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This paper proposes a novel approach to deal with modeling uncertainty when updating mechanics-based nonlinear finite element (FE) models. In this framework, a dual adaptive filtering approach is adopted, where the Unscented Kalman filter (UKF) is used to estimate the unknown parameters of the nonlinear FE model and a linear Kalman filter (KF) is employed to estimate the diagonal terms of the covariance matrix of the simulation error vector based on a covariance-matching technique. Numerically simulated response data of a two-dimensional three-story three-bay steel frame structure with eight unknown material model parameters subjected to unidirectional horizontal seismic excitation is used to illustrate and validate the proposed methodology. Geometry, inertia properties, gravity loads, and damping properties are considered as sources of modeling uncertainty and different levels and combinations of them are analyzed. The results of the validation studies show that the proposed approach significantly outperforms the parameter-only estimation approach widely investigated and used in the literature. Thus, a more robust and comprehensive identification of structural damage is achieved when using the proposed approach. A different input motion is then considered to verify the prediction capabilities of the proposed methodology by using the FE model updated by the parameter estimation results obtained. [ABSTRACT FROM AUTHOR]

The subject of structural health monitoring is drawing more and more attention over the last years. Many vibration-based techniques aiming at detecting small structural changes or even damage have been developed or enhanced through successive researches. Lately, several studies have focused on the use of raw dynamic data to assess information about structural condition. Despite this trend and much skepticism, many methods still rely on the use of modal parameters as fundamental data for damage detection. Therefore, it is of utmost importance that modal identification procedures are performed with a sufficient level of precision and automation. To fulfill these requirements, this paper presents a novel automated time-domain methodology to identify modal parameters based on a two-step clustering analysis. The first step consists in clustering modes estimates from parametric models of different orders, usually presented in stabilization diagrams. In an automated manner, the first clustering analysis indicates which estimates correspond to physical modes. To circumvent the detection of spurious modes or the loss of physical ones, a second clustering step is then performed. The second step consists in the data mining of information gathered from the first step. To attest the robustness and efficiency of the proposed methodology, numerically generated signals as well as experimental data obtained from a simply supported beam tested in laboratory and from a railway bridge are utilized. The results appeared to be more robust and accurate comparing to those obtained from methods based on one-step clustering analysis. [ABSTRACT FROM AUTHOR]

Technical systems and system’s components undergo gradual degradation over time. Continuous degradation occurred in system is reflected in decreased system’s reliability and unavoidably lead to a system failure. Therefore, continuous evaluation of State-of-Health (SoH) is inevitable to provide at least predefined lifetime of the system defined by manufacturer, or even better, to extend the lifetime given by manufacturer. However, precondition for lifetime extension is accurate estimation of SoH as well as the estimation and prediction of Remaining Useful Lifetime (RUL). For this purpose, lifetime models describing the relation between system/component degradation and consumed lifetime have to be established. In this contribution modeling and selection of suitable lifetime models from database based on current SoH conditions are discussed. Main contribution of this paper is the development of new modeling strategies capable to describe complex relations between measurable system variables, related system degradation, and RUL. Two approaches with accompanying advantages and disadvantages are introduced and compared. Both approaches are capable to model stochastic aging processes of a system by simultaneous adaption of RUL models to current SoH. The first approach requires a priori knowledge about aging processes in the system and accurate estimation of SoH. An estimation of SoH here is conditioned by tracking actual accumulated damage into the system, so that particular model parameters are defined according to a priori known assumptions about system’s aging. Prediction accuracy in this case is highly dependent on accurate estimation of SoH but includes high number of degrees of freedom. The second approach in this contribution does not require a priori knowledge about system’s aging as particular model parameters are defined in accordance to multi-objective optimization procedure. Prediction accuracy of this model does not highly depend on estimated SoH. This model has lower degrees of freedom. Both approaches rely on previously developed lifetime models each of them corresponding to predefined SoH. Concerning first approach, model selection is aided by state-machine-based algorithm. In the second approach, model selection conditioned by tracking an exceedance of predefined thresholds is concerned. The approach is applied to data generated from tribological systems. By calculating Root Squared Error ( RSE ), Mean Squared Error ( MSE ), and Absolute Error ( ABE ) the accuracy of proposed models/approaches is discussed along with related advantages and disadvantages. Verification of the approach is done using cross-fold validation, exchanging training and test data. It can be stated that the newly introduced approach based on data (denoted as data-based or data-driven) parametric models can be easily established providing detailed information about remaining useful/consumed lifetime valid for systems with constant load but stochastically occurred damage. [ABSTRACT FROM AUTHOR]

Abstract: Frequency response function (FRF) estimation is widely used for system identification and related applications, such as model updating and structural health monitoring. Whether estimated from data or from a finite element (FE) model, there is uncertainty involved that introduces variability in the FRF evaluation, which leads to misinterpretation of system identification results or false alarms (Type-I error) if used in hypothesis testing for damage detection. This paper aims to quantify the uncertainty of FRF estimated with randomly-excited experimental data using the H1 estimator, for both its magnitude and phase. A Gaussian bivariate statistical model is implemented, and probability density functions (PDF) of the magnitude and phase estimations are derived. The proposed statistical model is validated for both an ideal simulation test-bed and an experimental lab-scale structure. To further validate the model's robustness, artificial noise is added to contaminate the original data. The predicted PDFs show a very good match with histogram observations, where outlier percentage is selected as the comparison metric in this paper. For different significance levels, the outlier percentages all consistently agree with the pre-described uncertainty thresholds, indicating that the proposed statistical model suitably quantifies the uncertainty of FRF estimations using H1. [Copyright &y& Elsevier]

Vibrating structures may be subject to changes throughout their operating lifetime due to a range of environmental and technical factors. These variations can be considered as parameter changes in the dynamic model of the structure, while their online estimates can be utilized in adaptive control strategies, or in structural health monitoring. This paper implements the moving horizon estimation (MHE) algorithm on a low-cost embedded computing device that is jointly observing the dynamic states and parameter variations of an active cantilever beam in real time. The practical behavior of this algorithm has been investigated in various experimental scenarios. It has been found, that for the given field of application, moving horizon estimation converges faster than the extended Kalman filter; moreover, it handles atypical measurement noise, sensor errors or other extreme changes, reliably. Despite its improved performance, the experiments demonstrate that the disadvantage of solving the nonlinear optimization problem in MHE is that it naturally leads to an increase in computational effort. [ABSTRACT FROM AUTHOR]

Condition monitoring aims at ensuring system safety which is a fundamental requirement for industrial applications and that has become an inescapable social demand. This objective is attained by instrumenting the system and developing data analytics methods such as statistical models able to turn data into relevant knowledge. One difficulty is to be able to correctly estimate the parameters of those methods based on time-series data. This paper suggests the use of the Weighted Distribution Theory together with the Expectation–Maximization algorithm to improve parameter estimation in statistical models with latent variables with an application to health monotonic under uncertainty. The improvement of estimates is made possible by incorporating uncertain and possibly noisy prior knowledge on latent variables in a sound manner. The latent variables are exploited to build a degradation model of dynamical system represented as a sequence of discrete states. Examples on Gaussian Mixture Models, Hidden Markov Models (HMM) with discrete and continuous outputs are presented on both simulated data and benchmarks using the turbofan engine datasets. A focus on the application of a discrete HMM to health monitoring under uncertainty allows to emphasize the interest of the proposed approach in presence of different operating conditions and fault modes. It is shown that the proposed model depicts high robustness in presence of noisy and uncertain prior. [ABSTRACT FROM AUTHOR]

• A robust fuzzy-driven approach is derived for fully IO/OO automated modal analysis. • The methodology is applicable using any growing model-order identification method. • A new indicator called nMTN is derived for emphasizing weakly excited modes. • The efficiency of the strategy is demonstrated on actual shaking-table tests results. A crucial step when identifying the modal signature of systems using growing order parametric methods consists in discriminating spurious modes from physical modes. In this paper, a three-stages clustering strategy is presented in a fuzzy framework for automating this selection process in the context of Input/Output and Output-Only identification. The novelty and strong point of the approach lies in the first stage where, after computation of single mode validation indicators, a modified fuzzy c-means clustering procedure is developed for performing a first partition. It is shown how the membership function obtained for the cluster of physical modes can be interpreted as a new synthetic modal indicator and helps with pole-splitting detection, outlier rejection and generally improves the final modal parameters estimation. The developed methodology does not involve any user-specified threshold and can be used for discriminating modes produced by any methodology consisting in fitting a growing order model to experimental data of any type. In this paper, accelerations measured during the SMART2013 shaking-table test campaign are processed using data-driven state-space identification algorithms. The automated selection process is used for tracking the modal signature of a trapezoidal shaped reinforced-concrete specimen using in turn stochastic and combined deterministic-stochastic algorithms, defining for the latter the movement of the shaking table as input. Variations in the modal signature are then correlated to the damage actually observed on the specimen and a comparison between Output-Only and Input/Output results is made in order to estimate the interaction between the specimen and the whole shaking table device. [ABSTRACT FROM AUTHOR]

In recent years, a new research direction in structural condition assessment has been focusing on developing automated or semi-automated procedures to identify a structure's modal parameters from its response measurements. This is because long-term structural monitoring systems rely on the implementation of system identification methodologies that often involve the intervention of an expert user with an acquired experience in the field. This paper aims to offer a semi-automated methodology for extracting the modal parameters independently of the chosen parametric system identification technique with minimum user involvement in the parameter selection process. Here, the framework is applied to two different parametric system identification algorithms: Data-Driven Stochastic Subspace Identification (DD-SSI) and Output Only Observer Kalman Filter (O/O OKID). The procedure can be represented as a multi-stage strategy where unsupervised tools and three clustering options are offered to the user to reach a reliable estimate of the modal parameters. The proposed procedure is validated with an application in the operational modal analysis of an existing hospital structure located in Italy. The results demonstrated excellent accuracy and robust performance of the methodology, even in the presence of closely spaced modes. The proposed procedure helps to improve the data analysis process in continuous monitoring, where usually, the algorithm's parameters need to be constantly updated by the user. • Semi-automated operational modal analysis with low user interaction • Methodology independent from the parametric system identification algorithm adopted • Three clustering techniques available to the user: two k-means approaches and DBSCAN • Application on the analysis of vibration data recorded on a RC hospital structure • Validation using two system identification algorithms: DD-SSI and O/O OKID [ABSTRACT FROM AUTHOR]

Abstract: Modal strain energy has been widely utilized as a damage-sensitive feature to detect structural damage. Based on the closed-form of element modal strain energy sensitivity, a statistic structural damage detection algorithm is proposed in this paper. The method is oriented to the ambient vibration measurements where only the operational mode shapes are available. The established element damage equations are incorporated with the modal scaling factors so that the damage and modal scaling factor can be solved simultaneously. The statistic analysis is implemented to take into account the uncertainties existing in the structural model and measured structural modal parameters. The sensitivity formulas of system matrix and residual vector with respect to noise are derived. The proposed statistics based element damage equations have a compact algebraic form, which does not require a large computation time and a high storage capacity. The method is verified by a numerical simply supported beam with different assumed damage. The effects of noise and damage levels on damage detection are investigated. It is demonstrated that the proposed method can identify most likely damaged elements. [Copyright &y& Elsevier]

Abstract: In order to evaluate the usefulness of approaches based on modal parameters tracking for structural health monitoring of bridges, in September of 2007, a dynamic monitoring system was installed in a concrete arch bridge at the city of Porto, in Portugal. The implementation of algorithms to perform the continuous on-line identification of modal parameters based on structural responses to ambient excitation (automated Operational Modal Analysis) has permitted to create a very complete database with the time evolution of the bridge modal characteristics during more than 2 years. This paper describes the strategy that was followed to minimize the effects of environmental and operational factors on the bridge natural frequencies, enabling, in a subsequent stage, the identification of structural anomalies. Alternative static and dynamic regression models are tested and complemented by a Principal Components Analysis. Afterwards, the identification of damages is tried with control charts. At the end, it is demonstrated that the adopted processing methodology permits the detection of realistic damage scenarios, associated with frequency shifts around 0.2%, which were simulated with a numerical model. [Copyright &y& Elsevier]

Abstract: In this paper a new approach is proposed for identifying the presence and location of a crack in a simply supported plate undergoing free vibration. Specifically, the approach uses a Markov-chain Monte-Carlo implementation of Bayes’ Rule to estimate the crack parameters (i.e., its location, orientation, and size) and their probability distributions. Special attention is paid to developing a fast and accurate forward model for the response of the cracked plate. To generate the required time series, a semi-analytical free response is calculated using an FEM based eigen-solution. To speed up the simulations, modified elements are used at the crack tips; this permits a more course mesh without sacrificing accuracy. The approach is demonstrated to be effective at identifying all of the crack parameters. Furthermore, a natural by-product of this method is that it also provides a confidence (credible) interval for each of these parameters. The results show the utility and accuracy of this method in identifying cracks of various sizes, orientations, and locations. [Copyright &y& Elsevier]

Abstract: Modal-based damage detection algorithms are well-known techniques for structural health assessment, but they are not commonly used due to the lack of automated modal identification and tracking procedures. Development of such procedures is not a trivial task since traditional modal identification requires extensive interaction from an expert user. Nevertheless, computational efforts have to be carefully considered. If fast on-line data processing is crucial for quickly varying in time systems (such as a rocket burning fuel), a number of vibration-based condition monitoring applications are performed at very different time scales, resulting in satisfactory time steps for on-line data analysis. Moreover, promising results in the field of automated modal identification have been recently achieved. In the present paper, a literature review on this topic is presented and recent developments concerning fully automated output-only modal identification procedures are described. Some case studies are also reported in order to validate the approach. They are characterized by different levels of complexity, in terms of mode coupling, dynamic interaction effects and level of vibration. Advantages and drawbacks of the proposed approach will be pointed out with reference to available experimental results. The final objective is the implementation of a fully automated system for vibration-based structural health monitoring of civil engineering structures and identification of adequate requirements about sensor number and layout, record duration and hardware characteristics able to ensure a reliable low-cost health assessment of constructions. Results of application of the proposed methodology to modal parameter estimation in operational conditions and during ground motions induced by the recent L’Aquila earthquake will be finally presented and discussed. [Copyright &y& Elsevier]

• Analysis of Lamb wave propagation due to very low energy impacts on composite plates. • Rayleigh damping parameters estimated from lamb wave characteristics. • Finite element model simulation of the impacts show a good agreement with the test results. Structural Health Monitoring (SHM) systems for aerospace applications are becoming more prevalent and its potential cost-benefit relation is rapidly improving as more structures that were traditionally made with metallic materials are gradually being replaced by composites. To support these systems, simulations can play a crucial role; however, in the case of low energy impacts, the structural damping becomes a very significant element in the simulation. This damping is usually not well known, or is based in estimates with very low reliability. Therefore, in order to improve the reliability of these values this paper presents an attempt to estimate the Rayleigh damping parameters with the application of time-frequency analysis methods to transient signals, specifically, the reduced interference distribution. These parameters are estimated based in the results of a large number of structural tests done in a square carbon-fiber reinforced plastic panel, and are then validated by correlation of an explicit Finite Element Method (FEM) simulation. A discussion on how to apply the reduced interference distribution to the signals measured by piezoelectric sensors under an impact scenario and other possible usages of these results is also presented in this document. [ABSTRACT FROM AUTHOR]