Real-time Damage Detection Research Articles

Structural health monitoring algorithm application to a powerboat model impacting on water surface

In naval field, live monitoring of local strains and displacements in the hull is the basis for dynamic studies such as checking the design limits, sea-keeping tests in smooth and rough seas, fatigue life estimation and damage detection. Vessels sailing on water are subject to impulsive loadings and local deformations; in these conditions the damage detection in real time becomes crucial. In this paper, a numerical methodology is proposed to measure the deformation of the whole structure of a powerboat entering the water free surface starting from local strain measurements, obtained numerically in a FE simulation. A modal decomposition approach has been used to reconstruct the structural response of the whole boat body. The reconstruction algorithm is calibrated for this study by means of the normalized modal strains matrix obtained through a FEA. A transient FE analysis is implemented to generate local strain signals from virtual sensors. In this analysis hydrodynamic loading resulting from well-known models are applied. The positioning and number of the virtual reference and control sensors are investigated. Virtual control sensors are utilized to compare strains with respect to the reconstructed quantities. Subsequently, the structural health monitoring algorithm has been applied to the powerboat model with a localized damage on the structure. The results reported in the paper reveal the capability of the method to detect the damage in real time.

A Feasibility Study on Timber Damage Detection Using Piezoceramic-Transducer-Enabled Active Sensing.

In recent years, piezoelectric-based transducers and technologies have made significant progress towards structural health monitoring and damage evaluation for various metal and concrete structures. Timber is still commonly used as a construction material in practical engineering; however, there is a lack of research on the health monitoring of timber-based structures using piezoelectric-based transducers and methods. This paper conducts a feasibility study on timber damage detection using surface-mounted piezoelectric patches, which enable the stress-wave-based active sensing approach. Typical damage modes in timber frame structures, such as surface cracks and holes, were investigated in this study. In the active sensing approach, one piezoceramic transducer is used as an actuator to generate stress waves, which propagate along the surface of the timber structure, and other piezoceramic transducers function as sensors to detect the propagating stress waves. Defects, such as a crack or a hole, induce additional attenuation to the propagating stress wave. Based on this attenuation, the proposed method can detect the defects using the wavelet-packet-based damage index, demonstrating its implementation potential for real-time timber damage detection.

Wireless and real-time structural damage detection: A novel decentralized method for wireless sensor networks

Being an alternative to conventional wired sensors, wireless sensor networks (WSNs) are extensively used in Structural Health Monitoring (SHM) applications. Most of the Structural Damage Detection (SDD) approaches available in the SHM literature are centralized as they require transferring data from all sensors within the network to a single processing unit to evaluate the structural condition. These methods are found predominantly feasible for wired SHM systems; however, transmission and synchronization of huge data sets in WSNs has been found to be arduous. As such, the application of centralized methods with WSNs has been a challenge for engineers. In this paper, the authors are presenting a novel application of 1D Convolutional Neural Networks (1D CNNs) on WSNs for SDD purposes. The SDD is successfully performed completely wireless and real-time under ambient conditions. As a result of this, a decentralized damage detection method suitable for wireless SHM systems is proposed. The proposed method is based on 1D CNNs and it involves training an individual 1D CNN for each wireless sensor in the network in a format where each CNN is assigned to process the locally-available data only, eliminating the need for data transmission and synchronization. The proposed damage detection method operates directly on the raw ambient vibration condition signals without any filtering or preprocessing. Moreover, the proposed approach requires minimal computational time and power since 1D CNNs merge both feature extraction and classification tasks into a single learning block. This ability is prevailingly cost-effective and evidently practical in WSNs considering the hardware systems have been occasionally reported to suffer from limited power supply in these networks. To display the capability and verify the success of the proposed method, large-scale experiments conducted on a laboratory structure equipped with a state-of-the-art WSN are reported.

Live crack damage detection with local strain measurement on solid bodies subjected to hydrodynamic loading

The interaction of water free surface with solid bodies is object of interest in several mechanical, ocean, aerospace and civil engineering problems. The presence of impulsive loading and large local deformation leads to complex coupled dynamics. The possibility of live monitoring of these body could provide information about damage detection and fatigue life estimation. The definition of appropriate signal processing and modeling tools enabling the extraction of useful information from distributed sensing signals is a relevant scientific challenge. On the basis of previous works by some of the authors, this paper deals with the application of a method for real-time deformed shape reconstruction of solid bodies subjected to impulsive loadings using distributed numerically generated strain measurements signals, such as those produced by Fiber Bragg Grating (FBG) sensors. A numerical study is carried out considering a simplified model of the problem of hull structures subjected to hydrodynamic loading. The hull, analyzed in a simplified section, has been studied both in healthy condition and with the presence of crack damages. The potential for detecting, localizing and quantifying this damage using the reconstruction algorithm is investigated, by leveraging the proposed concept of control sensors, that are FBG sensors used for comparing reconstructed strains and/or displacements with measured quantities. The positioning and number of sensors and the effect of sensor layout on damage detection is investigated, with the aim of developing a real time damage detection methodology.

On-line Bayesian model updating for structural health monitoring

Abstract Fatigue induced cracks is a dangerous failure mechanism which affects mechanical components subject to alternating load cycles. System health monitoring should be adopted to identify cracks which can jeopardise the structure. Real-time damage detection may fail in the identification of the cracks due to different sources of uncertainty which have been poorly assessed or even fully neglected. In this paper, a novel efficient and robust procedure is used for the detection of cracks locations and lengths in mechanical components. A Bayesian model updating framework is employed, which allows accounting for relevant sources of uncertainty. The idea underpinning the approach is to identify the most probable crack consistent with the experimental measurements. To tackle the computational cost of the Bayesian approach an emulator is adopted for replacing the computationally costly Finite Element model. To improve the overall robustness of the procedure, different numerical likelihoods, measurement noises and imprecision in the value of model parameters are analysed and their effects quantified. The accuracy of the stochastic updating and the efficiency of the numerical procedure are discussed. An experimental aluminium frame and on a numerical model of a typical car suspension arm are used to demonstrate the applicability of the approach.

Real time damage detection using recursive principal components and time varying auto-regressive modeling

Abstract In this paper, a novel baseline free approach for continuous online damage detection of multi degree of freedom vibrating structures using Recursive Principal Component Analysis (RPCA) in conjunction with Time Varying Auto-Regressive Modeling (TVAR) is proposed. In this method, the acceleration data is used to obtain recursive proper orthogonal components online using rank-one perturbation method, followed by TVAR modeling of the first transformed response, to detect the change in the dynamic behavior of the vibrating system from its pristine state to contiguous linear/non-linear-states that indicate damage. Most of the works available in the literature deal with algorithms that require windowing of the gathered data owing to their data-driven nature which renders them ineffective for online implementation. Algorithms focussed on mathematically consistent recursive techniques in a rigorous theoretical framework of structural damage detection is missing, which motivates the development of the present framework that is amenable for online implementation which could be utilized along with suite experimental and numerical investigations. The RPCA algorithm iterates the eigenvector and eigenvalue estimates for sample covariance matrices and new data point at each successive time instants, using the rank-one perturbation method. TVAR modeling on the principal component explaining maximum variance is utilized and the damage is identified by tracking the TVAR coefficients. This eliminates the need for offline post processing and facilitates online damage detection especially when applied to streaming data without requiring any baseline data. Numerical simulations performed on a 5-dof nonlinear system under white noise excitation and El Centro (also known as 1940 Imperial Valley earthquake) excitation, for different damage scenarios, demonstrate the robustness of the proposed algorithm. The method is further validated on results obtained from case studies involving experiments performed on a cantilever beam subjected to earthquake excitation; a two-storey benchscale model with a TMD and, data from recorded responses of UCLA factor building demonstrate the efficacy of the proposed methodology as an ideal candidate for real time, reference free structural health monitoring.

Corrosion induced damage detection of in-service RC slabs using acoustic emission technique

For early detection of structural deficiencies due to corrosion, real time damage detection systems are needed to be deployed. With regard to this, present paper explores efficacy of established acoustic emission (AE) based mathematical procedure for corrosion assessment of in-service RC slabs. Two RC slabs (undamaged and damaged due to corrosion) were monitored using AE, half-cell potential and concrete resistivity techniques. The results demonstrate that AE data, captured even for shorter duration, can indicate appropriate probability of corrosion. The corrosion probability predicted by AE technique is well validated with that of electrochemical techniques, establishing AE as a powerful technique for corrosion-induced damage detection of in-service structural elements.

Separating damage from environmental effects affecting civil structures for near real-time damage detection

Analyzing changes in vibration properties (e.g. natural frequencies) of structures as a result of damage has been heavily used by researchers for damage detection of civil structures. These changes, however, are not only caused by damage of the structural components, but they are also affected by the varying environmental conditions the structures are faced with, such as the temperature change, which limits the use of most damage detection methods presented in the literature that did not account for these effects. In this article, a damage detection method capable of distinguishing between the effects of damage and of the changing environmental conditions affecting damage sensitivity features is proposed. This method eliminates the need to form the baseline of the undamaged structure using damage sensitivity features obtained from a wide range of environmental conditions, as conventionally has been done, and utilizes features from two extreme and opposite environmental conditions as baselines. To allow near real-time monitoring, subsequent measurements are added one at a time to the baseline to create new data sets. Principal component analysis is then introduced for processing each data set so that patterns can be extracted and damage can be distinguished from environmental effects. The proposed method is tested using a two-dimensional truss structure and validated using measurements from the Z24 Bridge which was monitored for nearly a year, with damage scenarios applied to it near the end of the monitoring period. The results demonstrate the robustness of the proposed method for damage detection under changing environmental conditions. The method also works despite the nonlinear effects produced by environmental conditions on damage sensitivity features. Moreover, since each measurement is allowed to be analyzed one at a time, near real-time monitoring is possible. Damage progression can also be given from the method which makes it advantageous for damage evolution monitoring.

Structural health monitoring of cylindrical bodies under impulsive hydrodynamic loading by distributed FBG strain measurements

Various mechanical, ocean, aerospace and civil engineering problems involve solid bodies impacting the water surface and often result in complex coupled dynamics, characterized by impulsive loading conditions, high amplitude vibrations and large local deformations. Monitoring in such problems for purposes such as remaining fatigue life estimation and real time damage detection is a technical and scientific challenge of primary concern in this context. Open issues include the need for developing distributed sensing systems able to operate at very high acquisition frequencies, to be utilized to study rapidly varying strain fields, with high resolution and very low noise, while scientific challenges mostly relate to the definition of appropriate signal processing and modeling tools enabling the extraction of useful information from distributed sensing signals.Building on previous work by some of the authors, we propose an enhanced method for real time deformed shape reconstruction using distributed FBG strain measurements in curved bodies subjected to impulsive loading and we establish a new framework for applying this method for structural health monitoring purposes, as the main focus of the work. Experiments are carried out on a cylinder impacting the water at various speeds, proving improved performance in displacement reconstruction of the enhanced method compared to its previous version. A numerical study is then carried out considering the same physical problem with different delamination damages affecting the body. The potential for detecting, localizing and quantifying this damage using the reconstruction algorithm is thoroughly investigated. Overall, the results presented in the paper show the potential of distributed FBG strain measurements for real time structural health monitoring of curved bodies under impulsive hydrodynamic loading, defining damage sensitive features in terms of strain or displacement reconstruction errors at selected locations along the structure.

Structural health monitoring on an unmanned aerial vehicle wing's beam based on fiber Bragg gratings and pattern recognition techniques

Composite materials have been extensively used on new aircraft airframes because of their advantages over metallic materials. This represents a difficulty for damage detection, a vital task for safety on the aerospace industry, as most nondestructive testing techniques are not effective on these materials since those usually present internal failures like delaminations which are difficult to detect. A miniaturized strain acquisition and wireless transmission system is presented alongside with a novel technique for structural behavior assessment, based on the use of Fiber Bragg Gratings to measure strains and non-supervised classification techniques to recognize different operational conditions. Operational tests were performed on an Unmanned Aerial Vehicles wing's beam, made of composite materials with the sensors embedded during its manufacturing. Strain measurements were processed using an Optimal Baseline Selection methodology. The tests performed proved the system's capability to identify and separate different operational conditions for a healthy structure, based on the analysis of its strain fields. The implementation of this methodologies can lead to perform real-time damage detection on aerospace complex structures made of composite materials.

Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks

Structural health monitoring (SHM) and vibration-based structural damage detection have been a continuous interest for civil, mechanical and aerospace engineers over the decades. Early and meticulous damage detection has always been one of the principal objectives of SHM applications. The performance of a classical damage detection system predominantly depends on the choice of the features and the classifier. While the fixed and hand-crafted features may either be a sub-optimal choice for a particular structure or fail to achieve the same level of performance on another structure, they usually require a large computation power which may hinder their usage for real-time structural damage detection. This paper presents a novel, fast and accurate structural damage detection system using 1D Convolutional Neural Networks (CNNs) that has an inherent adaptive design to fuse both feature extraction and classification blocks into a single and compact learning body. The proposed method performs vibration-based damage detection and localization of the damage in real-time. The advantage of this approach is its ability to extract optimal damage-sensitive features automatically from the raw acceleration signals. Large-scale experiments conducted on a grandstand simulator revealed an outstanding performance and verified the computational efficiency of the proposed real-time damage detection method.

Structural health monitoring and damage identification for composite panels using smart sensor

Real-time monitoring of structural integrity is an important challenge. This article presents the results of damage detection in real time for two materials: Al 6061-T6 and twill weave carbon fibre-reinforced epoxy composite. The natural frequency as a global dynamic technique was adopted and the structure was evaluated based on the change in the natural frequency. A square thin plate with simply supported edges was investigated under the effect of sinusoidal signal which was generated via mechanical vibration exciter to carry out the natural frequency of the panel. A smart sensor (piezoelectric ceramic lead zirconate titanate) bonded to the surface of the composite panel was used to capture the signals. Experiments demonstrate the effect of change in crack depth and the response of these panels. The results were measured via monitoring technique and evaluated using root mean square deviation index as statistical analysis.

An integrated real-time structural damage detection method based on extended Kalman filter and dynamic statistical process control

Real-time structural parameter identification and damage detection are of great significance for structural health monitoring systems. The extended Kalman filter has been implemented in many structural damage detection methods due to its capability to estimate structural parameters based on online measurement data. Current research assumes constant structural parameters and uses static statistical process control for damage detection. However, structural parameters are typically slow-changing due to variations such as environmental and operational effects. Hence, false alarms may easily be triggered when the data points falling outside of the static statistical process control range due to the environmental and operational effects. In order to overcome this problem, this article presents a novel real-time structural damage detection method by integrating extended Kalman filter and dynamic statistical process control. Based on historical measurements of damage-sensitive parameters in the state-space model, extended Kalman filter is used to provide real-time estimations of these parameters as well as standard derivations in each time step, which are then used to update the control limits for dynamic statistical process control to detect any abnormality in the selected parameters. The numerical validation is performed on both linear and nonlinear structures, considering different damage scenarios. The simulation results demonstrate high detection accuracy rate and light computational costs of the developed extended Kalman filter–dynamic statistical process control damage detection method and the potential for implementation in structural health monitoring systems for in-service civil structures.

Moving-window extended Kalman filter for structural damage detection with unknown process and measurement noises

The extended Kalman filter (EKF), as a popular tool for optimally estimating system state from noisy measurement, has been used successfully in various areas over the past several decades. However, classical EKF has several limitations when applied to structural system identification; thus, researchers have proposed a number of variations for this method. The current study focuses on using EKF for real-time system identification and damage detection in civil structures. An improved EKF approach, called moving-window EKF (MWEKF), is proposed in this paper after a discussion on the problems associated with the application of classical EKF in time-variant systems. The proposed approach uses the moving-window technique to estimate several statistical properties. MWEKF is more robust and adaptive in structural damage detection compared with classical EKF because of the following reasons: (1) it is insensitive to the selection of the initial state vector; (2) it exhibits more accurate system parameter identification; and (3) it is immune to the inaccurate assumption of noise levels because measurement and process noise levels are estimated in this approach. The salient features of MWEKF are illustrated through numerical simulations of time-variant structural systems and an experiment on a three-story steel shear building model. Results demonstrate that MWEKF is a robust and effective tool for system identification and damage detection in civil structures.

Structural-damage detection with big data using parallel computing based on MPSoC

Life of many people and vital social activities depend on good functioning of civil structures such as nuclear power plants, large bridges, pipelines, and others especially during and after high winds, earthquakes or environmental changes. We can recognize the structural damage of existing systems through signals of the stiffness reductions of their components, and changes in their observed displacements under certain load or environmental conditions. It can be formulated as an inverse problem and solved by the learning approach using neural networks, NN. Here, network function is referred as an associative memory device capable of satisfactory diagnostics even in the presence of noisy or incomplete measurements. It is not new idea, however, to obtain, in real time; an advanced warning of the onset of durability/structural problems at a stage when preventative action is possible has emerged creating an exciting new field within civil engineering. It refers to the broad concept of assessing the ongoing in-service performance of structures using a variety of measurement techniques, e.g., global position system—real time kinematic, GPS-RTK, wireless sensor networks, WSN, and others. It leads to, in this case, the so called big data. Big data is a term applied to data sets whose size is beyond the ability of commonly used traditional computer software and hardware to undertake their acquisition, access, and analytics in a reasonable amount of time. To solve this problem, we can use the Hadoop like Batch processing system using a distributed parallel processing paradigm called MapReduce, which like system handles the volume and variety part of big data. “MapReduce” is a programming model of Google and an associated implementation for processing and generating large data sets using a group of machines. Despite continuous data (BigData) originated from GPS-RTK and WSN is not studied in this paper; only one selected data is used to determine technical state of structure in real time. However, bigdata approach is used in this paper to solve the following problems: Firstly, small number of available data for neural network training may lead to strange, not practical solutions, which are beyond the permissible area. To overcome this problem, small available data is supplemented by numerical data generated from integration of Monte Carlo simulation and finite element method—MCSFEM model. Also, the learning approach using neural networks, NN, gives effect which is guaranteed only if this network has, among the various required conditions, a complete set of training data and so good (optimal) architecture of NN. Integration of a Monte-Carlo simulation with NN—MCSNN model in order to find the optimal (global) architecture of NN is selected and formulated to solve this problem. Both MCSFEM model and MCSNN model require expensive computing, which lead to necessity of the use of parallel computing based on MultiProcessorSystem on Chip, MPSoC, architecture. Secondly, the use of MPSoC architecture led to the emergence of multiple NN named net of neural network, NoNN, rather than a single NN. The simple approach “trial and error” for a single neural network is insufficient to determine an optimal architecture of NN from NoNN. To deal with this problem we develop, in this paper, a new distributed parallel processing using the “Master-Slaver” structure, in the “MapReduce” framework rather than the Google’s Hadoop like Batch processing system using a distributed parallel processing paradigm called MapReduce. Our method is based on the “single instruction multiple data”, SIMD, technology using computer with multiprocessor systems on a chip (or computer with multiple cores, CPUs) without necessarily Google’s computer program and a group of machines. It is used for both MCSFEM model and MCSNN model in order to generate virtual dataset and to find out the optimal architecture of NN from NoNN. It enables us to create quickly many numerical data and eighty thousand architectures (using computer with eight CPUs) can be created instead of eight thousand (using computer with one CPU) only in the same computation times. Effect obtained is presented in the numerical example for the local damage detection in real time of the plane steel truss structures so that one can react quickly when problems appear or to predict new trends in the near future.

Real-time fast damage detection of shear structures with random base excitation

This paper validates the use of two jerk energy-based damage localization methods, proposed for the regular test with pulse excitation in earlier work, in real-time damage detection of shear structures with random base excitation. First, a 20-story shear structure numerical model is considered to provide numerical validation with random base excitation. Then, performance of the two methods in damage detection is experimentally validated through a laboratory-scale 6-story shear structure model. Both experimental and numerical results of single and multiple damage cases with different levels of stiffness loss indicate that the two jerk energy-based methods can also be used in real-time damage detection of shear structures with random base excitation. The two jerk energy-based methods perform well in the presence of high noise level; moreover, they are model-free that avoid establishing the structural finite element model and model updating.

Structural Health Monitoring challenges on the 10-MW offshore wind turbine model

The real-time structural damage detection on large slender structures has one of its main application on offshore Horizontal Axis Wind Turbines (HAWT). The renewable energy market is continuously pushing the wind turbine sizes and performances. This is the reason why nowadays offshore wind turbines concepts are going toward a 10 MW reference wind turbine model. The aim of the work is to perform operational analyses on the 10-MW reference wind turbine finite element model using an aeroelastic code in order to obtain long-time-low- cost simulations. The aeroelastic code allows simulating the damages in several ways: by reducing the edgewise/flapwise blades stiffness, by adding lumped masses or considering a progressive mass addiction (i.e. ice on the blades). The damage detection is then performed by means of Operational Modal Analysis (OMA) techniques. Virtual accelerometers are placed in order to simulate real measurements and to estimate the modal parameters. The feasibility of a robust damage detection on the model has been performed on the HAWT model in parked conditions. The situation is much more complicated in case of operating wind turbines because the time periodicity of the structure need to be taken into account. Several algorithms have been implemented and tested in the simulation environment. They are needed in order to carry on a damage detection simulation campaign and develop a feasible real-time damage detection method. In addition to these algorithms, harmonic removal tools are needed in order to dispose of the harmonics due to the rotation.

Multiscale modeling of sensory properties of Co–Ni–Al shape memory particles embedded in an Al metal matrix

The concept of utilizing ferromagnetic shape memory alloys as embedded sensory particles in aluminum alloys for damage detection is discussed. When embedded in a material, a shape memory particle can undergo an acoustically detectable solid-state phase transformation when the local strain reaches a critical value. The emitted acoustic signal can be used for real-time damage detection. To study the transition behavior of the sensory particle inside a metal matrix under load, a simulation approach based on a coupled atomistic-continuum model is used. The simulation results indicate a strong dependence of the particle’s pseudoelastic response on its crystallographic orientation with respect to the loading direction. These results serve as a basis for understanding the efficacy and variability in the sensory particle transformation to detect damage processes.

Real-time damage detection of RC columns from their acceleration responses by estimating dominant Lipschitz-Hölder exponent

Real-time damage detection of RC columns from their acceleration responses by estimating dominant Lipschitz-Hölder exponent

SnowFort: An Open Source Wireless Sensor Network for Data Analytics in Infrastructure and Environmental Monitoring

SnowFort, an open source wireless sensor network (WSN) for data analytics, is proposed for monitoring infrastructure and environment. The wireless sensing unit is optimized to be low power for extremely long-term deployments. Several features, such as data compression and online reconfiguration, are introduced to further reduce power consumption. A low-power WSN over optimized time division multiple access scheme is designed to be scalable and reliable for a network with hundreds of sensors. Real-time data visualization and analytical tools are provided with a representational state transfer (RESTful) application programming interface. We utilize SnowFort to develop a real-time damage detection application in structural health monitoring. We develop a distributed algorithm robust to data loss and validate it in a laboratory setup.