Substructural damage detection in shear structures via ARMAX model and optimal subpattern assignment distance

Substructuring approaches possess many superiorities over traditional global approaches in damage identification because large-size global structures are replaced by small and manageable substructures. This paper proposes a substructural time series model for locating and quantifying the damage in complex systems. A substructural autoregressive moving average with exogenous inputs (ARMAX) model is established to extract the frequencies and mode shapes of substructures as indicators for damage detection. The detection of structural damage is essentially an inverse problem, and the damage in structure bears sparse properties. The inverse problem of substructural damage identification is efficiently solved via sparse regularization, and structural damage can be located and quantified through the nonzero terms in the solution vector. The accuracy of the proposed method is demonstrated by the numerical simulation of a frame structure and shaking table test of a shear building structure. As the substructural properties are more sensitive to local structural damage than the global properties, the substructural ARMAX model is quite accurate and efficient to be used in the damage identification of a complex system.

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.

The stability analysis of Electrocardiogram (ECG) intervals helps in determining its response to various disturbances in the cardiac cycle. ARMAX (Autoregressive with Moving Average and Exogenous Input) modeling of QT Interval (QTI) using RR Interval (RRI) as exogenous input shows the dependency of ventricular repolarization on heart rate. This study predicts the QTI using the ARMAX model, to evaluate the stability of QTI dynamics and then, to identify the factors responsible for causing instabilities. One minute-long ECG signals were recorded from Normal sinus Rhythm (NSR) subjects and the QTI of each signal are then modeled using the ARMAX approach. The stability of QTI dynamics was determined using the pole-zero plot. The proposed ARMAX model was capable of detecting unstable QTI in NSR subject and the unstable QTI was mainly due to Heart Rate Variability (HRV) within one-minute ECG.

A wireless sensing system is designed for application to structural monitoring and damage detection applications. Embedded in the wireless monitoring module is a two-tier prediction model, the auto-regressive (AR) and the autoregressive model with exogenous inputs (ARX), used to obtain damage sensitive features of a structure. To validate the performance of the proposed wireless monitoring and damage detection system, two near full scale single-story RC-frames, with and without brick wall system, are instrumented with the wireless monitoring system for real time damage detection during shaking table tests. White noise and seismic ground motion records are applied to the base of the structure using a shaking table. Pattern classification methods are then adopted to classify the structure as damaged or undamaged using time series coefficients as entities of a damage-sensitive feature vector. The demonstration of the damage detection methodology is shown to be capable of identifying damage using a wireless structural monitoring system. The accuracy and sensitivity of the MEMS-based wireless sensors employed are also verified through comparison to data recorded using a traditional wired monitoring system.

Detection of Subtle Damage in Structures through Smart Signal Reconstruction

The high structural deficient rate poses serious risks to the operation of many bridges and buildings. To prevent critical damage and structural collapse, a quick structural health diagnosis tool is needed during normal operation or immediately after extreme events. In structural health monitoring (SHM), many existing methods will have limited usefulness in the quick damage identification process because (1) the damage event needs to be identified quickly, and (2) postdamage information is usually unavailable. To address these drawbacks, we propose a new damage detection and localization approach based on stochastic time series analysis. Specifically, damage sensitive features, which are extracted from vibration signals, follow different distributions before and after a damage event. Hence, we use optimal change-point detection theory to find the time of damage occurrence. Because existing change-point detectors require the postdamage feature distribution, which is unavailable in SHM, we propose a maximum likelihood method for learning the distribution parameters from the time-series data. The proposed damage detection using estimated parameters achieves optimal performance. Also, we utilize the detection results to find damage location without any further computation. Validation results show highly accurate damage identification in American Society of Civil Engineers benchmark structures and two shake table experiments.

Over the past two decades, multi-sensor data fusion method has attracted increasing attention to structural health monitoring due to its inherent capabilities in extracting information from different sources and integrating them into a consistent, accurate and intelligible data set. Meanwhile, since the probabilistic neural network (PNN) describes measurement data in a Bayesian probabilistic approach, it has been successfully applied to structural damage detection (Jiang et al. 2004; Klenke et al. 1996; Ko et al. 1999; Ni et al. 2001). In order to make full use of multi-sensor data (or information) from multi-resource and to improve the diagnosis accuracy of the health conditions for complex structures, it is advisable to combine these methods and exploit their individual advantages. In this paper, a 5-phase complex structural damage detection method by integrating data fusion and PNN is developed and implemented. The proposed method is then applied to damage detection and identification of two simulation examples. The result shows that the proposed method is feasible and effective for damage identification.

Vibration-based damage identification is a useful tool for structural health monitoring. But, the damage detection results always have uncertainty because of the measurement noise, modeling error and environment changes. In this paper, information fusion based on D-S (Dempster-Shafer) evidence theory and Shannon entropy are employed for decreasing the uncertainty and improving accuracy of damage identification. Regarding that the multiple evidence from different information sources are different importance and not all the evidences are effective for the final decision. The different importance of the evidences is considered by assigning weighting coefficient. Shannon entropy is a measurement of uncertainty. In this paper it is employed to measure the uncertainty of damage identification results. The first step of the procedure is training several artificial neural networks with different input parameters to obtain the damage decisions respectively. Second, weighing coefficients are assigned to neural networks according to the reliability of the neural networks. The Genetic Algorithm is employed to optimize the weighing coefficients. Third, the weighted decisions are assigned to information fusion center. And in fusion center, a selective fusion method is proposed. Numerical studies on the Binzhou Yellow River Highway Bridge are carried out. The results indicate that the method proposed can improve the damage identification accuracy and increase the reliability of damage identification to compare with the method by neural networks alone.

In this paper, an improvement of the elliptical triangulation method for damage detection using Lamb waves is presented. The damage is the main cause of structural failure and often occurs on structures. In order to avoid sudden failure, a special attention was given in the past decades to the damage detection in structures. In order to obtain efficient damage detection techniques, the structural health monitoring has been the main research topic of many scientists worldwide. The elliptical triangulation method, proposed in this paper, is a non-destructive method based on measurement of Lamb waves. This method, through the calculation of the time of flight of the signals and the actuator-sensor positioning, allows to identify position and dimension of the damage. The application of the method to the metallic structures and to the composite material structures is presented in this paper. The complexity connected with the uncertainty of the waves’ propagation speed due to the anisotropy of the composite materials has been explored through an iterative approach. The initialization of the wave propagation speed at first tentative iteration is the key issue for the convergence of the method. Seven different conditions were used to validate the method on both metallic and composite structures combining two damage shapes, two damage dimensions (effective damaged area), and three different positions. Upon evaluating the effectiveness, the method has been applied at two composite panels in order to detect by test the post-impact damages. Tests results have been compared with the numerical ones. The feasibility of the elliptical triangulation method to detect the damage (evaluating the damage position and area) has been proved using the ultrasonic C-Scan.

Development of a N-type GM-PHD filter for multiple target, multiple type visual tracking

Two substructure approaches combined with different statistical techniques are presented in the paper to reduce the challenges in the health monitoring system, such as the computation complexity, the number of unknown parameters and the number of sensors. In the approaches, a complete structure is divided into smaller substructures which are modelled as a series of single-degree-of-freedom (SDOF) systems by manipulating the equations of motion of the substructures. Newmark’s method is utilized to construct the discrete systems only containing accelerations from the continuous systems. The structural parameters are then separately identified in each system using statistical techniques. A new substructure approach incorporated with the constrained least square algorithm is proposed by the authors. This new approach along with the previous substructure approach based on the ARMAX models are both evaluated and compared in two laboratory experiments including system identification of a three-story structure and damage detection of a two-story structure. All the possible ways to segment the shear structure into substructures are also investigated in the study, where the substructures with lower model errors are selected. The proposed substructure approaches are able to estimate structural parameters every two seconds, and the structural performance is evaluated through the rates of changes of structural parameters. A real-time structural health monitoring (SHM) system can be realized based on the proposed substructure approaches with processing several accelerations each time.

Many composite materials and structures are susceptible to defects, which can significantly reduce the strength of structures and may grow to failure. To avoid the catastrophic failure of structures, development of a reliable method of structural health monitoring is one of the most important keys in maintaining the integrity and safety of structures. Dynamic response-based damage detection offers a simple procedure as an alternative to the conventional nondestructive evaluation techniques. However, this technique depends on the quality of measured data for its identification accuracy. In this article, experimental aspects of dynamic response-based damage detection technique on carbon/ epoxy composites are addressed. Smart piezoelectric materials are used as sensors or actuators to acquire the curvature modes of structures. These materials are surface-bonded to the beams. An impulse hammer is used as an actuating source as well. Four types of damage detection algorithms are evaluated for several possible damage configurations with two different excitation sources. The quality of damage identification with the four different detection algorithms is discussed. These experimental damage identification techniques using curvature modes and piezoelectric materials can be effectively used in damage detection and health monitoring of composite structures.

The detection of structural damages, either on-line or almost on-line, based on vibration data measured from sensors, is essential for the structural health monitoring system. The problem is quite challenging, in particular when the external excitations are not completely measured and when the structural system is complex. In practical applications, external excitations (inputs), such as seismic excitations, wind loads, traffic loads, etc., may not be measured or may not be measurable, and the structure may not always be shear-beam type which can be easily represented as spring-mass system. In this paper, a newly proposed damage detection method, referred to as the adaptive quadratic sum-squares error with unknown inputs (AQSSE-UI), is used for the detection of structural damages of a plane steel truss with finite element model. In this approach, external excitations and some structural responses may not be measured. Analytical recursive solution for the proposed AQSSE-UI method will be presented. The accuracy and effectiveness of the proposed approach will be demonstrated by numerical simulations where the structure is excited by different external loads. The simulation results indicate that the proposed approach is a viable damage detection technique capable of: (i) identifying structural parameters, (ii) tracking the changes of parameters leading to the detection of structural damages, and (iii) identifying the unknown external excitations.

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

An innovative methodology based on the use of fiber Bragg gratings as strain sensors and strain field pattern recognition is proposed for damage detection in composite materials structures. The strain field pattern recognition technique is based on principal component analysis. Damage indices ( T2 and Q) and detection thresholds are presented. New techniques for unfolding and scaling tridimensional matrices arrays obtained from structures working under variable load conditions are presented.