Vibration-based Structural Health Monitoring Research Articles

A new open‐database benchmark structure for vibration‐based Structural Health Monitoring

Vibration-based Structural Health Monitoring is an ongoing field of research in many engineering disciplines. As for civil engineering, plenty of experimental structures have been erected in the past decades, both under laboratory and real-life conditions. Some of these facilities became a benchmark for different kinds of methods associated with Structural Health Monitoring such as damage analysis and Operational Modal Analysis, which led to fruitful developments in the global research community. When it comes to the continuous monitoring and assessment of the structural integrity of mechanical systems exposed to environmental and operational variability, the robustness and adaptability of the applied methods is of utmost importance. Such properties cannot be fully evaluated under laboratory conditions, which highlights the necessity of outdoor measurement campaigns. To this end, we introduce a test facility for Structural Health Monitoring comprising a lattice tower exposed to realistic conditions and featuring multiple reversible damage mechanisms. The structure located near Hanover in Northern Germany is densely equipped with sensors to capture the structural dynamics. The environmental conditions are monitored in parallel. The obtained continuous measurement data can be accessed online in an open repository. That is the foundation for benchmarks, consisting of a growing data set that enables the development, evaluation, and comparison of Structural Health Monitoring strategies and methods. In this article, we offer a documentation of the test facility and the data acquisition system. Lastly, we characterize the structural dynamics with the help of a finite element model and by analyzing several month of data.

Addressing practicalities in multivariate nonlinear regression for mitigating environmental and operational variations

A significant problem associated with the implementation of Vibration-Based Structural Health Monitoring (VSHM) systems originates from the detrimental effects caused by Environmental and Operational Variations (EOVs). The EOVs cause observations from the same structural condition to behave in different manners. As such, this leads to issues when defining a robust baseline state as well as making the discrimination between undamaged and damaged observations more challenging. In order to address these challenges, multivariate nonlinear regression is implemented to account for the EOVs. Damage Sensitive Features (DSFs) are extracted from acceleration data and then are regressed based on environmental and operational parameters. New features are effectively normalised by finding the difference between the measured and predicted values. This process removes the influence of the EOVs in an explicit manner, allowing for more reliable damage detection. While the benefit of the application of different regression methodologies has already been demonstrated in the past, this work addresses a number of practicalities in the implementation of VSHM on real systems. The analysis investigates the selection of the DSF and is investigated alongside another analysis into how the damage detection behaves under varying amounts of input information. Furthermore, a method is proposed to understand and account for a large number of outliers in the training data.

Localization and classification of structural damage using deep learning single-channel signal-based measurement

Diligent damage identification is a core thrust of structural health monitoring (SHM). Vibration-based SHM has recently gained paramount importance. Substantial research devoted to Deep Learning (DL) algorithms yielded superior accuracy. This study proposes a novel DL-based damage detection approach to automatically extract features from raw acceleration sensor data. A new One-Dimensional Convolutional Neural Network named BuildingNet was designed to learn features and identify damage locations in real-time under different damage assessment scenarios. Parametric studies were conducted on different layer numbers, size of training datasets, and noise levels. Systematic studies for optimizing the network architecture and training data were performed. Mid-rise building case studies demonstrated the accuracy and efficiency of the proposed model. Time-domain monitoring data, both from multiple and single-channel measurements, were used for training and testing different architectures of BuildingNet. The proposed model achieved excellent damage localization and classification performance on both noise-free and noisy data sets.

Advances in intelligent long-term vibration-based structural health-monitoring systems for bridges

The true realization of the benefits of vibration based structural health monitoring (VBSHM) in real-world applications is acquired through long-term continuous monitoring so that one can attain a detailed grasp of the behavior of the monitored structure. The challenges in long-term continuous VBSHM include: the large volume of accumulated monitoring data; the effective extraction of engineering information amid the influences of noise and uncertainties embedded in the monitoring data; maintaining continuity and consistency in the long-term monitoring data considering that the system and instrumentation may change due to sensor failure or renewal due to advances in sensing technologies. To meet these challenges, this paper presents recent research that has resulted in the development of a framework and specialized signal processing and data analytic tools for long-term continuous VBSHM suitable for real-world monitoring applications of structures in the field. These include efficient tools for large scale intelligent data processing and analysis, management of monitoring database and extracted information relevant to the structural health of the monitored structure. The novel Automated In-Line Full Space Identification (AI-FSI) method is presented to address the needs and challenges associated with long-term continuous VBSHM, such as the automation of all data processing and analysis operations including modal parameter estimations and mode tracking, and the need of minimizing the measurement and computational uncertainties and variability in the operational modal analysis results. A smart self-diagnostic system for the monitoring of the health of the data collection sensors and monitoring system has also been developed that will allow the consistent use of the monitoring data of different sensor configurations and era in the monitoring project. Examples on the efficiency of analyzing the monitoring data collected over 20 years from the Confederation Bridge monitoring project in Atlantic Canada by using the developed novel framework and data analytic tools are presented.

Vibration-based damage detection in a wind turbine blade through operational modal analysis under wind excitation

In the present study, a vibration-based structural health monitoring (SHM) method for wind turbine blades using Operational Modal Analysis (OMA) is presented. To simulate the dynamic response of the wind turbine, the NREL FAST tool implemented in QBlade was used to calculate the forces in the time domain along the blade in response to the stochastic excitation of the rotating blade by means of the rotationally sampled turbulence spectrum of the wind. These force time series are used as input to the transient analysis in ANSYS Workbench. The acceleration time series extracted along the blade are used for Operational Modal Analysis, and the modal parameters are obtained with the Frequency Domain Decomposition (FDD) algorithm. The comparison between the modal properties of an undamaged and a damaged blade is used for the detection of longitudinal crack-type damage features in the upper part of the blade. The proposed method is shown to be capable of correctly detecting and locating the damage with blade accelerations occurring due to the stochastic excitation by the wind only. This approach could pave the way to low-cost structural health monitoring of wind turbine blades and rotors.

Magnetostrictive vibration sensor for a self- powered structural health monitoring system

Previously, we proposed a self-powered vibration-based structural health monitoring system for diagnosing the structural soundness of entire bridges to screen bridges requiring detailed inspection. The core part of the system is the vibration-sensing device that is in the developing phase. This part relies on the Villari effect of magnetostrictive materials. In particular, the vibration-sensing device is a dual-use device that can serve as a vibration sensor and a power generator. This paper describes the design and evaluation results of the device’s performance. Galfenol, a typical magnetostrictive material, was used. Therefore, we first investigate the relationship between the magnetic flux density variation and the compression stress with different bias magnetic fields applied to the Galfenol rod. Moreover, an adequate range of the bias magnetic field was determined to maximize the power-generation performance and sensitivity of vibration. Subsequently, the prototype of the vibration-sensing device was designed and built, and a field test was conducted. The experimental results confirmed that the device works as a vibration sensor with high resolution. The device accurately detected the natural frequency of a bridge, even when subjected to the intermittent load of passing cars. The device generated 30.3 mJ in an hour even though it is a small-sized non-resonant power generator. With such generated energy, diagnosing the structural health at least once every two days can be executed with the developed device.

A review on vibration-based structural pipeline health monitoring method for seismic response

A review on vibration-based structural pipeline health monitoring method for seismic response

Review on Vibration-Based Structural Health Monitoring Techniques and Technical Codes

Structural damages occur in modern structures during operations due to environmental and human factors. The damages accumulating with time may lead to a significant decrease in structure performance or even destruction; natural symmetry is broken, resulting in an unexpected life and economic loss. Therefore, it is necessary to monitor the structural response to detect the damage in an early stage, evaluate the health condition of structures, and ensure the operation safety of structures. In fact, the structure and the evaluation can be considered as a special symmetry. Among several SHM methods, vibration-based SHM techniques have been widely adopted recently. Hence, this paper reviews the vibration-based SHM methods in terms of the vibrational parameters used. In addition, the technical codes on vibration based SHM system have also been reviewed, since they are more important in engineering applications. Several related ISO standards and national codes have been developed and implemented, while more specific technical codes are still required to provide more detailed guidelines in practice to maintain structure safety and natural symmetry.

A deep‐learning approach for health monitoring of a steel frame structure with bolted connections

This study is motivated by the need to develop a data-driven deep-learning approach for vibration-based structural health monitoring of a steel frame structure with bolted connections. A convolutional-neural-network-based deep-learning architecture is designed and trained to extract discriminative features from the vibration-based time-frequency scalogram images and use those to distinguish the undamaged and damaged cases of the targeted frame structure. Different damage and undamaged classes corresponding to the loosening of bolts are categorized as fully loose, hand tight, and fully tight (undamaged) conditions. The average training and validation accuracy were found to be 100% and 98.1%, respectively. In order to check the performance and robustness of the technique, testing is carried out for an unseen dataset corresponding to the training classes as well as some additional cases close to the training classes. The proposed deep-learning approach can successfully classify the damage classes with high testing accuracy that demonstrates its efficacy as an automation tool for health monitoring of connections of plane frame structures.

A numerical evaluation on nonlinear dynamic response of sandwich plates with partially rectangular skin/core debonding

Abstract As one of the most dangerous defects in the sandwich panel, debonding could significantly degrade load carrying capacity and affect dynamic behaviour. The present work dealt with debonding detection of the rectangular clamped hybrid sandwich plate by using ABAQUS software. The influence of various damage ratios on the linear and nonlinear dynamic responses has been studied. The finite element model was initially validated by comparing the modal response with the experimental test. Rectangular debonding was detected by comparing dynamic responses of free and forced vibrations between intact and debonded models. A wide range of driving frequency excitation corresponding to transient and harmonic concentrated loads was implemented to highlight nonlinear behaviour in the intermittent contact in the debonded models. The results showed that debonding existence contributed to the natural frequency reduction and modes shape change. The numerical results revealed that debonding affected both the steady-state and impulse responses of the debonded models. Using the obtained responses, it was detected that the contact in the debonded region altered the dynamic global response of the debonded models. The finding provided the potential debonding diagnostic in ship structure using vibration-based structural health monitoring.

Value of information from vibration-based structural health monitoring extracted via Bayesian model updating

Quantifying the value of the information extracted from a structural health monitoring (SHM) system is an important step towards convincing decision makers to implement these systems. We quantify this value by adaptation of the Bayesian decision analysis framework. In contrast to previous works, we model in detail the entire process of data generation to processing, model updating and reliability calculation, and investigate it on a deteriorating bridge system. The framework assumes that dynamic response data are obtained in a sequential fashion from deployed accelerometers, subsequently processed by an output-only operational modal analysis scheme for identifying the system’s modal characteristics. We employ a classical Bayesian model updating methodology to sequentially learn the deterioration and estimate the structural damage evolution over time. This leads to sequential updating of the structural reliability, which constitutes the basis for a preposterior Bayesian decision analysis. Alternative actions are defined and a heuristic-based approach is employed for the life-cycle optimization. By solving the preposterior Bayesian decision analysis, one is able to quantify the benefit of the availability of long-term SHM vibrational data. Numerical investigations show that this framework can provide quantitative measures on the optimality of an SHM system in a specific decision context.

Validation of vibration-based structural health monitoring on retrofitted railway bridge KW51

In this paper, the feasibility of structural health monitoring based on natural frequencies is investigated for a steel bowstring railway bridge in Leuven, Belgium. The data used in the study are obtained from an ongoing long-term monitoring campaign on the railway bridge and include acceleration measurements on the bridge deck and the arches. During the monitoring period, the railway bridge has been retrofitted, resulting in data for two distinct states of the structure. Particular attention is paid to removing the effects of environmental conditions, such as temperature, which affect the modal characteristics of the structure and therefore may lead to false-positive or false-negative damage detection. A comparison is made between standard linear regression and robust principal component analysis (PCA), two black-box modeling techniques which are adopted to remove natural frequency variations resulting from changes in the environmental conditions. In order to assess the success rate of these techniques, a receiver operating characteristic (ROC) curve analysis is performed, considering the actual retrofit as well as a number of more subtle structural changes, which are modeled using a detailed finite element model of the structure. The state transition can be observed for the actual retrofit as well as for smaller structural modifications that result in relatively small natural frequency shifts.

Structural energy transfer to the elevated pile-cap foundation of an offshore wind turbine based on extracted transfer path analysis

In the long-term operation of an offshore wind turbines (OWT), the tower bears various harmonic excitations generated by the rotation of the impeller and other environmental excitations. The energy generated by excitations will be transferred to the bottom foundation through the tower. For an elevated pile-cap foundation, different piles receive different amounts of energy, and piles that receive the largest amount of energy are more likely to move than others, making it difficult to ensure the stability of the foundation structure. In this study, the structural energy transfer to the elevated pile-cap foundation of the OWT based on extracted transfer path analysis (ETPA) is studied by considering the impeller–tower–pile structure as a source–path–receiver system. The energy transmissibility functions between the impeller and different piles of the foundation are calculated to identify the pile that bears the maximum energy from impeller rotation or other excitations. Compared with the widely applied classical transfer path analysis, the ETPA method can overcome the noise generated by OWT operation by feature extraction and yield more effective results in the case of noise. In general, the pile that bears the most energy is more likely to move compared to other piles. The calculated results can be used to assess the stability of the foundation for taking corresponding measures according to project requirements and be used for vibration-based damage detection and structural health monitoring. To study the effectiveness of the ETPA method, a four-degree-of-freedom energy transfer path theoretical model, finite element model and physical model of OWT are used to numerical study, simulation and physical experiment, respectively. Computed results of transmissibility are compared with the maximum extreme displacement of piles to verify the validity.

Statistical subspace-based damage detection with estimated reference

The statistical subspace-based damage detection technique has shown promising theoretical and practical results for vibration-based structural health monitoring. It evaluates a subspace-based residual function with efficient hypothesis testing tools, and has the ability of detecting small changes in chosen system parameters. In the residual function, a Hankel matrix of output covariances estimated from test data is confronted to its left null space associated to a reference model. The hypothesis test takes into account the covariance of the residual for decision making. Ideally, the reference model is assumed to be perfectly known without any uncertainty, which is not a realistic assumption. In practice, the left null space is usually estimated from a reference data set to avoid model errors in the residual computation. Then, the associated uncertainties may be non-negligible, in particular when the available reference data is of limited length. In this paper, it is investigated how the statistical distribution of the residual is affected when the reference null space is estimated. The asymptotic residual distribution is derived, where its refined covariance term considers also the uncertainty related to the reference null space estimate. The associated damage detection test closes a theoretical gap for real-world applications and leads to increased robustness of the method in practice. The importance of including the estimation uncertainty of the reference null space is shown in a numerical study and on experimental data of a progressively damaged steel frame.

Adaptive Probabilistic Optimization Approach for Vibration-Based Structural Health Monitoring

This paper presents a novel adaptive probabilistic algorithm to identify damage characteristics by integrating the use of the frequency response function with an optimization approach. The proposed algorithm evaluates the probability of damage existence and determines salient details such as damage location and damage severity in a probabilistic manner. A multistage sequence is used to determine the probability of damage parameters including crack depth and crack location while minimizing uncertainties. A simply supported beam with an open edge crack was used to demonstrate the application of the algorithm for damage detection. The robustness of the algorithm was tested by incorporating varying levels of noise into the frequency response. All simulation results show successful detection of damage with a relatively high probability even in the presence of noise. Results indicate that the probabilistic algorithm could have significant advantages over conventional deterministic methods since it has the ability to avoid yielding false negatives that are quite common among deterministic damage detection techniques.

A Framework for Long-Term Vibration-Based Monitoring of Bridges.

A new framework for long-term monitoring of bridges is proposed in order to negate (i) the impact of measurement uncertainties on damage detection in vibration-based structural health monitoring and (ii) the low sensitivity of damage indicators to low levels of damage. The framework is developed using three vibration-based damage indicators that have an intuitive physical correlation with damage: modal curvature, modal strain energy and modal flexibility. The article first quantifies the efficacy of these damage indicators when based on two observations, one from the undamaged state and one from the monitored state, in detecting and locating damage for different damage levels that are simulated on an 84-m long railway bridge. A long-term monitoring framework based on a new parameter defined as the frequency of the damage indicator exceeding the threshold value within a population of observations is developed. Impact of several factors including the damage location, damage indicator used in the framework, and the noise level on the success of the developed framework was investigated through numerical analysis. The new parameter, when used together with modal strain energy, was shown to provide a very clear picture of damage initiation and development over time starting from very low damage levels. Furthermore, the location of the simulated damage can be identified successfully at all damage levels and even for very high noise levels using the proposed framework.

Auto-Regressive Integrated Moving-Average Machine Learning for Damage Identification of Steel Frames

Auto-regressive (AR) time series (TS) models are useful for structural damage detection in vibration-based structural health monitoring (SHM). However, certain limitations, e.g., non-stationarity and subjective feature selection, have reduced its wide-spread use. With increasing trends in machine learning (ML) technologies, automated structural damage recognition is becoming popular and attracting many researchers. In this paper, we combined TS modeling and ML classification to automatically extract damage features and overcome the limitation of non-stationarity. We propose a two-stage framework, namely auto-regressive integrated moving-average machine learning (ARIMA-ML) with modules for pre-processing, model parameter determination, feature extraction, and classification. Based on shaking table tests of a space steel frame, floor acceleration data were collected and labeled according to experimental observations and records. Subsequently, we designed three damage classification tasks for: (1) global damage detection, (2) local damage detection, and (3) local damage pattern recognition. The results from these three tasks indicated the robustness and accuracy of the proposed framework where 97%, 98%, and 80% average segment accuracy were achieved, respectively. The confusion matrix results showed the unbiased model performance even under an imbalanced-class distribution. In summary, the presented study revealed the high potential of the proposed ARIMA-ML framework in vibration-based SHM.

Toward Workable and Cost-Efficient Monitoring of Unstable Rock Compartments with Ambient Noise

Ambient Vibration-Based Structural Health Monitoring (AVB–SHM) studies on prone-to-fall rock compartments have recently succeeded in detecting both pre-failure damaging processes and reinforcement provided by bolting. The current AVB–SHM instrumentation layout is yet generally an overkill, creating cost and power issues and sometimes requiring advanced signal processing techniques. In this article, we paved the way toward an innovative edge-computing approach tested on ambient vibration records made during the bolting of a ~760 m3 limestone rock column (Vercors, France). First, we established some guidelines for prone-to-fall rock column AVB–SHM by comparing several basic, computing-efficient, seismic parameters (i.e., Fast Fourier Transform, Horizontal to Vertical and Horizontal to Horizontal Spectral Ratios). All three parameters performed well in revealing the unstable compartment’s fundamental resonance frequency. HHSR appeared as the most consistent spectral estimator, succeeding in revealing both the fundamental and higher modes. Only the fundamental mode should be trustfully monitored with HVSR since higher peaks may be artifacts. Then, the first application of a novelty detection algorithm on an unstable rock column AVB–SHM case study showed the following: the feasibility of automatic removing the adverse thermomechanical fluctuations in column’s dynamic parameters based on machine learning, as well as the systematic detection of clear, permanent change in column’s dynamic behavior after grout injection and hardening around the bolts (i1 and i2). This implementation represents a significant workload reduction, compared to physical-based algorithms or numerical twin modeling, and shows better robustness with regard to instrumentation gaps. We believe that edge-computing monitoring systems combining basic seismic signal processing techniques and automatic detection algorithms could help facilitate AVB–SHM of remote natural structures such as prone-to-fall rock compartments.

A New Damage Index Based on Statistical Features, PCA, and Mahalanobis Distance for Detecting and Locating Cables Loss in a Cable-Stayed Bridge

Cable-stayed bridges are widely used all around the world. Unfortunately, during their service life, they are exposed to adverse conditions that may cause their deterioration and, consequently, their collapse. Vibration-based structural health monitoring techniques have become the most promising alternatives for efficiently detecting and locating damage into civil structures. In this regard, this paper presents a new methodology based on statistical features, Principal component analysis (PCA), and Mahalanobis distance (MD) for detecting and locating a cable loss in the Río Papaloapan bridge (RPB) using vibration signals. It is based on the extraction of a set of statistical time features (STFs) from vibration signals, which are analyzed using the autocorrelation function (ACF) to denoise and strengthen the features found in them. Then PCA-based models are computed by using the STFs to enhance the damage location process. Then a new damage index based on MD is proposed to indicate if a damage exists and its location.

Polynomial Chaos-Kriging metamodel for quantification of the debonding area in large wind turbine blades

This study aims to investigate the performance of a data-driven methodology for quantifying damage based on the use of a metamodel obtained from the Polynomial Chaos-Kriging method. The investigation seeks to quantify the severity of the damage, described by a specific type of debonding in a wind turbine blade as a function of a damage index. The damage indexes used are computed using a data-driven vibration-based structural health monitoring methodology. The blade’s debonding damage is introduced artificially, and the blade is excited with an electromechanical actuator that introduces a mechanical impulse causing the impact on the blade. The acceleration responses’ vibrations are measured by accelerometers distributed along the trailing and the wind turbine blade. A metamodel is formerly obtained through the Polynomial Chaos-Kriging method based on the damage indexes, trained with the blade’s healthy condition and four damage conditions, and validated with the other two damage conditions. The Polynomial Chaos-Kriging manifests promising results for capturing the proper trend for the severity of the damage as a function of the damage index. This research complements the damage detection analyses previously performed on the same blade.