A generalized flexibility matrix based approach for structural damage detection

Bayesian‐optimized unsupervised learning approach for structural damage detection

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.

The frequency-shift-based scheme is one of the popular structural damage detectionmethods used in the field. Assuming that the damage will cause structural parameter(e.g. mass or stiffness) changes, one can detect damage by observing natural frequencyvariations before and after damage occurs. One of the major limitations of thefrequency-shift-based damage detection approach is that the natural frequencies could beinsensitive to local and small parameter variations (damages) in the structure. To addressthis problem, the concept of sensitivity-enhancing control has been developed andexplored by researchers. Recently, an eigenstructure (dual eigenvalue–eigenvector)assignment-based approach has been proposed to advance the state of the artof sensitivity-enhancing control design for multiple degrees-of-freedom systems(Jiang et al 2007 Trans. ASME, J. Vib. Acoust. 129 771–83). While promisingfeatures have been shown, the approach has not been experimentally verified. Inaddition, such a scheme requires an accurate analytical model, which is in generaldifficult to obtain for complex structures. The purpose of this research is to advancethe state of the art by exploring an experimental-based sensitivity-enhancingcontrol approach for structural damage detection. That is, we aim at utilizingmathematical models identified from experimental measurement data for controllerdesign and system analysis, and hence eliminate the requirement of an accurateanalytical model. With the identified model, an eigenstructure assignment-basedoptimization scheme is utilized to design the closed-loop control to best enhance thenatural frequency sensitivity to structural damages. An experimental investigationbased on a test bed beam structure is conducted to demonstrate and verify theproposed method. Sensitivity-enhancing controls are designed to increase thefirst two natural frequency sensitivities to mass changes in the beam structure.Experimental results show that by applying the designed sensitivity-enhancing controls tothe beam structure, the natural frequency sensitivities to mass variations canbe significantly enhanced. Results also show that the damage locations can besuccessfully identified and the damage severity can be predicted by using the measuredfrequency changes of the closed-loop systems before and after damage occurrence.

Distributed long gauge strain sensing technology has solved the problem of difficult identification of local damage in traditional ‘point’ monitoring, and has received extensive attention in the field of structural damage identification. Owing to the inevitable presence of measurement noise and environmental factors in the macro strain response measurement, a single damage index has also underlined some drawbacks generally arising when multiple damages occur, or errors affect the identified dynamic properties of the systems. To address these challenges, this paper proposes a data fusion method based on the Dempster–Shafer evidence theory, relying on distributed strain sensing technology. The identification results of the modal macro strain-based index and quasi-static macro strain energy-based damage index are fused to make a comprehensive decision on structural damage location. Damage identification studies are conducted on different types of structures under impact loads and random wind loads to verify the effectiveness and accuracy of the proposed data fusion method in the case of single and multiple damage conditions. The results show that the proposed data fusion method can accurately identify the damage location and effectively reduce misjudgment on undamaged locations; it has potential application value in practical structural health monitoring.

Two-stage structural damage detection using fuzzy neural networks and data fusion techniques

Based on the measured vibration information, a method for detecting reduction in stiffness parameters of a structure using generalized flexibility matrix and changes in natural frequencies is presented in this paper. For incomplete measured data of the first mode shape, dynamic expansion of mode shape is introduced. The generalized flexibility matrix is approximately constructed by using the measured first frequency and the extended mode shape only, and it is further used to detect the possible damaged elements. An iterative algorithm using the changes of the first several natural frequencies only is subsequently used to update the damage extent of these possible damaged elements, while the number of damage-extent parameters to be determined in the algorithm is remarkably reduced. Finally, two numerical examples concerning a two-span beam and a two-story frame are used to illustrate the effectiveness of the proposed method.

Nowadays, structural health monitoring has attracted much attention, due to the construction of important and complex structures and their safety. One branch of structural health monitoring is damage detection, estimating the location and extent of eventual damages in the structural systems. In this study, an efficient method is introduced to determine the location and extent of damage in the frame, beam and truss structures using time domain response and an optimization algorithm. First of all, the structural damage detection problem is formulated as a standard optimization problem. So that, the optimization objective function is defined by using the acceleration of damaged structures and analytical model acceleration. The acceleration is obtained using Newmark method. Damage is simulated by reducing the elasticity modulus of structural members. Then, damage problem converted to an optimization problem are solved by teaching–learning-based optimization (TLBO) algorithm therefore, the exact location and extent of damage in structure can be determined. In order to show the capabilities of the proposed approach for identifying structural damage, four illustrative test examples are considered with considering measurement noise effect. The results clearly show that the proposed method is as a powerful method to detect multiple damage in structures.

Damage can be identified using generalized flexibility matrix based methods, by using the first natural frequency and the corresponding mode shape. However, the first mode is not always appropriate to be used in damage detection. The contact interface of rod-fastened-rotor may be partially separated under bending moment which decreases the flexural stiffness of the rotor. The bending moment on the interface varies as rotating speed changes, so that the first- and second-modal parameters obtained are corresponding to different damage scenarios. In this paper, a structural damage detection method requiring single nonfirst mode is proposed. Firstly, the system is updated via restricting the first few mode shapes. The mass matrix, stiffness matrix, and modal parameters of the updated system are derived. Then, the generalized flexibility matrix of the updated system is obtained, and its changes and sensitivity to damage are derived. The changes and sensitivity are used to calculate the location and severity of damage. Finally, this method is tested through numerical means on a cantilever beam and a rod-fastened-rotor with different damage scenarios when only the second mode is available. The results indicate that the proposed method can effectively identify single, double, and multiple damage using single nonfirst mode.

SUMMARY In this study, a data-driven approach for structural seismic damage detection and localization in a multiple-degree-of-freedom shear-type building structure subjected to strong ground motion is presented. The proposed method is based on the joint implementation of time–frequency analysis and fractal dimension (FD) characteristics. The approximate analytical wavelet transform is first used to obtain the time–frequency feature (TFF) of the transient response at the measured story. The TFF is defined as the real part of the wavelet coefficients. Next, the box-counting method is used to acquire the FD of the TFF within the fundamental frequency band. It is verified that the proposed FDs at all stories of the linear system are identical, whereas the FDs at the stories with nonlinearities will be different from those at the stories with linearity. Therefore, the nonlinearity of the structure caused by strong ground motion can be detected and localized by comparing the FDs at the measured stories. A numerical simulation on a 10-story shear-type building was conducted. The simulation results indicate that the aforementioned approach is capable of detecting and localizing single-location or multiple-location seismic damages in shear-type building structures under various seismic excitations and is robust to measurement noise. Finally, the sensor placement for this approach, the effect of damping ratio, and choice of interstory drifts or accelerations as the measured signals are investigated and discussed. Copyright © 2012 John Wiley & Sons, Ltd.

Artificial Neural Networks (ANNs) have been applied in structural damage detection as a classifier, but generally a capable ANNs has to be trained with a certain amount of samples. When both damage locations and damage extents are to be identified, the amount of training samples is tremendous because of the combinations of damage locations and extents. By wavelet transform of the structure free motion equations, the Residual Wavelet Coefficient Vector (RWCV) is deduced. A damage feature parameter is defined as the ratio between RWCVs in two different frequency bands. This parameter has a unique property that it's sensitive only to damage locations, and is independent of damage extents. The damage feature parameters are then fed to the neural network for damage localization. After the damage sites are detected, the damage extent is further identified by another neural network with RWCVs as inputs. This two-phase approach for damage localization and extent identification can simply the neural network and reduce the training samples tremendously. Finally a numerical example is given for damage detection of a 10 DOFs system using the proposed approach.

Recently, the authors proposed computationally attractive algorithms to determine the location and extent of structural damage for undamped structures assuming damage results in a localized change in stiffness properties. The algorithms make use of a finite-element model and a subset of measured eigenvalues and eigenvectors. The developed theories approach the damage location and extent problem in a decoupled fashion. First, a theory is developed to determine the location of structural damage. With location determined, a damage extent theory is then developed. The damage extent algorithm is a minimum rank perturbation, which is consistent with the effects of many classes of structural damage on a finite-element model. In this work, the concept of the minimum rank perturbation theory (MRPT) is adopted to determine the damage extent on the mass properties of an undamped structure. In addition, the MRPT is extended to the case of proportionall y damped structures. For proportionally damped structures, the MRPT is used to find the damage extent in any two of the three structural property matrices (mass, damping, or stiffness). Finally, illustrative case studies using both numerical and actual experimental data are presented. HE advent of the Space Shuttle has prompted considerable attention to the design and control of large space structures. Due to the large size and complexity of envisioned structures, as well as the use of advanced materials to reduce structural weight, it may become necessary to develop a structural health monitoring system to detect and locate structural damage as it occurs. From experience gained in the machinery health monitoring field, one would expect the vibration signature of the structure, either frequency response functions and/or modal parameters, to provide useful information in determining the location and extent of structural damage. Assume that a refined finite element model (FEM) of the structure has been developed before damage has occurred. By refined, we mean that the measured and analytical modal properties are in agreement. Next, assume that at a later date some form of structural damage has occurred. If significant, the damage will result in a change in the structures modal parameters. The question is: can the discrepancy between the original FEM modal properties and postdamage modal properties be used to ascertain structural damage? Most prior work in damage detection has used the general framework of FEM refinement (system identification) in the development of damage assessment algorithms. The motivation behind the development of FEM refinement techniques is based on the need to validate engineering FEMs before their acceptance as the basis for final design analysis. The standard problem has been to seek a refined FEM that is as close to the original FEM and whose modal properties are in agreement with those that are measured subject to various constraints such as symmetry and sparsity preservation. A considerable amount of work in this area has been

A coupled approach for damage detection of framed structures using piezoelectric signature

The representation of structural dynamics in the absence of physics-based models, is often accomplished through the identification of parametric models, such as the autoregressive with exogenous inputs, e.g. ARX models. When the structure is amenable to environmental variations, parameter-varying extensions of the original ARX model can be implemented, allowing for tracking of the operational variability. Yet, the latter occurs in sufficiently longer time-scales (days, weeks, months), as compared to system dynamics. For inferring a “global”, long time-scale varying ARX model, data from a full operational cycle has to typically become available. In addition, when the sensor network comprises multiple nodes, the identification of long time-scale varying, vector ARX models grow in complexity. We address these issues by proposing a distributed framework for structural identification, damage detection and localization. Its main features are: (i) the individual estimation of local, single-input-single-output ARX models at every operational point; (ii) the long time-scale representation of each individual ARX coefficient via a Gaussian process regression, which captures dependency on varying Environmental and Operational Conditions (EOCs); (iii) the establishment of a distributed residual generation algorithm for damage detection, which produces time-series of well-defined stationary statistics, with detected discrepancies used for damage diagnosis; and, (iv) exploitation of ARX-inferred mode shape curvatures, obtained via ARX-inferred global state-space models, of the healthy and damaged states, for damage localization. The method is assessed via application on two numerical case studies of different complexity, with the results confirming its efficacy for diagnostics under varying EOCs.

Genetic fuzzy system for damage detection in beams and helicopter rotor blades

Efficient wind energy harvesting becomes more and more important as a consequence of the increasing interest in renewable energy in the European Union [1]. This leads to growing sizes of wind turbines (WTs), and with it, larger WT blades (WTBs). The structural designs of these WTBs are created to optimize the potential energy output, where low mass is a key requirement. However, high flexibilities and lower buckling capacities are further results of these developments [2], thus certain damage scenarios become significant. Intelligently designed structural health monitoring (SHM) systems can help to reduce the associated operations and maintenance costs. Even though, several techniques are already developed for structural damage detection (SDD) in WTBs, the majority of these methods is not suitable for inservice measurements. This paper presents a SDD and structural damage localization (SDL) method based on the partial autocorrelation function (PACF) of vibration responses. The approach is applied to a numerical model of a large WTB, where the acceleration responses are obtained from transient dynamic simulations with a simplified aerodynamic loading approach. The novel damage sensitive feature (DSF) is developed as the Mahalanobis distance between a baseline and current vector of PACF coefficients. First, numerical modal analysis of the finite element (FE) WTB model is performed in order to estimate the effect of a disbonding damage scenario on the vibration characteristics. Second, the behaviour of the PACF for time series of the healthy system is discussed. Third, the SDD results on the basis of statistical hypothesis testing are assessed for two selected sensor locations and increasing damage extents. Finally, the performance of the proposed DSF with respect to SDL is illustrated for multiple locations on the WTB’s surface. This study demonstrated the efficiency of a DSF based on the PACF for SDD and SDL, which is promising for future developments of vibration-based SHM techniques in WTBs.