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In the structural health monitoring of vibration systems, varying excitation always affects the accuracy of damage identification. The proposed symbolic three-order square matrix damage detection method with the matrix norm as a damage indicator can solve the difficult problem of damage identification under ambient excitation. The new sampling pattern extracts data from signals in the time domain at specific intervals based on the structural properties with the help of the autocorrelation coefficient. Then, the data extracted are converted into symbols and arranged into a three-order square matrix, and the Frobenius norm of the matrix is used for structural damage identification as a reliable damage indicator. In this process, the transmissibility function is employed to eliminate the effects of varying excitation. First, the method was verified by a cracked simply supported beam—a simulated Abaqus model. Then, a wooden truss bridge in the laboratory and an actual engineering scenario under ambient excitation together demonstrated the effectiveness and accuracy of the damage identification method and proved the proposed method to be robust to different types of damage under ambient excitation. Compared with other related methods, this method is more intuitive and efficient. [ABSTRACT FROM AUTHOR]

In the structural health monitoring of vibration systems, varying excitation always affects the accuracy of damage identification. The proposed symbolic three-order square matrix damage detection method with the matrix norm as a damage indicator can solve the difficult problem of damage identification under ambient excitation. The new sampling pattern extracts data from signals in the time domain at specific intervals based on the structural properties with the help of the autocorrelation coefficient. Then, the data extracted are converted into symbols and arranged into a three-order square matrix, and the Frobenius norm of the matrix is used for structural damage identification as a reliable damage indicator. In this process, the transmissibility function is employed to eliminate the effects of varying excitation. First, the method was verified by a cracked simply supported beam—a simulated Abaqus model. Then, a wooden truss bridge in the laboratory and an actual engineering scenario under ambient excitation together demonstrated the effectiveness and accuracy of the damage identification method and proved the proposed method to be robust to different types of damage under ambient excitation. Compared with other related methods, this method is more intuitive and efficient.

Structural health monitoring is the process of implementing a continuous damage detection strategy to optimize the inspection and maintenance schedules of bridges, and extend their lifespans. One of the main challenges of automated damage detection is the lack of data on damaged states, which makes it difficult to validate new approaches in the research and development stage. To alleviate this problem, a monitoring campaign on a two‐span test bridge with defined defects is conducted and documented in this article. The bridge is a steel‐concrete composite structure with a length of 30 m, with two primary steel girders and a segmented concrete deck. The recorded data capture the long‐term ambient data from 18 test days and changing environmental conditions, as well as the short‐term ambient data and dynamic load tests from four damage scenarios with well‐defined damage extents. A mobile measurement system with numerous sensors is used for data acquisition. A shaker is placed on the bridge to excite white noise. The main goal of this article is to document the experimental procedure and perform preliminary plausibility checks on the measured data. First results demonstrate that system response data and environmental conditions are recorded reliably and that environmental effects significantly affect the long‐term measurements. Therefore, a suitable data set is provided as open‐source data for future studies on data normalization and automated damage detection. [ABSTRACT FROM AUTHOR]

Vision-based modal analysis has gained popularity in the field of structural health monitoring due to significant advancements in optics and computer science. For long term monitoring, the structures are subjected to ambient excitation, so that their vibration amplitudes are quite small. Hence, although natural frequencies can be usually identified from the extracted displacements by vision-based techniques, it is still difficult to evaluate the corresponding mode shapes accurately due to limited resolution. In this study, a novel signal reconstruction algorithm is proposed to reconstruct the dynamic response extracted by the vision-based approach to identify the mode shapes of structures with low amplitude vibration due to environmental excitation. The experimental test of a cantilever beam shows that even if the vibration amplitude is as low as 0.01 mm, the first two mode shapes can be accurately identified if the proposed signal reconstruction algorithm is implemented, while without the proposed algorithm, they can only be identified when the vibration amplitude is at least 0.06 mm. The proposed algorithm can also perform well with various camera settings, indicating great potential to be used for vision-based structural health monitoring. [ABSTRACT FROM AUTHOR]

With the advancement in computing power and sensing technology in the last decade, smart monitoring and decision-making approaches are becoming more feasible and replacing the traditional structural health monitoring (SHM) techniques. In this paper, an unsupervised machine learning approach, namely one-class support vector machine (OC-SVM) approach augmented with a localization metric is proposed for damage identification purposes. The auto-regressive (AR) parameters estimated using acceleration measurements collected from the sensors are utilized as damage sensitive features to train the OC-SVM and damage detection stage is accomplished in a decentralized manner where data acquired with each sensor are processed locally. The statistical measures; Itakura distance, Mahalanobis distance, and Fisher criterion are employed as possible means to localize damage once damage is detected by the trained machine. The method is evaluated through numerical simulations on a five-story moment resisting frame with several damage scenarios including loss of member and connection rigidities. The assessment of the methodology is completed through experimental data collected on a one-story one-bay reinforced concrete frame subjected to progressive damage. The proposed methodology shows promise towards implementation as an automated damage assessment tool that can be employed for frame type structures. [ABSTRACT FROM AUTHOR]

Vision-based modal analysis has gained popularity in the field of structural health monitoring due to significant advancements in optics and computer science. For long term monitoring, the structures are subjected to ambient excitation, so that their vibration amplitudes are quite small. Hence, although natural frequencies can be usually identified from the extracted displacements by vision-based techniques, it is still difficult to evaluate the corresponding mode shapes accurately due to limited resolution. In this study, a novel signal reconstruction algorithm is proposed to reconstruct the dynamic response extracted by the vision-based approach to identify the mode shapes of structures with low amplitude vibration due to environmental excitation. The experimental test of a cantilever beam shows that even if the vibration amplitude is as low as 0.01 mm, the first two mode shapes can be accurately identified if the proposed signal reconstruction algorithm is implemented, while without the proposed algorithm, they can only be identified when the vibration amplitude is at least 0.06 mm. The proposed algorithm can also perform well with various camera settings, indicating great potential to be used for vision-based structural health monitoring.

Due to the unpredictability of seismic excitation, the data-driven damage identification method, which only depends on the monitoring response data, has a good development prospect in structural health monitoring. In recent years, damage identification methods based on transmissibility function (TF) and Mahalanobis squared distance (MSD) have been widely studied. However, the existing methods are only applicable to damage warning. To overcome this limitation, an improved method for structural damage localization is proposed. Strain TF is used to eliminate the influence of unknown ambient excitation and unknown seismic excitation, which is more sensitive to local damage than traditional TF based on acceleration and displacement data. The MSD of strain TF is employed to construct a novel damage indicator that is used to identify the damage location. Two numerical simulations have been conducted to verify the feasibility of the method for damage localization and good anti-noise performance. In the case of the multi-damage condition, the novel damage indicator is performed to estimate the severity of damage to some extent. [ABSTRACT FROM AUTHOR]

Summary: In the context of detecting changes in structural systems, several vibration‐based damage detection methods have been proposed and successfully applied to both mechanical and civil structures over the past years. These methods involve computing data‐based features, which are then evaluated in statistical tests to detect damages. While being sensitive to damages, the data‐based features are affected by changes in the ambient excitation properties that potentially lead to false alarms in the statistical tests, a characteristic that renders their use impractical for structural monitoring. In this paper, a damage detection method is presented that is robust to changes in the covariance of the ambient excitation. The proposed approach is based on the Mahalanobis distance of output covariance Hankel matrices, which are normalized with respect to possibly changing excitation properties. The statistical properties of the developed damage feature are reported and used for efficient hypothesis testing. Its robustness towards changes in the excitation covariance is illustrated on numerical simulations and successfully tested on a numerical offshore foundation model. [ABSTRACT FROM AUTHOR]

Background: Modal parameters of a quayside container crane can reflect the vibration performance of a structure at work. Identifying these parameters through modal tests can be used to improve the design scheme, evaluate performance, and detect damage. Method: This paper introduces the process of modal parameter identification based on data-driven stochastic subspace identification (SSI). The proposed algorithm is programmed based on the MATLAB platform. The effectiveness of the algorithm is verified by a numerical three-degree-of-freedom vibration model. In this paper, the overall structure of a quayside container crane was modally tested for the first time under ambient excitation. The test scheme using the mobile measurement method is introduced in detail. The signal data collected by the modal test are used as the input data of the data-driven SSI, and the first seven natural frequencies, damping ratios, and mode shapes of the crane are identified successfully. Furthermore, the finite element model of the crane is established with ANSYS, and the first seven frequencies and mode shapes are also obtained. Results and Conclusions: By comparing the results of the two methods, it can be found that the parameters identified by the proposed algorithm have high accuracy. The proposed method can be applied to the health monitoring system of the quayside container crane. [ABSTRACT FROM AUTHOR]

Temperature has significant effects on modal parameters of a structure. However, it is difficult to do a full-field dynamic measurement and identify modal parameters of the structure under high temperature and working conditions. In this work, three-dimensional digital image correlation combined with Bayesian operational modal analysis is used to yield a full-field non-contact thermal modal testing technique under ambient excitation. Thermal modal verification experiments are carried out on a titanium plate with free boundary conditions under ambient excitation by a low-cost high-temperature dynamic measurement system. Natural frequencies and mode shapes of the plate under different average high temperatures are studied in detail. From the experimental process, it is believed that the thermal modal testing technique proposed in this work is an effective alternative testing technique to traditional contact testing techniques, and after some improvements, one can achieve thermal modal analysis of a structure under high temperature and working conditions. [ABSTRACT FROM AUTHOR]

Since many critical infrastructures of large civil engineering are shear structures and are exposed to ambient excitation most of the operational time, it is crucial to utilize ambient vibration data for damage diagnosis of shear structures. However, some autoregressive moving average with exogenous inputs (ARMAX) model methods will face challenges under ambient excitation due to assumptions about external loads. To address this issue, we propose a new data-driven strategy. Firstly, pseudo-free responses of shear structures are calculated from correlation signals. Subsequently, substructure ARMAX models are constructed using pseudo-free responses, and model residuals are extracted as damage-sensitive features (DSFs). Furthermore, to improve the accuracy of the anomaly diagnosis, the Mel-frequency cepstral coefficients (MFCCs) are employed for dimensionality reduction of model residuals and are combined with Mahalanobis-squared distance (MSD) to form a hybrid distance method. Finally, the practicality and superiority of the strategy are validated using numerical and experimental benchmark structures. • Applicable to ambient vibration data. • Available to diagnose the location and relative extent of damage. • Suitable for unsupervised learning of distributed sensing systems. • Validated using numerical and experimental benchmark structures. [ABSTRACT FROM AUTHOR]

Mobile phones can be used as potential useful tool in structural modal identification. Using the acceleration data collected by mobile phone sensors under ambient excitation, a method for structural modal identification based on variational mode decomposition (VMD) is proposed. Firstly, the feasibility and applicability of mobile phone sensors for vibration data measuring is verified by a shaking table test of a steel frame structure. Then, the mobile phone is used as an accelerometer to record the vibration acceleration of a pedestrian overpass under ambient excitation. In this test, vibration acceleration is effectively decomposed by VMD, and the major components of the vibration signal is obtained through the division and screening of frequency domain. Finally, the modal parameters of the pedestrian overpass are identified, and the results show the identification method based on VMD is effective and feasible. [ABSTRACT FROM AUTHOR]

Due to the unpredictability of seismic excitation, the data-driven damage identification method, which only depends on the monitoring response data, has a good development prospect in structural health monitoring. In recent years, damage identification methods based on transmissibility function (TF) and Mahalanobis squared distance (MSD) have been widely studied. However, the existing methods are only applicable to damage warning. To overcome this limitation, an improved method for structural damage localization is proposed. Strain TF is used to eliminate the influence of unknown ambient excitation and unknown seismic excitation, which is more sensitive to local damage than traditional TF based on acceleration and displacement data. The MSD of strain TF is employed to construct a novel damage indicator that is used to identify the damage location. Two numerical simulations have been conducted to verify the feasibility of the method for damage localization and good anti-noise performance. In the case of the multi-damage condition, the novel damage indicator is performed to estimate the severity of damage to some extent.

An improved damage detection index for a structural component is proposed, using eigenvalues estimated by means of frequency domain decomposition (FDD) and mode contribution subjected to ambient excitation. It is based on vibration measurements obtained from the acceleration data of a simple steel beam. Since the extraction of modal parameters involves practical limitations and, in general, it is difficult to obtain accurate results, therefore in the proposed method a derivative value of the time series acceleration response, termed modal contributing parameter (MCP), is used in combination with eigenfrequencies. The damage is indicated by element stiffness reduction (ESR). Different damage cases for various stiffness reduction values of 1% to 15% were investigated. Damage identification indices for every single damage and multiple damage cases were calculated. The modified MCP damage detection index showed a high index value, even for low-level damage with an element stiffness reduction of as low as 1% over the existing frequency drop and indices based on mode shape change. MCP index derived from the modal response, considering modal contributions to the entire structural response and eigenvalues for damage detection, improved overall sensitivity and reliability of index results. Both single and multiple cases of damage provided equally accurate results based on the MCP index value. [ABSTRACT FROM AUTHOR]

This paper investigates the possibility of utilizing response from natural ice loading for modal parameter identification of real offshore platforms. The test platform is the JZ20-2MUQ jacket platform located in the Liaodong Bay, China. A field experiment is carried out in winter season, as the platform is excited by floating ices. The feasibility is demonstrated by the acceleration response of two different segments. By the SSI-data method, the modal frequencies and damping ratios of four structural modes can be successfully identified from both segments. The estimated information from both segments is almost identical, which demonstrates that the modal identification is trustworthy. Furthermore, by taking the Jacket platform as a benchmark, the numerical performance of five popular time-domain EMA methods is systematically compared from different viewpoints. The comparisons are categorized as: (1) stochastic methods versus deterministic methods; (2) high-order methods versus low-order methods; (3) data-driven versus covariance-driven stochastic subspace identification methods. [ABSTRACT FROM AUTHOR]

Many methods of damage identification in bridge structures have focused on the use of numerical models, modal parameters or non-destructive damage tests as a means of condition assessment. These techniques can often be very effective but can also suffer from specific pitfalls such as, numerical model calibration issues for non-linear and inelastic behaviour, modal parameter sensitivity to environmental and operational conditions and bridge usage restrictions for non-destructive testing. This paper covers alternative approaches to damage identification of bridge structures using empirical parameters applied to measured vibration response data obtained from two field experiments of progressively damaged bridges subjected to ambient and vehicle-induced excitation, respectively. Numerous non-modal vibration-based damage features are detailed and selected for the assessment of either the ambient or vehicle-induced excitation data based on their inherent properties. The results of the application to two real bridges, one under ambient vibration and the other of forced vibration, demonstrate the robustness of the proposed damage features for damage identification using measurements of ambient and vehicle excitations. Moreover, this investigation has demonstrated that the novel empirical vibration parameters assessed are suitable for damage detection, localisation and quantification. CAV cumulative absolute velocity CAD cumulative absolute displacement DVI distributed vibration intensity MCVI mean cumulative vibration intensity IVI instantaneous vibration intensity AIVI Amalgamated instantaneous vibration intensity EMD empirical mode decomposition ICEEMDAN improved complete ensemble empirical mode decomposition with adaptive noise HHT Hilbert–Huang Transform IMF intrinsic mode functions MCD minimum covariance determinate MSD mahalanobis squared distance MTS Mahalanobis Taguchi system [ABSTRACT FROM AUTHOR]

Because it is difficult to determine the degree and location of damage to bridge structures under environmental excitation, a new method combining frequency domain decomposition and strain mode identification for bridge structure damage assessment is proposed and the related identification principle is derived. General variations in the strain mode parameters of different reinforced concrete beam damage states are accordingly studied under ambient excitation. The results show that the proposed strain mode identification method based on frequency domain decomposition has good anti-noise performance and can identify the strain mode parameters of a structure relying solely on the strain response information of the structure, even under strong background noise. The mutation of the strain mode was effectively used to determine the damage condition of reinforced concrete beams, and the adaptability, feasibility, and reliability of the proposed method for modal parameter and damage identification of reinforced concrete beams under environmental excitation were verified. [ABSTRACT FROM AUTHOR]

Vibration-based damage detection methods play a vital role in structural health monitoring, but there are many problems in the application of those traditional damage detection methods; for instance, the ability to promptly sense and accurately locate realistic damage in structures are the primary challenges confronting traditional damage detection methods. Strain modes typically have high sensitivity in structural damage detection. Thus, this paper defined a novel damage detection method based on strain modes. According to the relationship between strain and displacement, the natural excitation technique based on the strain response combined with the eigensystem realization algorithm was applied to identify the strain modal parameters of structures under ambient excitation. The detection index was also employed to localize damage. Crack and corrosion damage of beam-type structures was simulated under experimental conditions. Next, single-damage and multi-damage cases with different degrees and locations were prefabricated experimentally. The results demonstrated that all damage cases were detected in a reasonable manner, thereby demonstrating the validity of the proposed method in structural damage detection. [ABSTRACT FROM AUTHOR]

It is still necessary to investigate the detection of structural damage under ambient excitations since the excitations are random and unmeasured while measurement noises are inevitable. In this paper, a method based on the synthesis of cross-correlation functions of partial structural responses and the extended Kalman filter (EKF) approach is proposed for the identification and damage detection of structures under ambient excitations, in which both independent stationary and non-stationary white noise excitations in the product models are discussed. First, the equations of cross-correlation functions of structural responses are established when the ambient excitations are independent stationary white noise processes. Then, the EKF approach is utilized to identify structural parameters and cross-correlation functions using partial measurements of structural acceleration responses. Structural damage is detected based on the degradations of the identified structural element stiffness parameters. Finally, the proposed method is extended to deal with independent non-stationary white noise excitations in the product models. The numerical simulation examples of the ASCE structural health monitoring benchmark building subject to ambient excitation, a moment resisting frame model under white noise excitation, and a cantilever beam model under multiple independent non-stationary excitations are used to validate the feasibility of the proposed method. It is shown that the method is not sensitive to measurement noises. Also, a lab experimental study of the identification of a multi-story shear structure is investigated to further illustrate the applicability of the proposed method. [ABSTRACT FROM AUTHOR]

In the modal analysis of a structure excited by both the artificial force and the ambient noise, the precision of modal parameter estimation can be improved by making full use of the noise responses. In this paper, a method of modal parameter identification with consideration of noise excitation based on the non-parametric noise model is presented. Applying Hilbert transformation to the power spectrum density (PSD) of the noise response can yield a new function named analogous frequency response function (AFRF), which can be combined with the frequency response function obtained through artificial excitation to identify modal parameters through the ploy-reference least-squares complex frequency-domain (poly-LSCF) algorithm. The presented method is validated by a simulation example of an aircraft wing model and a practical modal test for a plane model. Compared with the traditional methods based on the experimental modal analysis without consideration of ambient excitation, the new method is simpler, more effective and of higher precision in the identification of modal parameters. [ABSTRACT FROM AUTHOR]

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This paper addresses the enhanced identification and localization of structural damage by means of the recorded response induced by ambient excitation. Damage detection is based purely on the vibration energy in structural acceleration records, deriving thereof the normalized cumulative power spectral density as the characteristic damage sensitive quantity. As key aspect of this contribution, a method for 'correction' of the recorded response is proposed, to account for deviations from perfect stationary white noise excitation. Based on an overdetermined system of equations, recorded spectra are modified to better fit previously recorded spectra. This fitting affects broader frequency ranges, while the damage sensitive feature captures changes in narrow frequency bands. Thus, the proposed 'correction' method does not mask or remove the effects of structural changes in the response. Subsequent damage localization in cantilever-like structures is based on the changes of the drift of the fundamental mode shape amplitudes. The efficiency of the proposed two-step damage identification procedure is tested on a small-scale shear frame model in various damaged conditions. It is shown that the defined normalized cumulative power spectral density damage index is suitable to indicate most of the imposed damages, in particular when the proposed response correction methodology is applied. The spots of damage are successfully identified by the utilized mode shape damage indicator. [ABSTRACT FROM AUTHOR]

Damage detection consists of monitoring the deviations of a current system from its reference state, characterized by some nominal property repeatable for every healthy state. Preferably, the damage detection is performed directly on vibration data, hereby avoiding modal identification of the structure. The practical aspect of using only the output measurements cause difficulties because of variations in ambient excitation due to variability in the environmental conditions, like sea, wind, and temperature. In this paper, a new Mahalanobis distance-based damage detection method is studied and compared to the well-known subspace-based damage detection algorithm in the context of two large case studies. Both methods are implemented in the modal analysis and structural health monitoring software ARTeMIS, in which the joint features of the methods are concluded in a control chart in an attempt to enhance the resolution of the damage detection. The damage indicators from both methods are evaluated based on the ambient vibration signals from numerical simulations on a novel offshore support structure and an experimental campaign with a full scale bridge. The results reveal that the performance of the two damage detection methods is similar, hereby implying merit of the new Mahalanobis distance-based approach, as it is less computational complex. The fusion of the damage indicators in the control chart provides the most accurate view on the progressively damaged systems. [ABSTRACT FROM AUTHOR]

The state-of-the-art modal identification for civil structures is limited to the estimation mode shapes with a low spatial resolution. Modal coordinates are identified only at the location of sensors, which are fixed at particular locations on the structure. Increasing the number of sensors in the structure is an alternative to increase the spatial resolution. Unfortunately, this increases the cost of the instrumentation and the data to be transmitted and processed. Another common alternative is to use numerical algorithms to expand the modal coordinates to nonmeasured degrees of freedom. However, mode shape expansion techniques could introduce errors in the identified modes. This paper presents the formulation, evaluation, and validation of a methodology for the ambient vibration-based modal identification using mobile sensors (MIMS). The methodology uses two sensors, one mobile and one stationary sensor, to identify spatially dense modes of vibration. The methodology is experimentally verified by using a uniform simply supported steel beam. Results show that MIMS is able to identify dense mode shapes using a fraction of sensors and data when compared with traditional modal identification techniques. [ABSTRACT FROM AUTHOR]

Autocorrelation is commonly used in signal processing for analyzing time signals, however the shape and magnitude of its statistical error, caused by its estimation from finite length measurements, is rarely addressed. Previous research showed that in the general case, the error on the autocorrelation estimator of an arbitrary signal is inversely proportional to the square root of the signal length. For operational modal analysis of linear systems under unknown ambient excitation, this error seems to present a peculiar shape influencing the estimation of damping ratios. In this paper the error on autocorrelation for a linear, time-invariant system subjected to white Gaussian noise is analytically and numerically studied. It is found to be very similar to the system's response to an ambient noise, thus with a frequency content close to that of the expected autocorrelation itself. An upper bound of error magnitude is found for each mode of the system. Then, the resulting error on damping computations is investigated through numerical simulations applied to a two degrees of freedom mechanical system. Numerical outcomes show good agreement with the theoretical part and are then supported by experimental data of a prestressed concrete bridge under ambient traffic. This study allows for a better understanding and quantification of the inaccuracy on damping ratios computation through techniques using autocorrelation in time domain, notably the logarithmic decrement or time–frequency domain decomposition based on wavelet analysis. • Autocorrelation is a widely used tool in Operational Modal Analysis. • Estimation of autocorrelation from finite length signals causes statistical errors. • Errors in autocorrelation propagate to the estimation of damping. • These statistical errors on autocorrelation and damping are estimated. • Validation through numerical simulations and experimental data. [ABSTRACT FROM AUTHOR]

Structural modal parameter identification under ambient excitation has strong engineering value and theoretical significance. As the most popular tool for solving Blind Source Separation (BSS) problems, Independent Component Analysis (ICA) is able to directly extract the time-domain modal parameters, including frequencies, damping ratios and modal shapes. ICA, however, has a fatal flaw of failing to identify structures with higher damping. To overcome the flaw above, the paper proposes a new method named “ICA + IDT”. Firstly, free vibration response of a structure is obtained from structural outputs under ambient excitation. Inverse damping transfer (IDT) is employed to turn a highly damped signal into a low damping response signal without changing of frequencies and mode shapes. Then, structural modal parameters are extracted from the low damping response signal by ICA. Finally, the identified damping ratios are adjusted to eliminate the impact of IDT. To verify the effectiveness and applicability of IDT + ICA proposed herein, two numerical simulations—mass-spring model and simply supported concrete beam—and an experiment model of three-story steel frame are built, and the analysis results reveal that presented method can identify structures with higher damping effectively. [ABSTRACT FROM AUTHOR]

Under ambient excitation of white noise, structural modal parameters can be identified from cross-power spectrum calculated from structural outputs, without known input. The minimization of difference between theoretical value and test value of cross-power spectrum - the former is an equation including modal parameters to be identified, and the latter is computed based on the output-only data of structure - is adopted as an objective function for optimization. The optimal objective function value can be obtained through optimal modal parameter search. Quantum-behaved particle swarm optimization (QPSO) - swarm intelligence optimization algorithm based on particle swarm optimization (PSO) - is used for optimization to identify the modal parameters of structure under the ambient excitation. Then, the modal parameter identification method based on QPSO presented herein had been demonstrated by a numerical simulation of three-span continuous beam and experimental results of a three story structure. The computed results indicated that QPSO can be effectively used in the structural modal parameter identification under ambient excitation. [ABSTRACT FROM AUTHOR]

Modal parameter identification is a core issue in the health monitoring and damage detection of hydraulic structures. The parameters are mainly obtained from the measured vibrational response under ambient excitation. However, the response signal is mixed with noise and interference signals, which will cover the structure vibration information; therefore, the parameter cannot be identified. This paper proposes an improved filtering method based on an ensemble empirical mode decomposition (EEMD) and wavelet threshold method. A ‘noise index’ is presented to estimate the noise degree of the components decomposed by the EEMD, and this index is related to the wavelet threshold calculation. In addition, the improved filtering method combined with an eigensystem realization algorithm (ERA) and a singular entropy (SE) is applied to an operational modal identification of a roof overflow powerhouse with a bulb tubular unit. [ABSTRACT FROM AUTHOR]

An accurate awareness of the properties of the interarea electromechanical oscillation modes is essential for the secure operation of the power systems. Previously, the Ibrahim time domain (ITD) method has been applied to estimate the interarea oscillation modes using ring-down signals caused by major disturbances (such as three phase faults). In this paper, through the combination of the random decrement technique (RDT) and the ITD method, the RDT–ITD method is proposed to estimate the interarea modes during ambient operation of the power systems, using random responses caused by random changes of the loads. The estimated parameters include the frequencies, damping ratios and mode shapes. Considering the fact that both the ambient excitations and the measurement noise are stochastic in nature, Monte Carlo simulations were conducted to evaluate the performances of the RDT–ITD method in a statistical way. Simulation results in the 16 machine nonlinear power system model, as well as comparison results with the RDT-Prony method, the NExT-ERA method and the Subspace method realized by N4SID (Numerical algorithm for Subspace State Space System Identification) show that the RDT–ITD method is promising in estimating the interarea modes during ambient operation conditions. The RDT–ITD method was also validated using real PMU measurements from Sichuan power grid of China. [ABSTRACT FROM AUTHOR]

Early damage detection not only improves the safety and reliability of structures but also reduces maintenance cost. However, damage detection is difficult to implement in large structures under ambient excitation because of the limitation of sensors, the uncertainty of ambient excitation, and the global properties of modal frequencies and displacement modes. This paper proposes a new damage detection method that employs the real encoding multi-swarm particle swarm optimization algorithm and fitness functions evolved from strain modes to find the optimal match between measured and simulated modal parameters and to determine the actual condition of structures. The proposed method requires low-frequency modes and incomplete modes and does not require mass normalization of parameters, thus making the method suitable for nondestructive dynamic damage detection of large structures under ambient excitation. Taking a concrete guide wall structure as an example, this paper studied the global searching performance and the sensitivity of the proposed method. The efficiency of the proposed method was analyzed by using different noise levels and sensor numbers. Results show that the proposed method is effective and can be applied in many types of large structures. [ABSTRACT FROM PUBLISHER]

Abstract: In the last ten years, monitoring the integrity of the civil infrastructure has been an active research topic, including in connected areas as automatic control. It is common practice to perform damage detection by detecting changes in the modal parameters between a reference state and the current (possibly damaged) state from measured vibration data. Subspace methods enjoy some popularity in structural engineering, where large model orders have to be considered. In the context of detecting changes in the structural properties and the modal parameters linked to them, a subspace-based fault detection residual has been recently proposed and applied successfully, where the estimation of the modal parameters in the possibly damaged state is avoided. However, most works assume that the unmeasured ambient excitation properties during measurements of the structure in the reference and possibly damaged condition stay constant, which is hardly satisfied by any application. This paper addresses the problem of robustness of such fault detection methods. It is explained why current algorithms from literature fail when the excitation covariance changes and how they can be modified. Then, an efficient and fast subspace-based damage detection test is derived that is robust to changes in the excitation covariance but also to numerical instabilities that can arise easily in the computations. Three numerical applications show the efficiency of the new approach to better detect and separate different levels of damage even using a relatively low sample length. [Copyright &y& Elsevier]

The statistical subspace-based damage detection technique has shown promising theoretical and practical results for vibration-based structural health monitoring. It evaluates a subspace-based residual function with efficient hypothesis testing tools, and has the ability of detecting small changes in chosen system parameters. In the residual function, a Hankel matrix of output covariances estimated from test data is confronted to its left null space associated to a reference model. The hypothesis test takes into account the covariance of the residual for decision making. Ideally, the reference model is assumed to be perfectly known without any uncertainty, which is not a realistic assumption. In practice, the left null space is usually estimated from a reference data set to avoid model errors in the residual computation. Then, the associated uncertainties may be non-negligible, in particular when the available reference data is of limited length. In this paper, it is investigated how the statistical distribution of the residual is affected when the reference null space is estimated. The asymptotic residual distribution is derived, where its refined covariance term considers also the uncertainty related to the reference null space estimate. The associated damage detection test closes a theoretical gap for real-world applications and leads to increased robustness of the method in practice. The importance of including the estimation uncertainty of the reference null space is shown in a numerical study and on experimental data of a progressively damaged steel frame. • An output covariance Hankel matrix is confronted to its left reference null space in damage residual. • The residual distribution is derived, also considering estimation uncertainties of the reference. • Thresholds for early damage detection can be defined precisely a priori. • The resulting detection test has increased performance in particular for short reference data. • The method is applied to experimental data under small damage and for short data. [ABSTRACT FROM AUTHOR]

Abstract: In this study, Operational Modal Analysis (OMA) is used to identify the damping value of the fundamental for-aft (FA) mode of an Offshore Wind Turbine (OWT) using both real life measurements and simulations. Estimations of the total damping of an offshore wind turbine (taking into account the effects of the aerodynamic, hydrodynamic and soil loads) give a quantitative view of the stability characteristics of the wind turbine. Two different test cases including an overspeed stop and ambient excitation have been considered. The experimental data has been obtained during a measurement campaign on an offshore wind turbine in the Belgian North Sea and the results are compared with the numerical simulations which have been carried out in HAWC2. [Copyright &y& Elsevier]

The focus of this work is on methods for modal identification of civil structures using output data only. An important family of time domain methods uses autoregressive time series models and exploits formulations developed in the field of system control. Another strategy consists of using methods well tested in the identification of structures on the basis of impulse response or free decay and extending them to the analysis of response signals generated by excitations of a more general nature. A third option refers to the Ho-Kalman minimal realization algorithm, which was extended by Akaike and Aoki to stochastic systems. These approaches, or their combinations, include a sizable proportion of the methods actually used in output-only identification of civil structures subjected to natural excitation, and most of them are based on stationarity assumptions. The question that prompted this study was as follows: what degrees of reliability and accuracy can such methods ensure when they are used, as is often the case in actual practice, in nonstationary conditions? An answer to this question was sought numerically by focusing on nonstationary conditions deemed typical of the actions naturally applied to civil structures. [ABSTRACT FROM AUTHOR]