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• An AEKF-UM approach is proposed for identifying time-variant parameters with unknown mass distribution. • A real-time updating procedure is presented for improving the process and measurement noise covariance matrices. • An index is defined basing on dramatic increase of measurement noise covariance for damage instant detection. • The covariance resetting technique is used to enhance the tracking capability for effectively capturing the time-varying parameters. Although the extended Kalman filter (EKF) provides a promising way for structural state estimation, it cannot effectively track time-varying parameters online. Besides, the structural mass distribution is usually assumed to be known in advance for many EKF-based methods, limiting their applications. In this paper, by using limited observations, an adaptive EKF with unknown mass coefficients (AEKF-UM) approach is proposed for the identification of time-variant parameters and mass distribution at the same time. A real-time updating procedure is presented for improving the process and measurement noise covariance matrices at each time step to assure the stability and accuracy of convergence results. Based on the dramatic increase of measurement noise covariance, an index is defined for determining the damage instant. A covariance resetting technique is then used to enhance the tracking capability for the purpose of effectively capturing the time-varying parameters. The unknown mass coefficients can be estimated at the same time by adding them into the extended state vector. To validate the effectiveness of the proposed approach, two numerical cases are considered, i.e. (i) the Phase I ASCE structural health monitoring benchmark building structure, and (ii) a four-story nonlinear structure equipped with a magneto-rheological (MR) damper. Experimental tests on a four-story building model subject to base excitation are also conducted to investigate the performance of the proposed approach. Results show that the proposed approach is capable of satisfactorily tracking abrupt changes of stiffness parameters with unknown mass distributions. [ABSTRACT FROM AUTHOR]

• A particle filter-based prognostic method combining multiple features is shown. • An online feature selection criterion is proposed based on bias estimation. • The method is tested by fatigue testing of an Aluminum lug joint with Lamb wave monitoring. • The robustness of the proposed method is tested on different specimens. Damage prognosis generally resorts to damage quantification functions and evolution models to quantify the current damage state and to predict the future states and the remaining useful life (RUL). The former typically consists of a function describing the relationship between the damage state and a statistical feature extracted from the measured signals, thus the prognostic performance will strongly depend on the selection of a proper feature. Given the best feature may vary for different specimens or even at each time instant for the same specimen during damage progression, such selection is a challenging task but has received little investigation so far. In this context, this paper proposes a particle filter-based damage prognosis framework, which involves an online feature fusion and selection scheme. A prognostic model is considered for each feature, with a multivariate process equation, formulated using both a damage degradation function and a bias parameter, and a measurement equation linking the damage state and that feature considering a data-driven model and the bias. One PF is used to estimate the damage state, its evolution parameters, and the bias for each model. Then, at each step, the feature with the smallest estimated bias is selected as the best feature providing the most likely state vectors and is used to select the most likely samples of the damage state and growth parameters for predicting the RUL and for calculating the prior at the next step. The proposed prognostic framework is demonstrated by an experimental study, where an aluminum lug structure subject to fatigue crack growth is monitored by a Lamb wave measurement system. [ABSTRACT FROM AUTHOR]

Virtual sensing techniques have gained traction in applications to the structural health monitoring of monopile-based offshore wind turbines, as the strain response below the mudline, which is a primary indicator of fatigue damage accumulation, is impractical to measure directly with physical instrumentation. The Gaussian process latent force model (GPLFM) is a generalized Bayesian virtual sensing technique which combines a physics-driven model of the structure with a data-driven model of latent variables of the system to extrapolate unmeasured strain states. In the GPLFM, unknown sources of excitation are modeled as a Gaussian process (GP) and endowed with a structured covariance relationship with response states, using properties of the GP covariance kernel as well as correlation information supplied by the mechanical model. It is shown that posterior inference of the latent inputs and states is performed by Gaussian process regression of measured accelerations, computed efficiently using Kalman filtering and Rauch–Tung–Striebel smoothing in an augmented state-space model. While the GPLFM has been previously demonstrated in numerical studies to improve upon other virtual sensing techniques in terms of accuracy, robustness, and numerical stability, this work provides one of the first cases of in-situ validation of the GPLFM. The predicted strain response by the GPLFM is compared to subsoil strain data collected from an operating offshore wind turbine in the Westermeerwind Park in the Netherlands. A number of test cases are conducted, where the performance of the GPLFM is evaluated for its sensitivity to varying operational and environmental conditions, to the instrumentation scheme of the turbine, and to the fidelity of the mechanical model. In particular, this paper discusses the capacity of the GPLFM to achieve relatively robust strain predictions under high model uncertainty in the soil-foundation system of the offshore wind turbine by attributing sources of model error to the estimated stochastic input. [ABSTRACT FROM AUTHOR]

This paper presents a recursive Bayesian inference framework for digital twinning of an offshore wind turbine (OWT) in the Block Island Wind Farm (BIWF), the first commercial offshore wind farm in the U.S. with jacket substructure. The framework employs a window-based extended Kalman filter (EKF) to jointly estimate the dynamic input loads and unknown model parameters of the finite element (FE) model of the turbine using sparse output-only measurements. The estimation of input loads on the OWT is critical for fatigue life estimation, foundation monitoring and control strategy. In the proposed joint estimation algorithm, unknown input time histories are divided into overlapping windows and the current window of inputs is concatenated with uncertain model parameters to form an augmented state vector. To account for the uncertainty about wind direction and misalignment between wind direction and the turbine yaw angle, two independent horizontal wind load time histories in X and Y directions are estimated at the nacelle level. A numerical validation study is first performed in which wind loads of the OWT are simulated using the multi-physics platform FAST, and structural vibration responses are generated using the FE model in OpenSees. The window-based EKF is implemented using the simulated measurements, and the estimations of structural stiffness as the updating model parameter and input loads are observed to have high accuracy. The proposed algorithm is then validated using in-situ measurements on one of the OWT in BIWF. To investigate the effectiveness and robustness of the method under different environmental and operational conditions, four cases are considered in which the turbine is operating at the rated rotor speed (region 3 of power generation), below the rated rotor speed (region 2), idling rotor state (region 1 or no power), and region 4 beyond cut-out wind speed. Accurate predictions of acceleration and strain data are obtained using the proposed algorithm, and the estimations of model parameter are consistent and close to their nominal value under different conditions. These results from numerical and experimental studies validate the proposed window-based EKF for joint parameter-input estimation and digital twinning for long-term structural health monitoring of the OWT. [ABSTRACT FROM AUTHOR]

High-frequency global navigation satellite system (GNSS) is one of the most effective means for structural health monitoring. At present, most of the GNSS-based structural health monitoring research focuses on the positioning method, however, the unmodeled error caused by the complex monitoring environment and the receiver are always ignored by such technology. Most of the displacement extraction algorithms based on GNSS kinematic positioning are based on the Kalman filter and its variants, and the research on the effective determination of the process noise matrix and the measurement noise matrix is still limited. This paper aims to explore a displacement extraction method with automatic parameter tuning for structural health monitoring. An auto-tuning Kalman smoother has been introduced for extracting vibration displacement from GNSS kinematic positioning. Specifically, we divide the GNSS kinematic positioning results into a training set and a validation set, then use non-convex optimization to obtain the hyperparameters of the Kalman smoother. Finally, we extract the displacement from Kalman smoother with the tuned hyperparameters. Simulation and field experiments were carried out to verify the proposed method. The results show the feasibility and effectiveness of the method for vibration displacement extraction. For real-time kinematic positioning, the displacement extraction accuracy is improved by 9.58% compared to the traditional Kalman smoother; for precise point positioning, the displacement extraction accuracy is improved by 15.36%. The influence of the proportion of training set on the proposed method is discussed, and suggestion for the training set proportion is given. [ABSTRACT FROM AUTHOR]

• Curvature is identified from dynamic and quasi-static acceleration response. • Filtering and empirical mode decomposition provide curvature influence lines. • Sparse modal parameters and dense influence lines are fused using Kalman filter. • The system state represents dense and robust curvature estimate. • Monitoring the state evolution allows high-resolution damage localization. Curvature is one of the most popular damage-sensitive features in vibration-based structural health monitoring applications, typically calculated from identified modal features. While the relevant strategic or historical importance of bridges may justify dense sensor networks, a limited budget is generally assigned to monitor "minor" viaducts, thus involving inexpensive devices or extremely sparse sensing solutions. Modal parameters can only be obtained at instrumented locations. Thereby, damage assessment methods based on identified features typically have a low spatial resolution, especially when using low-cost monitoring setups with a modest number of sensing devices. This paper proposes an original identification method for the curvature of bridges based on sparse acceleration measurements that can be collected using standard accelerometers. The raw acceleration signal is processed through a particular filter bank that extracts dynamic and quasi-static signal components. The first components are employed to identify modal parameters, from which sparse yet robust estimates of the structural curvature are retrieved. On the other hand, the quasi-static acceleration generated by the structural deflection induced by traffic load is used to identify the curvature influence lines of the bridge, which are fused with modal estimates using a Kalman filter. The state variable of the analyzed system, representing a dense curvature profile of the structure subjected to concentrated loads, can be used as a damage-sensitive feature for high-resolution damage localization. The method is applied to a steel truss bridge subject to different damage configurations. [ABSTRACT FROM AUTHOR]

The difficulty in quantifying the benefit of Structural Health Monitoring (SHM) for decision support is one of the bottlenecks to an extensive adoption of SHM on real-world structures. In this paper, we present a framework for such a quantification of the value of vibration-based SHM, which can be flexibly applied to different use cases. These cover SHM-based decisions at different time scales, from near-real time diagnostics to the prognosis of slowly evolving deterioration processes over the lifetime of a structure. The framework includes an advanced model of the SHM system. It employs a Bayesian filter for the tasks of sequential joint deterioration state-parameter estimation and structural reliability updating, using continuously identified modal and intermittent visual inspection data. It also includes a realistic model of the inspection and maintenance decisions throughout the structural life-cycle. On this basis, the Value of SHM is quantified by the difference in expected total life-cycle costs with and without the SHM. We investigate the framework through application on a numerical model of a two-span bridge system, subjected to gradual and shock deterioration, as well as to changing environmental conditions, over its lifetime. The results show that this framework can be used as an a-priori decision support tool to inform the decision on whether or not to install a vibration-based SHM system on a structure, for a wide range of SHM use cases. [ABSTRACT FROM AUTHOR]

The establishment of a Digital Twin of an operating engineered system can increase the potency of Structural Health Monitoring (SHM) tools, which are then bestowed with enhanced predictive capabilities. This is particularly relevant for wind energy infrastructures, where the definition of remaining useful life is a main driver for assessing the efficacy of these systems. In order to ensure a proper representation of the physical structure, the monitored response of the Digital Twin should match the one experienced by the actual system throughout the complete spectrum of its operational conditions. In most typical SHM configurations, it is only possible to rely on output-only measurements, acquired from finite positions within a structure, which naturally raises the challenge of recovering the full-field operational response, including unmeasured locations. This problem, also known as Virtual Sensing (VS), has been treated using different schemes, including Bayesian filtering and Modal Expansion (ME). In this paper, the Augmented Kalman Filter (AKF) is exploited to this end; a tool which allows for simultaneous full-field response and unmeasured input prediction. The common issue of Bayesian filtering relies on calibration of the filters defining parameters, namely the assumed measurement and process noise covariance levels. While the first is directly related to the accuracy of the employed physical sensors, the latter often acts as a tuning parameter for improving the reliability of the prediction. The process noise covariance adjustment is often performed in an offline fashion, either by making use of regularization methods, e.g., the L-curve method, or via trial and error. In this work, we propose a methodology for automated process noise covariance adaptation, relying on response estimates recovered by means of an improved ME approach. The method is validated on experimental data from a large scale research Wind Turbine (WT) blade made of glass fiber reinforced plastics. [ABSTRACT FROM AUTHOR]

In this paper, a new data assimilation framework for correcting finite element models from datasets acquired on-the-fly in low-frequency dynamics is presented. An Unscented Kalman filter algorithm is coupled with a modified Constitutive Relation Error (mCRE) observer, leading to a Modified Dual Kalman Filter algorithm (MDKF). Built as a Hermitian data-to-model distance written in the frequency domain enriched with a CRE residual accounting for model bias, the mCRE functional has shown interesting assets for model updating purposes, in particular enhanced convexity and robustness to measurement noise. The proposed data assimilation strategy integrates the latter through a metric change in the measurement update equation. It thus differs from classical nonlinear Kalman filtering for parameter estimation as the comparison between measurements and model predictions is achieved through the mCRE functional itself. Besides, the calibration of MDKF internal parameters is facilitated by a set of general guidelines that ensure the performance of the algorithm. The methodology is applied to two earthquake engineering examples. The performances of MDKF are first assessed using synthetic measurements from a plane frame subjected to random ground acceleration. Actual measurements from the SMART2013 benchmark are then assimilated in a real-time context to monitor the eigenfrequency drop of a reinforced-concrete structure submitted to a sequence of gradually damaging shaking-table tests. The nice correlation with (i) data-driven identification results, and (ii) sequential mCRE-based model updating results, illustrates the relevance of this new approach and suggests promising use of MDKF for on-the-fly adaptive control prospects and applications involving data-to-model interaction. • A new Kalman Filter approach is proposed for structural monitoring in dynamics. • The mCRE functional is integrated as observer in a Dual Unscented Kalman Filter. • General guidelines are provided for easy calibration of decisive internal parameters. • The robustness of the methodology is assessed from highly noisy synthetic data. • Actual measurements of the SMART2013 project are processed in a real-time context. [ABSTRACT FROM AUTHOR]

• A mmSHM system is proposed to achieve multi-scale and full-field vibration measurement. • An adaptive segmented phase demodulation method to track vibrations spanning from μm to dm scales. • Full-field vibration measurement method and thoroughly experimental validations. • The accuracy of the mmWave sensing method reaches μm-level. Vibration measurement is of great significance for structural health monitoring (SHM). The emerging microwave vibration measurement technology has distinct advantages over the laser and vision-based approaches, such as long-range, low-power consumption and environmental adaptability. However, it has critical limitations on multi-scale and full-field vibration measurement, especially for SHM of large structures. To overcome these drawbacks, in this paper, we propose mmSHM, a mmWave radar-based vibration measurement approach for SHM. An adaptive segmented phase demodulation (ASPD) method is developed to effectively quantify the displacement variation, allowing accurate displacement measurement spanning from µm to dm scales. What's more, to eliminate the coupling and aliasing interferences from adjacent targets in full field of view, an improved interferometric phase evolution tracking method is presented to extract the vibration displacement from the range-angle joint dimensions. To be specific, the basic principle of vibration measurement using linear frequency-modulated continuous wave (LFMCW) radar is firstly depicted. Then, the detailed procedures of multi-scale and full-field vibration measurement method are illustrated. Finally, with the built prototype of mmSHM, experimental results under different scenarios show that our method can achieve multi-scale and full-field synchronous vibration measurement with µm-level accuracy. The proposed method can be expected to provide a promising approach for contactless vibration measurement in SHM. [ABSTRACT FROM AUTHOR]

• A recursive window-based Bayesian inference approach is formulated and validated. • It is implemented for joint input-parameter estimation of an offshore structure. • Strain time histories at hotspots are accurately predicted through virtual sensing. • The proposed method outperform modal expansion method for virtual sensing. This paper presents a recursive Bayesian inference framework for joint parameter-input identification, and virtual sensing for strain time history prediction of an offshore platform using sparse output-only measurements. The studied offshore platform, known as FINO3, is in the North Sea and is instrumented with a variety of sensors, including accelerometers and strain gauges. Offshore platforms are fatigue critical structures due to harsh marine environmental conditions and continuous cyclic wind and wave loads. Therefore, continuous monitoring of strain time histories at hotspot locations of offshore structures is important for reducing maintenance cost and avoiding unexpected failures. A windowed unscented Kalman filter (UKF) is employed to estimate an uncertain modeling parameter (foundation stiffness) and unknown input load time histories using output-only acceleration and strain measurements. The input loads are divided into overlapping windows, and windowed inputs and model parameters are combined as an augmented state vector in the UKF framework. Then strain time histories at critical locations are estimated through a virtual sensing strategy using the estimated input loads and model parameter. A traditional modal expansion approach combined with model updating is also implemented for the purpose of verification and comparison. The proposed method is first demonstrated through a numerical study using a finite element model of FINO3, where accurate model parameter and input estimations are obtained. Then the approach is further investigated using the actual measurements on FINO3. More accurate strain predictions are provided by the UKF than the modal expansion approach, which recommends the proposed UKF method for fatigue monitoring and input estimation. [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]