas metálicas vem crescendo nas últimas décadas no Brasil, em decorrência de acidentes com colapso total de algumas estruturas devido às vibrações ambientes em diversas regiões do país. Na região amazônica, por exemplo, onde esse tipo de estrutura deve vencer obstáculos como florestas e rios de grande largura, casos de colapso total de estruturas metálicas também são relatados. O foco principal dessa dissertação é o estudo do comportamento modal de estruturas metálicas submetidas às vibrações ambientes cuja magnitude das forças de excitação é desconhecida. Dois estudos de caso são apresentados: no primeiro deles, o comportamento modal de uma torre de linha de transmissão de energia elétrica é investigado; e no segundo caso, tanto o comportamento modal como os níveis de desconforto de uma ponte são estudados. Os estudos realizados neste último caso visam avaliar os níveis de desconforto da ponte quando submetida às excitações ambientes como rajadas de vento e o tráfego de veículo de acordo a norma brasileira NBR 8800 (1986). Em ambos os estudos de caso foram realizadas análises experimentais e computacionais. Na etapa experimental, ambas as estruturas foram monitoradas com emprego de um conjunto de acelerômetros de baixa freqüência e também de um sistema de aquisição apropriados para ensaios de vibração de estruturas civis. Como é muito difícil medir a magnitude das forças de excitação ambientes, foram utilizados os métodos de identificação estocásticos SSI-DATA e SSI-COV para extração de parâmetros modais de estruturas civis a partir somente dos dados de resposta coletados nos ensaios de vibração. Entre as atividades desenvolvidas nessa etapa, destaca-se a criação de um programa computacional com recursos do Graphical User Interface (GUI) da plataforma Matlab®, destinado à identificação modal de estruturas civis com o emprego dos referidos métodos estocásticos. Esse programa é constituído de três módulos: o primeiro é destinado ao processamento e tratamento dos sinais coletados nos ensaios de vibração; o segundo é utilizado para adicionar as informações do posicionamento dos acelerômetros utilizados nos arquivos dos sinais de resposta; e o terceiro e último módulo é destinado à identificação a partir dos arquivos de dados de resposta processados nos dois primeiros módulos. Na etapa das análises teóricas, foram criados modelos numéricos utilizando o método dos elementos finitos para simular o comportamento dinâmico das estruturas analisadas. Comparando os resultados obtidos em ambas as etapas de análise, verifica-se que resultados experimentais e teóricos apresentaram parâmetros bastante próximos entre si nos primeiros modos de vibração. Os resultados experimentais mostraram que ambos os métodos estocásticos foram muito eficientes na identificação das estruturas ensaiadas.

Medidas de deslocamento e vibrações através de imagens de câmeras digitais

Pós-graduação em Engenharia Mecânica - FEIS

Comparison of output-only methods for modal identification of a twelve-span viaduct

Peer reviewed

A statistical strategy to select optimal structural health monitoring data in operational modal analysis

Operational modal analysis (OMA) is prevalent in modal identifi cation of civil structures. It asks for response measurements of the underlying structure under ambient loads. A valid OMA method requires the excitation be white noise in time and space. Although there are numerous applications of OMA in the literature, few have investigated the statistical distribution of a measurement and the infl uence of such randomness to modal identifi cation. This research has attempted modifi ed kurtosis to evaluate the statistical distribution of raw measurement data. In addition, a windowing strategy employing this index has been proposed to select quality datasets. In order to demonstrate how the data selection strategy works, the ambient vibration measurements of a laboratory bridge model and a real cable-stayed bridge have been respectively considered. The analysis incorporated with frequency domain decomposition (FDD) as the target OMA approach for modal identifi cation. The modal identifi cation results using the data segments with different randomness have been compared. The discrepancy in FDD spectra of the results indicates that, in order to fulfi l the assumption of an OMA method, special care shall be taken in processing a long vibration measurement data. The proposed data selection strategy is easy-to-apply and verifi ed effective in modal analysis.

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.