Vibration-based Damage Detection Techniques Research Articles

A novel damage index for crack identification of a drilling riser during deployment based on the modal vibration energy flow

Early detection of structural damage especially small cracks in marine deepwater drilling risers is of paramount importance. Small cracks are always hard to detect because of weak vibration signals if using traditional damage indices, such as natural frequencies and mode shapes including modal displacements, cross-section slopes, and curvatures. Especially when the deepwater riser is during deployment, the variable suspension riser length and strong sea environment noise make the small crack identification more difficult. To address this problem, this study formulates a new concept of damage detection by integrating the vibration energy flow theory into the vibration-based damage detection (VDD) technique. A novel damage-sensitive feature (DSF) named modal vibration energy flow (MVEF) is established for detecting and locating the small cracks in deepwater drilling risers. The capability of the approach is verified by using the same riser model and the numerical results in the literature. The sensitivities versus crack positions and depths, variable suspension riser length, and sea noise are studied in detail. Results show that the proposed index MVEF is more sensitive than the classic traditional DSFs, i.e., modal slope, modal strain (or curvature), and modal strain energy. In total, the MVEF approach can accurately identify the small cracks and is robust against noise interference, suitable for detecting and locating small cracks in the deepwater drilling riser during deployment.

A novel index for vibration-based damage detection technique in laminated composite plates under forced vibrations: experimental study

In this study, vibration modal analysis has been used to experimentally investigate the detection of fiber breakage (FB) and delamination in laminated composite plates. The modal analysis was performed on two arrangement styles of carbon fiber reinforced polymer (CFRP) laminated plates under forced vibrations. The main contribution in this study is that a new index named Improved Curvature Damage Factor (ICDF) was introduced to detect and localize damages in composite structures. It is a developed version of an earlier index based on the change in mode shape curvature (MSC) of dynamic response. To include the effect of more than one mode shape Cumulative Improved Damage Factor (CICDF) was proposed. The ICDF index was validated with a number of earlier studies that investigated the damage detection in different structures in order to approve the efficiency of this index. A scanning laser vibrometer was also used to experimentally examine the modal-based and forced responses for intact and damaged plates. Various experimental tests were used to verify the advantages of the proposed methodology and its use in structural health monitoring. In addition, the analysis of experimental results showed that the mutation caused by FB is almost double that of the value of delamination peaking.

Experimental Investigation on Damage Sensitive Dynamic Responses of a Reinforced Concrete Beam

Reinforced concrete structures are subjected to damage due to environmental loading and operational conditions. Early detection of damages in reinforced concrete structures is very important. However, this becomes difficult because failure at the micro level in the form of minute cracks, develops much earlier than the visual appearance of actual damages resulting out of coalesce of several such micro level damages. Vibration based damage detection techniques, particularly use of modal testing by exciting a structure dynamically and measuring resulting responses is well established in current practice. Still, real experimental investigation involving a full scale reinforced concrete structure is somewhat rare in current literature. In the present investigation, dynamic responses of a 3.3-metre long reinforced concrete beam were measured experimentally before and after damage. Damage in the form of flexural cracks was inflicted by applying quasi-static load using a universal testing machine of capacity 300 kN under the four-point bending configuration. Broadband roving impact excitation was imparted through an impact hammer and the resulting responses were picked up by a single accelerometer. The time signals of both the force and acceleration responses were Fourier transformed using a spectrum analyser to determine the frequency response functions. The modal parameters e.g. frequencies, mode shapes, modal damping factors are found out through curve fitting. The frequency response function at a particular point has also been investigated for all the load increments and gradual changes into the dynamic properties are noted. Comparison of modal parameters between the undamaged and damaged state including their curvatures indicates that they are sensitive to the crack locations. On the other hand, the differences in frequency response functions including their curvatures were sensitive to the damage intensity in turn. The current experimental investigation provides great insight into the application of vibration-based damage detection technique to reinforced concrete structures.

Analytical and theoretical study of vibration-based damage detection technique in a composite structure

Analytical and theoretical study of vibration-based damage detection technique in a composite structure

Analytical and theoretical study of vibration-based damage detection technique in a composite structure

Analytical and theoretical study of vibration-based damage detection technique in a composite structure

A State-of-the-Art Review on FRF-Based Structural Damage Detection: Development in Last Two Decades and Way Forward

Vibration-based damage detection techniques receive wide attention of the research community in recent years to overcome the limitations of conventional structural health monitoring methods. The modal parameters, namely, natural frequencies, mode shapes, transmissibility, frequency response function (FRF), and other damage sensitive features are usually employed to identify damage in a structure. The main objective of this review is to generate a detailed understanding of FRF-based techniques and to study their performance in terms of advantage, accuracy, and limitations in structural damage detection. This paper also reviews various approaches to develop methodologies in terms of efficiency and computational time. The study observed that excitation frequency, location of application of excitation, type of sensor, number of measurement locations, noise contamination in FRF data, selection of frequency range for simulation, weighting and numerical techniques to solve the over-determined set of equations influence the effectiveness of damage identification procedure. Limitations and future prospects have also been addressed in this paper. The content of this paper aims to guide researchers in developing formulations, updating models, and improving results in the field of FRF-based damage identification.

Damage assessment in hyperbolic cooling towers using mode shape curvature and artificial neural networks

Hyperbolic cooling towers are large thin shell reinforced concrete structures that are used to remove the heat from wastewater and transfer it to the atmosphere using the process of evaporation. During its long service life, a cooling tower can experience damage due to the large temperature variations, environmental degradation, or random actions such as impacts or earthquakes. Such a damage can develop over time and result in the sudden collapse of the cooling tower. To ensure that a cooling tower operates safely and efficiently at all times, it is important to monitor its structural health. In this context, structural health monitoring based on the vibration characteristics of the structure, has emerged as a useful method to detect and locate damage in structures. Hyperbolic cooling towers, due to their particular shape, exhibit rather complex vibration characteristics that do not suit the traditional vibration-based damage detection techniques. This paper develops and applies a damage assessment method using the absolute changes in mode shape curvature (ACMSC) in conjunction with Artificial Neural Networks (ANNs) to detect, locate, and quantify damage in hyperbolic cooling towers. ANN is a machine learning technique that can predict behavioural patterns using a set of data samples and finds use in the damage quantification process. The proposed method for detecting and locating damage is experimentally validated and demonstrated its capability to accurately detect and locate damage. A feed-forward network having one hidden layer with Bayesian algorithm is used to train the artificial neural network. Damage indices calculated from noise polluted mode shape data are used to train the network. The trained network is then used to successfully assess the unknown damage severities in the cooling tower. The outcomes of this paper will enable early warning of damages in the cooling towers and will help towards their safe operation.

Automated Structural Damage Identification Using Data Normalization and 1-Dimensional Convolutional Neural Network

In the field of structural-health monitoring, vibration-based structural damage detection techniques have been practically implemented in recent decades for structural condition assessment. With the development of deep-learning networks that make automatic feature extraction and high classification accuracy possible, deep-learning-based structural damage detection has been gaining significant attention. The deep-learning neural networks come with fixed input and output size, and input data must be downsampled or cropped to the predetermined input size of the networks to obtain desired output of the network. However, the length of input data (i.e., sensing data) is associated with the excitation quality of a structure, adjusting the size of the input data while maintaining the excitation quality is critical to ensure high accuracy of the deep-learning-based structural damage detection. To address this issue, natural-excitation-technique-based data normalization and the use of 1-D convolutional neural networks for automated structural damage detection are presented. The presented approach converts input data to predetermined size using cross-correlation and uses convolutional network to extract damage-sensitive feature for automated structural damage identification. Numerical simulations were conducted on a simply supported beam model excited by random and traffic loadings, and the performance was validated under various scenarios. The proposed method successfully detected the location of damage on a beam under random and traffic loadings with accuracies of 99.90% and 99.20%, respectively.

Dynamic Analysis of Damaged Carbon Nanotubes Micro-Beam Structures

In this article, the intact and damaged multi-walled carbon nanotubes (MWCNTs) models were performed in micro-beam structures to detect the damaged parts. To that end, numerical analysis using ABAQUS software was implemented to achieve free vibration including the calculating of natural frequencies and normalized mode shapes. Vibration-based damage detection techniques were proposed to assess and localize the damaged parts. Within this study, the damage was presented in terms of reducing the local stiffness at 10%, 20%, and 30% Ec at each location. Then to accomplish the detection task, the irregularity of the higher derivative index was calculated. According to the computed results, the peak of the irregularity index precisely shows the effect of the defect, although it was unseen in the mode shape.

Temperature Changes Effect on the Inner Product Vector Method and its Application to Structural Health Monitoring of Aircraft Industry

The purpose of the paper is to study the effect of temperature change on the theory of inner product vector (IPV). The IPV method can be used to detect structural damage. This study evaluates the IPV method ability to detect damage of an Airbus A320 slat-track, which is in the form of a longitudinal crack. The results show that the IPV method is able to detect defects in the structure as well as its location, with close approximation. Then, the Airbus A320 slat-track was investigated for the effect of changes in temperature on the IPV method, evaluated over a temperature between –73 and 260 °C. The effect of temperature on the performance of IPV damage detection method has not been investigated so far. The results of the IPV method show a spurious defect in the structure as the temperature changes; therefore, the IPV method is temperature-sensitive. Also, this study highlighted the importance of applying simulation methods to develop vibration-based damage detection (VBDD) techniques, especially for evaluating the effect of changes in environmental temperature when the structure is complex.

One-dimensional convolutional neural network for damage detection of jacket-type offshore platforms

Vibration-based damage detection techniques play an important role in health monitoring of offshore structures. This study explores the possibility to use the one-dimensional convolutional neural network (CNN) to extract the damage sensitive features automatically from the raw strain response data of a structure under a certain excitation without requiring any hand-crafted feature extraction. The validity of the proposed method is verified by using a numerical simulation of a jacket-type offshore platform under a regular and a random wave excitations in different directions, respectively. The damage localization and damage severity identification are conducted with considering several damage cases including different damage locations and the effect of noise. The data preprocessing procedure based on convolution and deconvolution for noisy data is proposed to enhance the capability of feature extraction and noise immunity of CNN. Furthermore, the experimental studies of a jacket-type offshore platform model subjected to a sinusoidal excitation, a white noise excitation and an impulse excitation are respectively investigated to check the applicability of the method, in which the major damage and minor damage on single and multiple locations are involved. Results indicate this approach has an excellent performance on structural damage detection.

VIBRATION-BASED STRUCTURAL DAMAGE DETECTION TECHNIQUES: A REVIEW

Damage in infrastructure can be as a result of its degenerating state under service loads or after exposure to impact loads such as earthquakes. Early damage detection is essential to preventing failure and ensure the integrity and safety of structures. Damages lead to changes in the geometric and material properties like mass, stiffness, and damping, and influences the response behavior of the structure. It has been proven that vibration-based damage detection technique is an efficient means of damage identification and assessing structural integrity. This review article examines conventional vibration-based damage detection techniques. It highlights the importance of early damage detection as a means of ensuring infrastructural safety, reliability and maintenance. Damage detection techniques like the time domain methods, frequency domain and modal domain methods have been developed and constantly evolving to meet the existing challenge of identifying structural damages. The practical application is still minimal, hence more research works are necessary for damage detection in large civil engineering structures.

Sparse Bayesian learning for structural damage detection under varying temperature conditions

Structural damage detection inevitably entails uncertainties, such as measurement noise and modelling errors. The existence of uncertainties may cause incorrect damage detection results. In addition, varying environmental conditions, especially temperature, may have a more significant effect on structural responses than structural damage does. Neglecting the temperature effects may make reliable damage detection difficult. In this study, a new vibration based damage detection technique that simultaneously considers the uncertainties and varying temperature conditions is developed in the sparse Bayesian learning framework. The structural vibration properties are treated as the function of both the damage parameter and varying temperature. The temperature effects on the vibration properties are incorporated into the Bayesian model updating on the basis of the quantitative relation between temperature and natural frequencies. The structural damage parameter and associated hyper-parameters are then solved through the iterative expectation–maximization technique. An experimental frame is utilized to demonstrate the effectiveness of the proposed damage detection method. The sparse damage is located and quantified correctly by considering the varying temperature conditions.

Experimental damage assessment of support condition for plate structures using wavelet transform

Wavelet transforms (WTs) have gained popularity among researchers as tools for identifying damage in structures using vibration-based damage detection (VBDD) techniques, owing to their ability to identify singularities by decomposing mode shapes structural responses of the structure. In VBDD, the support condition of a structure influences the structural responses and modal properties. In fact, structural responses and modal properties are a lot more sensitive to changing boundary conditions than to crack and fatigue damage, resulting in inaccurate damage detection results. Therefore, in this study, sensitivity tests to estimate a suitable distance range which allows damage detection by imposing single support damage were carried out. The estimated appropriate distance was then applied to detect damage at multiple supports. This involved the applicability of response acceleration of plate structures to support assessment by applying continuous wavelet transform (CWT) and discrete wavelet transform (DWT). The damage cases were introduced by releasing bolts at the specified fixed supports of the plate to simulate the damage. The response accelerations of the rectangular plate at points close to the supports were measured and decomposed using CWT and DWT to assess the structural integrity of each support. The results showed that an appropriate distance range is necessary for accurate damage detection, and both, CWT and DWT can provide reliable outputs. However, the first- and fourth-level detail coefficients of DWT fail to indicate damage in some cases. A more detailed investigation of the effect of different wavelet scale ranges on damage detection using CWT demonstrated that the accuracy of damage detection increases as the scale decreases.

A novel vibration based breathing crack localization technique using a single sensor measurement

Abstract Structural damages such as a fatigue-breathing crack can result in nonlinear dynamical signatures that can significantly enhance their detection. Majority of the existing vibration based breathing crack diagnosis techniques demands rather a dense sensor network in order to detect, precisely locate the spatial location and characterize the breathing crack as the change in the dynamic characteristics of a structure with breathing crack is much smaller when compared to open cracks of the same magnitude. Further, the quality of identification improves with the increase in the number of sensors. In this paper, a novel vibration-based damage detection technique based on the zero strain energy nodes concept is proposed for the first time to identify the exact spatial location of the breathing crack using a single sensor measurement. Two different procedures based on sweep sine and harmonic excitation are outlined for breathing crack localization. Multi-level Singular Spectrum Analysis (M-SSA) is used in the present work to conclude about the presence of nonlinear behaviour of the structure and also for the identification of frequencies at which the cracked structure behaves linearly. Numerical and experimental investigations presented in this paper clearly establish that the proposed approach has the ability to identify single and as well as multiple breathing cracks present anywhere in the structure even with noise contaminated measurements.

Vibration-Based Damage Detection Techniques for Health Monitoring of Construction of a Multi-Storey Building

Conducting surveys of multi-storey buildings is a laborious task, because large volumes of visual and instrumental research should be carried out. Reduction of labor costs with an increase in the reliability of information about the state of damage and technical condition is an actual scientific and practical task. One of the ways to solve it is to use non-destructive vibration diagnostic methods. The purpose of carrying out diagnostics with the use of vibration based damage detection methods is to search for damages in structural elements that can cause the deviation of the dynamic parameters of a structure from calculated ones. Determination of the dynamic parameters of the structure, in particular natural frequencies and mode shapes of mechanical systems, is one of the most important tasks that allows obtaining integral information about the state of a structure. This article presents the results of calculations for the localization of slabs defects in a multi-storey building with a transverse crack, span L = 4.5 (m), height H = 0.2 (m), with prestressed reinforcement d = 0.05 (m). Vibration based Damage Index method was used to localize the defect. During the study, reliable localization values of the defect area of the slab were obtained, this indicates that the vibration method for determining the damage index with a sufficient degree of accuracy allowed predicting the site of damage to the structure.

Damage detection of rotating wind turbine blades using local flexibility method and long-gauge fiber Bragg grating sensors

Wind turbines are a cost-effective alternative energy source; however, their blades are susceptible to damage. Therefore, damage detection of wind turbine blades is of great importance for condition monitoring of wind turbines. Many vibration-based structural damage detection techniques have been proposed in the last two decades. The local flexibility method, which can determine local stiffness variations of beam-like structures by using measured modal parameters, is one of the most promising vibration-based approaches. The local flexibility method does not require a finite element model of the structure. A few structural modal parameters identified from the ambient vibration signals both before and after damage are required for this method. In this study, we propose a damage detection approach for rotating wind turbine blades using the local flexibility method based on the dynamic macro-strain signals measured by long-gauge fiber Bragg grating (FBG)-based sensors. A small wind turbine structure was constructed and excited using a shaking table to generate vibration signals. The structure was designed to have natural frequencies as close as possible to those of a typical 1.5 MW wind turbine in real scale. The optical fiber signal of the rotating blades was transmitted to the data acquisition system through a rotary joint fixed inside the hollow shaft of the wind turbine. Reversible damage was simulated by aluminum plates attached to some sections of the wind turbine blades. The damaged locations of the rotating blades were successfully detected using the proposed approach, with the extent of damage somewhat over-estimated. Nevertheless, although the specimen of wind turbine blades cannot represent a real one, the results still manifest that FBG-based macro-strain measurement has potential to be employed to obtain the modal parameters of the rotating wind turbines and then locations of wind turbine segments with a change of rigidity can be estimated effectively by utilizing these identified parameters.

Strain sensing of beams in flexural vibrations using rotation rate sensors

This study investigated the application of rotation rate sensors to measure flexural vibrations of beams. This new area of the application of rotational sensors can be beneficial for monitoring various structures, particularly civil engineering structures like reinforced concrete beams and frames and masonry structures, which are difficult to assess using vibration-based damage detection techniques. In a small-scale laboratory experiment, Horizon HZ1-100-100 rotation rate sensors were put in the middle of a plexiglass cantilever beam along with four strain gauges. The beam underwent kinematic excitations, during which variations of the rotations and strains are observed simultaneously. Results of the tests are presented in detail and it was concluded that the rotation rate sensors can be effectively used to monitor flexural vibrations of beams, with particular attention to indirect strain monitoring. In addition, a simple test to check the effectiveness of the rotation rate sensors to monitor stiffness drops in localized locations was carried out. The experiments demonstrated that rotation rate sensors have potential to dramatically improve SHM of civil engineering structures and with improved accuracy, these sensors can find wide application in on-line and post-earthquake structural assessment.

Optimization the inner product vector method and its application to structural health monitoring

Fast methods to identification and recognition of structural defects are important issues for the industry. In recent years, a growing interest has been on quick structural inspection to significantly reduce inspection’s cost and time, while minimizing the number of Non-Destructive Testing (NDT). Investigations on damage detection methods based on vibration monitoring have been developed in the last decade. This paper presents a study on Inner Product Vector (IPV) method. The IPV method is a new vibration based damage detection technique. In this method vibration responses are measured before and after damage occurrence. The vibration responses include the time domain acceleration (or displacement or velocity). The IPV method has the potential to significantly reduce costs by minimizing the need for NDT methods. For damage detection via the IPV method, a threshold should be selected for classifying the damaged and undamaged sections of a structure. Proper determination of the threshold value requires selection of Confidence Interval Factor (CIF). In this study, a new algorithm for the IPV method is suggested in which a new optimized model for damage detection is presented. Aforementioned optimized model can provide an accurate value for the CIF. To ascertain the exact CIF, the damage detection method is simulated. An accurate threshold makes the IPV method capable to accurately detect damages. The method has been verified by means of an FEM model as well as an experimental case study. The results show that the optimization process can be used as a reference to ascertain value for the CIF. The IPV method can be used as a Structural Health Monitoring (SHM) method, but it’s necessary to optimize the IPV method for each sample.

Bridge Damage Detection Based on Vibration Data: Past and New Developments

Overtime, the structural condition of bridges tends to decline due to a number of degradation processes, such as creep, corrosion and cyclic loading, among others. Considerable research has been conducted over the years to assess and monitor the rate of such degradation with the aim of reducing structural uncertainty. Traditionally, vibration-based damage detection techniques in bridges have focused on monitoring changes to modal parameters and subsequently comparing them to numerical models. These traditional techniques are generally time consuming and can often mistake changing environmental and operational conditions as structural damage. Recent research has seen the emergence of more advanced computational techniques that not only allow the assessment of noisier and more complex data, but also allow research to veer away from monitoring changes in modal parameters alone. This paper presents a review of the current state-of-the-art developments in vibration based damage detection in small to medium span bridges with particular focus on the utilization of advanced computational methods that avoid traditional damage detection pitfalls. A case study of the S101 Bridge is also presented to test the damage sensitivity a chosen methodology.