Vibration-based Structural Health Monitoring Research Articles

ICD: A methodology for real time onset detection of overlapped acoustic emission waves

Accurate time of arrival (TOA) detection is essential for successful wave and vibration-based structural health monitoring (SHM) system including Acoustic Emission (AE). AE is the foremost among passive wave-based monitoring systems. During high burst rate results, AE waves could overlap. Overlapping of AE waves leads to overlap in both time and frequency. As a result, the TOA information is lost in the coda of the previous wave and cannot be (reliably) detected by conventional techniques. To this end, ICD is introduced. ICD involves 3 cascaded systems a) (Overlapping) Identification b) (Overlap) Cleaning c) (Arrival) Detection. A novel 3D Fingerprint is meticulously designed to autogenously and injectively identify the overlapping waves. Positive identification activates the cleaning system which eradicates the influence of the Intersecting Wave using newly proposed adaptive spectral subtraction (ASpS). Then, TOA for the Intersected Wave was identified. The approaches were verified in controlled as well as source localization tests. The results from controlled study validate robustness in low IRR values for a wide range of waveform parameters. Finally, source localization results from laboratory testing confidently display the scientific applicability of the proposed system. This system could enhance detectability, reliability, and accuracy of a conventional system.

Machine Learning Algorithms in Civil Structural Health Monitoring: A Systematic Review

Applications of Machine Learning (ML) algorithms in Structural Health Monitoring (SHM) have become of great interest in recent years owing to their superior ability to detect damage and deficiencies in civil engineering structures. With the advent of the Internet of Things, big data and the colossal and complex backlog of aging civil infrastructure assets, such applications will increase very rapidly. ML can efficiently perform several analyses of clustering, regression and classification of damage in diverse structures, including bridges, buildings, dams, tunnels, wind turbines, etc. In this systematic review, the diverse ML algorithms used in this domain have been classified into two major subfields: vibration-based SHM and image-based SHM. The efficacy of deploying ML algorithms in SHM has been discussed and detailed critical analysis of ML applications in SHM has been provided. Accordingly, practical recommendations have been made and current knowledge gaps and future research needs have been outlined.

Special Feature Vibration-Based Structural Health Monitoring

Structural health monitoring by vibration requires the understanding of multidisciplinary fields of engineering sciences [...]

Damage Detection and Condition Monitoring of Pre-stressed Concrete Bridges by using Vibrationbased Health Monitoring Techniques

Damage detection plays a vital role in bridges as the structural performance of the bridges decreases progressively throughout their service life due to many deterioration processes. Although damage detection is performed mainly through periodic visual inspection, due to its immense drawbacks, researchers have focused increasing attention to the potential of using vibrationbased structural health monitoring (V-SHM) techniques to detect damages and monitoring conditions of the structures. The fundamental theory of the V-SHM is that structural damages will change mass, stiffness and damping properties of structures leading to detectable changes in their dynamic characteristics such as natural frequencies, mode shapes and modal damping ratios. The applicability of V-SHM techniques to detect the existence of damage in pre-stressed concrete bridges by experimentally identifying their dynamic characteristics is reported in this study. Eigensystem Realization Algorithm (ERA) was used for experimental modal analysis of the bridges by using the free vibration records extracted from field vibration measurements. Numerical modal analysis for the bridges was performed using finite element models of the bridges to obtain the dynamic characteristics of the intact structures. The identified dynamic characteristics from both experimental and numerical approaches were used in obtaining an indication of the possible level of existence of damages in studied pre-stressed concrete bridges. Three global damage identification (DI) criterions are defined and evaluated to further corroborate that these bridges have not undergone appreciable damages, as the evaluated DI criterions are within acceptable tolerances for no damage state, accommodating noisy data.

Research on sampling rate selection of sensors in offshore platform shm based on vibration

In vibration-based structural health monitoring (SHM), if the data are collected at a higher sampling rate from sensors, a large amount of computing resources and space resources will be consumed. For offshore platforms with limited space resources, there are many difficulties in long-term continuous monitoring. Aiming at this problem, the influence of sampling rate on structural damage identification is studied by comparing and analyzing the acceleration and posture sensors commonly used in offshore platform monitoring. In the simulation of parameter change of nonlinear system, random decrements and their spectrum characteristics are extracted from acceleration and displacement data respectively under different sampling rates, and the characteristics are classified by support vector machine (SVM) to identify the rule of change. The research of model experiment data also shows that the low sampling rate data can realize the identification of the damage, and the recognition degree of the low sampling rate data based on posture is not lower than that of the acceleration. The method is applied to the monitoring data analysis of FPSO soft york single-point mooring system and the damage situation is identical with the actual situation. The results show that the structural damage can be reflected from the low sampling rate posture monitoring data.

Bio-inspired flexible vibration visualization sensor based on piezo-electrochromic effect

Monitoring structural vibration can provide quantitative information for both structural health evaluations and early-warning maintenance. The most classic vibration-based structural health monitoring is equipped with piezoelectric accelerometers, which is expensive and inconvenient due to cumbersome and time-consuming sensor installation and high power-consumptive data acquisition systems. One other main challenge with these systems is the inherent limitations for multi-point monitoring in critical elements with curvature due to their non-conformability. Here, inspired by the chameleon, we report a cost-effective, flexible and conformal, self-powered vibration sensor based on elasto-electro-chemical synergistic effect of piezoelectricity and electrochromism. The sensor can provide not only in-situ visualization, but also ex-situ recording of structural vibration due to the non-volatile color memory effect of electrochromism. The passive sensor system is composed of two distinct electronic components — ternary Pb(In1/2Nb1/2)O3Pb(Mg1/3Nb2/3)O3PbTiO3 piezoelectric single crystal ribbon sensors and a solid-state tungsten trioxide electrochromic indicator driven by vibration-induced voltage generated by the piezoelectric ribbons. The proposed piezo-electrochromic based passive non-volatile visualization sensor may find diverse applications in structural health monitoring, smart wallpapers, and medical injury rehabilitation.

A validated robust and automatic procedure for vibration analysis of bridge structures using MEMS accelerometers

Abstract Today, short- and long-term structural health monitoring (SHM) of bridge infrastructures and their safe, reliable and cost-effective maintenance has received considerable attention. From a surveying or civil engineer’s point of view, vibration-based SHM can be conducted by inspecting the changes in the global dynamic behaviour of a structure, such as natural frequencies (i. e. eigenfrequencies), mode shapes (i. e. eigenforms) and modal damping, which are known as modal parameters. This research work aims to propose a robust and automatic vibration analysis procedure that is so-called robust time domain modal parameter identification (RT-MPI) technique. It is novel in the sense of automatic and reliable identification of initial eigenfrequencies even closely spaced ones as well as robustly and accurately estimating the modal parameters of a bridge structure using low numbers of cost-effective micro-electro-mechanical systems (MEMS) accelerometers. To estimate amplitude, frequency, phase shift and damping ratio coefficients, an observation model consisting of: (1) a damped harmonic oscillation model, (2) an autoregressive model of coloured measurement noise and (3) a stochastic model in the form of the heavy-tailed family of scaled t-distributions is employed and jointly adjusted by means of a generalised expectation maximisation algorithm. Multiple MEMS as part of a geo-sensor network were mounted at different positions of a bridge structure which is precalculated by means of a finite element model (FEM) analysis. At the end, the estimated eigenfrequencies and eigenforms are compared and validated by the estimated parameters obtained from acceleration measurements of high-end accelerometers of type PCB ICP quartz, velocity measurements from a geophone and the FEM analysis. Additionally, the estimated eigenfrequencies and modal damping are compared with a well-known covariance driven stochastic subspace identification approach, which reveals the superiority of our proposed approach. We performed an experiment in two case studies with simulated data and real applications of a footbridge structure and a synthetic bridge. The results show that MEMS accelerometers are suitable for detecting all occurring eigenfrequencies depending on a sampling frequency specified. Moreover, the vibration analysis procedure demonstrates that amplitudes can be estimated in submillimetre range accuracy, frequencies with an accuracy better than 0.1 Hz and damping ratio coefficients with an accuracy better than 0.1 and 0.2 % for modal and system damping, respectively.

MOVA/MOSS: Two integrated software solutions for comprehensive Structural Health Monitoring of structures

Recent ground-breaking advances in sensing technologies, data processing, and structural identification have made Structural Health Monitoring (SHM) occupy a central place in Structural Engineering. Although the technological transfer to the industry is still in the early development stages, there is clear evidence that SHM-enabled condition-based maintenance of structures will soon supersede traditional periodic maintenance strategies. Among the existing solutions, ambient vibration-based SHM has become particularly popular owing to its minimum intrusiveness and global damage assessment capabilities. Nevertheless, it is well documented that local pathologies with limited impact over the stiffness of structures can be hardly detected by such techniques. As a solution, recent studies advocate the use of integrated monitoring systems, where data from heterogeneous sensor networks are simultaneously processed to achieve a comprehensive structural assessment. Despite the great advances of these systems reported by researchers, practitioners still find many difficulties to bring them to practice. In this light, this paper reports the development of two novel software solutions for long-term SHM of structures, MOVA and MOSS, that are intended to bridge this gap while also introducing new methodological and scientific advances. The developed software enables the online system identification and damage detection of structures, including vibration-based SHM and data fusion of heterogeneous sensing systems with an innovative automated anomaly detection algorithm. A case study of a permanent static/dynamic/environmental monitoring system installed in a monumental masonry palace, the Consoli Palace in Gubbio (Italy), is presented to illustrate the capabilities of MOVA/MOSS.

Review of Vibration-Based Structural Health Monitoring Using Deep Learning

With the rapid progress in the deep learning technology, it is being used for vibration-based structural health monitoring. When the vibration is used for extracting features for system diagnosis, it is important to correlate the measured signal to the current status of the structure. The measured vibration responses show large deviation in spectral and transient characteristics for systems to be monitored. Consequently, the diagnosis using vibration requires complete understanding of the extracted features to discard the influence of surrounding environments or unnecessary variations. The deep-learning-based algorithms are expected to find increasing application in these complex problems due to their flexibility and robustness. This review provides a summary of studies applying machine learning algorithms for fault monitoring. The vibration factors were used to categorize the studies. A brief interpretation of deep neural networks is provided to guide further applications in the structural vibration analysis.

Robust damage detection in the time domain using Bayesian virtual sensing with noise reduction and environmental effect elimination capabilities

Vibration-based structural health monitoring aims at detecting changes in the dynamic characteristics of a structure. A direct time-domain damage detection method is proposed, in which no system identification is needed. In this data-based method, response signals are acquired using a sensor network. Utilizing hardware redundancy, each sensor signal can be estimated from the remaining signals in the network. The residuals, or the differences between the actual and estimated signals are used for damage detection. However, the measurement error affects the detection performance. Therefore, a novel two-step algorithm is proposed. The signal-to-noise ratio of the signals is first increased by developing Bayesian virtual sensors that are proved to be more accurate than the hardware. Virtual sensor data then replace the actual measurements in the subsequent data analysis. In the second step, environmental or operational influences can be eliminated by acquiring training data under different conditions and estimating the signal of each sensor using the remaining virtual sensors in the network. The excitation or the environmental or operational variables are not measured. In this two-step algorithm for damage detection, each sensor's signal is estimated twice. The novelty is how to apply Bayesian virtual sensors to residual generation resulting in enhanced damage detection. Principal component analysis is applied to the residuals and an extreme value statistics control chart is designed for damage detection. Vibrations of a healthy and damaged bridge structure were simulated under random excitation and temperature variability. Virtual sensors outperformed the actual measurements in damage detection making an early warning more plausible.

Vibration-based structural monitoring of bi-clamped metal-composite bonded joint: Experimental and numerical analyses

ABSTRACT The present study verifies the applicability of vibration-based Structural Health Monitoring (SHM) to identify debonding in bi-clamped metal-composite bonded joints, which can be applied in aeronautical structures. The method compares the Frequency Response Functions (FRFs) of the case studies with and without debonding damage. This analysis is done mainly through metrics of damage detection based on a comparison between the magnitude and phase angles of FRFs. The same metrics were also used after the application of a data filtering process. The specimens were manufactured from carbon-fiber-reinforced polymers and titanium and were joined by epoxy resin. Half of the specimens have a piezoelectric transducer (PZT) attached to the titanium adherent. The debonding damage was simulated for the damaged specimens by having 50% of the overlap area covered by a layer of Teflon. Three case studies have been reported: first, it verifies the influence of the PZT on FRFs; second, it evaluates the influence of the debonding damage by using accelerometers, and third, it performs the damage detection analysis by using the electrical responses of the PZT. Numerical and experimental analyses are compared, evaluating limitations and potentialities of the computational models. The results support the applicability of the metrics for damage quantification.

Vibration-based SHM with up-scalable and low-cost Sensor Networks

Structural health monitoring (SHM) is becoming increasingly attractive for its potentialities in many application contexts, such as civil and aeronautical engineering. In these scenarios, modern SHM systems are typically constituted by a multitude of sensor nodes. Such devices should be based on low-cost and low-power solutions both to ease the deployment of progressively denser sensor networks and to be compatible with a permanent installation; this allows real-time monitoring while reducing the global maintenance costs. Among the developed inspection methodologies, operational modal analysis (OMA) is an efficient tool to assess the integrity of vibrating structures. This article describes a sensor network that is based on either microelectromechanical systems (MEMS) accelerometers or cost-effective piezoelectric devices to extract strictly synchronized modal parameters. The performances of the two sensing technologies are evaluated in two different setups, to assess the reliability in the estimation of modal features even in the presence of potential damages. Particular attention was given to the mode shape reconstruction issue from piezoelectric signals, primarily encompassing a purposely developed modal coordinate tuning procedure. Moreover, the consistency of the obtained results paves the way to a more compact and affordable monitoring system exploiting piezoelectric-driven modal analysis.

Four years of Structural Health Monitoring of the San Pietro Bell Tower in Perugia, Italy: two years before the earthquake versus two years after

This paper addresses the structural health monitoring (SHM) of the bell tower of the Basilica of San Pietro in Perugia, Italy, which is located in a seismic area. Known as one of the landmarks of the Umbrian capital, the tower belongs to a monumental complex of exceptional historical and cultural value. Therefore, its protection with respect to earthquakes is an important issue. To this purpose, a vibration-based SHM system able to detect anomalies in the structural behaviour by means of statistical process control tools has been installed in the tower and is under continuous operation since December 2014. The effects of the 2016-2017 Central Italy seismic sequence were clearly detected by this system, even if earthquakes took place at relatively large distance from the bell tower. The large amount of SHM data collected over four years allowed to assess the modifications in the structural behaviour of the bell tower in post-earthquake conditions.

Application of vibration-based damage detection algorithms to experimental data from multi-storey steel structures

Recently, there has been remarkable progress in the development of data acquisition systems and statistical damage detection algorithms in the field of vibration-based structural health monitoring (SHM). However, many of them have not been validated by experimental data. This paper presents and analyzes a shake table experiment of two three-story steel structures. The structural damage was introduced in a systematic and controlled way and the structures were tested with different levels of earthquakes. Three types of analyses were conducted. First, a wireless sensor system designed for SHM, SnowFort, was demonstrated to have the same accuracy as the conventional wired sensors. Second, the rotation algorithm is applied to estimate residual displacements in the structures and achieves excellent agreements with the displacement sensor measurements. At last, a continuous wavelet transform-based damage detector is applied to the experimental data. The results highlight that the wavelet model parameters are sensitive to the damage state.

Application of vibration-based damage detection algorithms to experimental data from multi-storey steel structures

Recently, there has been remarkable progress in the development of data acquisition systems and statistical damage detection algorithms in the field of vibration-based structural health monitoring (SHM). However, many of them have not been validated by experimental data. This paper presents and analyzes a shake table experiment of two three-story steel structures. The structural damage was introduced in a systematic and controlled way and the structures were tested with different levels of earthquakes. Three types of analyses were conducted. First, a wireless sensor system designed for SHM, SnowFort, was demonstrated to have the same accuracy as the conventional wired sensors. Second, the rotation algorithm is applied to estimate residual displacements in the structures and achieves excellent agreements with the displacement sensor measurements. At last, a continuous wavelet transform-based damage detector is applied to the experimental data. The results highlight that the wavelet model parameters are sensitive to the damage state.

Fast unsupervised learning methods for structural health monitoring with large vibration data from dense sensor networks

Data-driven damage localization is an important step of vibration-based structural health monitoring. Statistical pattern recognition based on the prominent steps of feature extraction and statistical decision-making provides an effective and efficient framework for structural health monitoring. However, these steps may become time-consuming or complex when there are large volumes of vibration measurements acquired by dense sensor networks. To deal with this issue, this study proposes fast unsupervised learning methods for feature extraction through autoregressive modeling and damage localization through a new distance measure called Kullback–Leibler divergence with empirical probability measure. The feature extraction approach consists of an iterative algorithm for order selection and parameter estimation aiming to extract residuals in the training phase and another iterative process aiming to extract residuals only in the monitoring phase. The key feature of the proposed approach is the use of correlated residual samples of the autoregressive model as a new time series at each iteration, rather than handling the measured vibration response of the structure. This is shown to highly reduce the computational burden of order selection and feature extraction; moreover, it effectively provides low-order autoregressive models with uncorrelated residuals. The Kullback–Leibler divergence with empirical probability measure method exploits a segmentation technique to subdivide random data into independent sets and provides a distance metric based on the theory of empirical probability measure with no need to explicitly compute the actual probability distributions at the training and monitoring stages. Numerical and experimental benchmarks are then used to assess accuracy and performance of the proposed methods and compare them with some state-of-the-art approaches. Results show that the proposed approaches are successful in feature extraction and damage localization, with a reduced computational burden.

Vibration-based structural health monitoring using symbiotic organism search based on an improved objective function

Vibration-based structural health monitoring (VSHM) relying on model updating methods has been developed greatly and nowadays, not only serves as a major subset of SHM, but also shapes a special class of optimization problems. The historical course of evolution of this field via different research groups and with different goals and objectives, has resulted in the emergence of multiple objective functions, each appropriate only for certain damage scenarios and incapable of reasonably addressing others. The natural frequency residual (NFR) is an objective function sensitive to the damage intensity, which in the meantime, fails to predict the damage location in the symmetric structures. The total modal assurance criterion (TMAC) is another objective function which is sensitive to the damage position, but fails to estimate of the damage severity in the uniformly damaged structures. The present work successfully provides an innovative solution to unify both aforementioned objectives in a holistic objective function (HOF), through a particular combination of NFR and TMAC. Additionally, the competency of the above HOF for solving the VSHM problems has been investigated and demonstrated using a symbiotic organism search (SOS) optimization algorithm. The robustness and efficiency of the proposed VSHM method are verified through assessment of two un-damped benchmark structures. The obtained results indicate that in all damage scenarios, the HOF predicts with a high precision the damage location and succeeds in the high accuracy estimation of the damage extent. Consequently, the combination of the proposed HOF criterion and the SOS optimization algorithm is recommended as a reliable technique for VSHM.

Response-based multiple structural damage localization through multi-channel empirical mode decomposition

ABSTRACTExtensive research has been carried out on vibration-based structural health monitoring in the last few decades. A large number of these studies focus on response-based techniques due to its ease and efficiency. The main concern in such investigations is to extract a proper damage indicative feature from response data. This paper presents a new scheme in decomposing response data in order to extract a structural damage feature. Individual points on the structure body have their own time response signal. All these signals decomposed simultaneously through Multi-channel Empirical Mode Decomposition. Decomposed data of all structural points are put together to form a virtual structural deflection shape over time for each decomposed base vector. These time dependent deflection shapes are employed as a feature to determine damage location, if any. The proposed method was implemented both numerically and experimentally. In all cases, the proposed technique was able to locate the damaged zone successfully.

Elliptical Method for Damage Identification in Carbon Fibre Reinforced Polymer Laminates

Oftentimes, researchers in the area of vibration-based structural health monitoring (SHM) and damage detection focused their attention on the global properties of structures, which are modal frequencies and modal damping factors. However, the effect on the local properties for SHM, that is, modal constants, has not been extremely explored. In this paper, the elliptical plane modal identification method is proposed to be used as a damage identification method itself. It is observed that when the receptance is plotted in the elliptical plane, the area of the ellipse formed close to the resonant frequencies (which depends on the modal constants) can be used to detect damage, namely, composite carbon fibre reinforced polymer (CFRP) rectangular plates. Although a mathematical correlation has not been established yet, results show that the method is sensitive to the presence of damage in the test plates, as the area of the ellipse changes with damage.

Influence of damage versus temperature on modal strains and neutral axis positions of beam-like structures

Vibration-based Structural Health Monitoring is a non-destructive condition assessment method that exploits damage-related changes in the dynamic characteristics of a structure. The sensitivity of some commonly employed dynamic characteristics such as natural frequencies, to local damage of moderate severity might be lower than their sensitivity to environmental influences such as temperature. Modal strains are more sensitive to local damage, but the accurate dynamic monitoring of the very low strain levels that occur in civil engineering structures under ambient excitation became possible only recently. In this work, the influence of both temperature and damage on modal strains is experimentally investigated for a prestressed concreted beam in controlled laboratory conditions. Not only the modal strains themselves are considered, but also the neutral axis position under bending deformation, which relates directly to the bending stiffness. It is found that the induced temperature changes in the concrete beam do not have a measurable influence on the modal strains and neutral axis positions. The cracks that are induced in the beam by external loading in a progressive damage test do have a clear and local influence on the strain mode shapes and neutral axis positions, even at low damage levels.