Vibration-based Structural Health Monitoring Research Articles

Detection and Quantification of Delamination Failures in Marine Composite Bulkheads via Vibration Energy Variations.

This paper proposes a new vibration-based structural health monitoring method for the identification of delamination defects in composite bulkheads used in small-length fiber-based ships. The core of this work is to find out if the variations of vibration energy can be efficiently used as a key performance indicator for the detection and quantification of delamination defects in marine composite bulkheads. For this purpose, the changes of vibrational energy exerted by delamination defects in sandwich and monolithic composite panel bulkheads with different types of delamination phenomenon are investigated using a non-destructive test. Experiments show that the overall vibration energy of the bulkheads is directly dependent on the damage conditions of the specimens and therefore, the variations of this parameter are a good indicator of the incorporation of delamination defects in composite bulkheads. Additionally, the overall vibration energy changes also give interesting information about the severity of the delamination defect in the panels. Hence, this methodology based on vibratory energy can be used to accurately determine delamination defects in medium-sized composite bulkheads with the advantages of being a simple and cost-effective approach. The findings of this research possess important applications for the identification of delamination failures in composite components such as bulkheads, turbine blades, and aircraft structures, among others.

An artificial neural network methodology for damage detection: Demonstration on an operating wind turbine blade

This study presents a novel artificial neural network (ANN) based methodology within a vibration-based structural health monitoring framework for robust damage detection. The ANN-based methodology establishes the nonlinear relationships between selected damage sensitive features (DSF) influenced by environmental and operational variabilities (EOVs) and their corresponding novelty indices computed by the Mahalanobis distance (MD). The ANN regression model is trained and validated based on a reference state (i.e., a healthy structure). The trained model is used to predict the corresponding MD of new observations. The prediction error between the calculated and predicted MD is used as a new novelty index for damage detection. Firstly, an artificial 2D feature set is generated to illustrate how the limitations of solely using the MD-based novelty index can be overcome by the proposed ANN-based methodology. Secondly, the methodology is implemented in data obtained from an in-operation wind turbine with different artificially induced damage scenarios in one of its blades. Finally, the performance of the proposed methodology is evaluated by the metrics of accuracy, F1-score and Matthews correlation coefficient. The results demonstrate the advantages of the proposed methodology by improving damage detectability in all the different damage scenarios despite the influence of EOVs in both the simulated and real data.

Vibration-based damage detection of a concrete arch dam

Civil engineering structures are generally supposed to have long operation life spans during which they are submitted to many factors, both environmentally and human-induced, that degrade their condition and performance, gradually leading to the occurrence of damages and malfunctions. Therefore, systems capable of monitoring and assessing structural performance automatically are highly desirable.In this context, this paper describes the vibration-based structural health monitoring of Baixo Sabor arch dam, in Portugal, detailing results from the several processing stages needed to achieve the ability to detect damage. Automated operational modal analysis is applied to the data obtained during the first three years of monitoring, after which environmental and operational conditions affecting modal properties are studied and their effects minimised through the combined application of weighted regression models and principal components analysis. Finally, damages are simulated in a numerical model of the dam, and the results are used to test the ability of damage detection tools to find these anomalies.The present paper uses a quite unique set of experimental data to test for the first time the ability of a vibration-based SHM system to identify realistic damages in an arch dam.

A Comparative Analysis of Signal Decomposition Techniques for Structural Health Monitoring on an Experimental Benchmark.

Signal Processing is, arguably, the fundamental enabling technology for vibration-based Structural Health Monitoring (SHM), which includes damage detection and more advanced tasks. However, the investigation of real-life vibration measurements is quite compelling. For a better understanding of its dynamic behaviour, a multi-degree-of-freedom system should be efficiently decomposed into its independent components. However, the target structure may be affected by (damage-related or not) nonlinearities, which appear as noise-like distortions in its vibrational response. This response can be nonstationary as well and thus requires a time-frequency analysis. Adaptive mode decomposition methods are the most apt strategy under these circumstances. Here, a shortlist of three well-established algorithms has been selected for an in-depth analysis. These signal decomposition approaches—namely, the Empirical Mode Decomposition (EMD), the Hilbert Vibration Decomposition (HVD), and the Variational Mode Decomposition (VMD)—are deemed to be the most representative ones because of their extensive use and favourable reception from the research community. The main aspects and properties of these data-adaptive methods, as well as their advantages, limitations, and drawbacks, are discussed and compared. Then, the potentialities of the three algorithms are assessed firstly on a numerical case study and then on a well-known experimental benchmark, including nonlinear cases and nonstationary signals.

Clump interpolation error for the identification of damage using decentralized sensor networks

Recent developments in the field of smart sensing systems enable performing simple onboard operations which are increasingly used for the decentralization of complex procedures in the context of vibration-based structural health monitoring (SHM). Vibration data collected by multiple sensors are traditionally used to identify damage-sensitive features (DSFs) in a centralized topology. However, dealing with large infrastructures and wireless systems may be challenging due to their limited transmission range and to the energy consumption that increases with the complexity of the sensing network. Local DSFs based on data collected in the vicinity of inspection locations are the key to overcome geometric limits and easily design scalable wireless sensing systems. Furthermore, the onboard pre-processing of the raw data is necessary to reduce the transmission rate and improve the overall efficiency of the network. In this study, an effective method for real-time modal identification is used together with a local approximation of a damage feature, the interpolation error, to detect and localize damage due to a loss of stiffness. The DSF is evaluated using the responses recorded at small groups of sensors organized in a decentralized topology. This enables the onboard damage identification in real time thereby reducing computational effort and memory allocation requirements. Experimental tests conducted using real data confirm the robustness of the proposed method and the potential of its implementation onboard decentralized sensor networks.

Statistical Approach for Vibration-Based Damage Localization in Civil Infrastructures Using Smart Sensor Networks

One of the most discussed aspects of vibration-based structural health monitoring (SHM) is how to link identified parameters with structural health conditions. To this aim, several damage indexes have been proposed in the relevant literature based on typical assumptions of the operational modal analysis (OMA), such as stationary excitation and unlimited vibration record. Wireless smart sensor networks based on low-power electronic components are becoming increasingly popular among SHM specialists. However, such solutions are not able to deal with long data series due to energy and computational constraints. The decentralization of processing tasks has been shown to mitigate these issues. Nevertheless, traditional damage indicators might not be suitable for onboard computations. In this paper, a robust damage index is proposed based on a damage sensitive feature computed in a decentralized fashion, suitable for smart wireless sensing solutions. The proposed method is tested on a numerical benchmark and on a real case study, namely the S101 bridge in Austria, a prestressed concrete bridge that has been artificially damaged for research purposes. The results obtained show the potential of the proposed method to monitor the conditions of civil infrastructures.

An improved time-varying empirical mode decomposition for structural condition assessment using limited sensors

The safety concerns of transport infrastructure around the world have significantly increased over the past few years and pose a significant threat to the public and economic well-being. Condition assessment of transport infrastructure is currently performed using structural health monitoring (SHM). Over the last few decades, SHM of ageing infrastructure is leveraged using advancements in sensing technologies. However, vibration-based SHM methods that rely on sensor-driven data collection face several practical challenges such as traffic interruptions, availability of limited space, and a limited amount of sensors. In this study, a system identification technique is developed, which can deal with these practical challenges of SHM and provide reliable results. The proposed technique involves a time-varying filtering empirical mode decomposition algorithm that can decompose a single measured signal into its mono-component modal responses. The resulting mono-component signals are then analyzed using a wavelet-based reallocation algorithm called Multi-Synchrosqueezing Transform. The iterative nature of this algorithm is utilized to enhance the time-frequency representation of a nonstationary signal obtained from the ageing structures. The capability of the proposed method to identify the modal parameters is demonstrated using a suite of numerical and full-scale studies under various cases of measurement noise, closely-spaced and low energy modes, and damage detection using the limited sensor measurements.

Model-Assisted Compressed Sensing for Vibration-Based Structural Health Monitoring

The main challenge in the implementation of long-lasting vibration monitoring systems is to tackle the constantly evolving complexity of modern “mesoscale” structures. Thus, the design of energy-aware solutions is promoted for the joint optimization of data sampling rates, onboard storage requirements, and communication data payloads. In this context, the present work explores the feasibility of the rakeness-based compressed sensing (Rak-CS) approach to tune the sensing mechanism on the second-order statistics of measured data. In particular, a novel model-assisted variant (MRak-CS) is proposed, which is built on a synthetic derivation of the spectral profile of the structure by pivoting on numerical priors. Moreover, a signal-adapted sparsity basis relying on the wavelet packet transform operator is conceived, which aims at maximizing the signal sparsity while allowing for a precise time-frequency localization. The adopted solutions were tested with experiments performed on a sensorized pinned-pinned steel beam. Results prove that the rakeness-based compression strategies are superior to conventional eigenvalue approaches and to standard CS methods. The achieved compression ratio is equal to seven and the quality of the reconstructed structural parameters is preserved even in presence of defective configurations.

Damage Sensitive PCA-FRF Feature in Unsupervised Machine Learning for Damage Detection of Plate-Like Structures

Damage detection is important in maintaining the integrity and safety of structures. The vibration-based Structural Health Monitoring (SHM) methods have been explored and applied extensively by researchers due to its non-destructive manner. The damage sensitivity of features used can significantly affect the accuracy of the vibration-based damage identification methods. The Frequency Response Function (FRF) was used as a damage sensitive feature in several works due to its rich yet compact representation of dynamic properties of a structure. However, utilizing the full size of FRFs in damage assessment requires high processing and computational time. A novel reduction technique using Principal Component Analysis (PCA) and peak detection on raw FRFs is proposed to extract the main damage sensitive feature while maintaining the dynamic characteristics. A rectangular Perspex plate with ground supports, simulating an automobile, was used for damage assessment. The damage sensitivity of the extracted feature, i.e. PCA-FRF is then evaluated using unsupervised [Formula: see text]-means clustering results. The proposed method is found to exaggerate the shift of damaged data from undamaged data and improve the repeatability of the PCA-FRF. The PCA-FRF feature is shown to have higher damage sensitivity compared to the raw FRFs, in which it yielded well-clustered results even for low damage conditions.

Application of scaled boundary finite element method for vibration-based structural health monitoring of breathing cracks

The dynamic response of the host structure to a high-frequency actuation is usually used for the detection of tiny damage in structures in the form of breathing crack. The simulation of the microcrack’s effect on the response is essential for several damage identification targets. The conventional finite element method suffers from very small mesh size requirements to address the high-frequency problems, resulting in very large mass and stiffness matrices. In this study, the scaled boundary finite element method was applied to model different schemes of structural health monitoring of a structure with breathing cracks based on high-frequency vibration. The scaled boundary finite element method discretizes only the boundary of the model and thus substantially reduces the size of structural matrices. The node-to-node contact strategy was introduced to the scaled boundary finite element method to capture the contact problem that occurs during the vibration of the breathing crack. As breathing crack vibration results in some nonlinear effects, the simulation of three phenomena was of interest: higher harmonic generation, frequency shift, and vibro-acoustic modulation. A shooting method was used for efficient time integration and description of the frequency response function in the nonlinear regime. According to the results, the scaled boundary finite element method is of great power, efficiency, and accuracy to treat the contact problems, especially in high-frequency regimes. Moreover, the nonlinear methods provide certain advantages over the linear techniques in the early detection of incipient damage.

Infrared Thermography Measurement for Vibration-Based Structural Health Monitoring in Low-Visibility Harsh Environments.

In this study, infrared thermography is used for vibration-based structural health monitoring (SHM). Heat sources are employed as sensors. An acrylic frame structure was experimentally investigated using the heat sources as structural marker points to record the vibration response. The effectiveness of the infrared thermography measurement system was verified by comparing the results obtained using an infrared thermal imager with those obtained using accelerometers. The average error in natural frequency was between only 0.64% and 3.84%. To guarantee the applicability of the system, this study employed the mode shape curvature method to locate damage on a structure under harsh environments, for instance, in dark, hindered, and hazy conditions. Moreover, we propose the mode shape recombination method (MSRM) to realize large-scale structural measurement. The partial mode shapes of the 3D frame structure are combined using the MSRM to obtain the entire mode shape with a satisfactory model assurance criterion. Experimental results confirmed the feasibility of using heat sources as sensors and indicated that the proposed methods are suitable for overcoming the numerous inherent limitations associated with SHM in harsh or remote environments as well as the limitations associated with the SHM of large-scale structures.

A Novel VBSHM Strategy to Identify Geometrical Damage Properties Using Only Frequency Changes and Damage Library

Vibration-based structural health monitoring is an efficient way to diagnose damage and structural integrity at the earliest stage. In this paper, a new strategy is developed for damage localization and estimation, as well as damage properties identification for a rectangular geometry damage using only eigenfrequencies of the healthy and damaged structure. This strategy is applied to a cantilever beam. In this framework, a damage library is built by correlating 2D and 3D finite element models. The correlation is done by minimizing a so-called frequency shift coefficient. The proposed strategy also uses the frequency shift coefficient to correlate a 2D damaged model with an unknown beam case. The 2D damage, represented by a bending stiffness reduction, is then associated to a 3D damage by employing the damage library. Numerical cases with single and double damage of varying position and severity are tested and used to validate the approach. Finally, experimental results are proposed that show the relevance of the strategy.

Development of wireless smart sensor network for vibration-based structural health monitoring of civil structures

This paper introduces a wireless smart sensor network to meet the requirements for measuring low-amplitude ambient and sudden event monitoring of civil structures. The developed sensor platform consists of a high-sensitive sensing component for high-fidelity vibration measurements and a reliable event detection switch through a trigger accelerometer to detect and record sudden events. Particularly, the hardware of the sensor nodes was designed to provide high resolution and sensitivity, low noise, low power consumption, and the ability to measure the structural vibration in ultralow-power in long term to record sudden events. Also, the software architecture of the smart nodes was implemented to provide accurate time synchronisation and reliable event-triggered sensing with high resolution. To test the sensor performance, a series of experimental and field tests was conducted on a bridge model using shake table and an in-service highway viaduct. Also, the dynamic performance of the viaduct was assessed using its modal parameters. The results showed that the wireless smart sensor node is able to detect and record sudden events and provide high-fidelity structural responses and synchronised wireless network for structural condition monitoring. In addition, it was observed that the overall dynamic condition of the viaduct has no significant changes.

Machine learning paradigm for structural health monitoring

Structural health diagnosis and prognosis is the goal of structural health monitoring. Vibration-based structural health monitoring methodology has been extensively investigated. However, the conventional vibration–based methods find it difficult to detect damages of actual structures because of a high incompleteness in the monitoring information (the number of sensors is much fewer with respect to the number of degrees of freedom of a structure), intense uncertainties in the structural conditions and monitoring systems, and coupled effects of damage and environmental actions on modal parameters. It is a truth that the performance and conditions of a structure must be embedded in the monitoring data (vehicles, wind, etc.; acceleration, displacement, cable force, strain, images, videos, etc.). Therefore, there is a need to develop completely novel structural health diagnosis and prognosis methodology based on the various monitoring data. Machine learning provides the advanced mathematical frameworks and algorithms that can help discover and model the performance and conditions of a structure through deep mining of monitoring data. Thus, machine learning takes an opportunity to establish novel machine learning paradigm for structural health diagnosis and prognosis theory termed the machine learning paradigm for structural health monitoring. This article sheds light on principles for machine learning paradigm for structural health monitoring with some examples and reviews the existing challenges and open questions in this field.

Infinite mixture models for operational modal analysis: An automated and principled approach

The development of a fully automated system identifier without the need for human intervention, is a key step for real-time vibration-based Structural Health Monitoring (SHM). In this paper a novel approach based on the Dirichlet Process Gaussian Mixture Model (DP-GMM) is developed in order to analyze the stabilization diagram. The aim is to separate the true physical modes from the mathematically spurious modes in a fully automated manner, whilst eliminating the need for any manually specified parameters or thresholds. The parametric Covariance Driven Stochastic Subspace Identification (SSI-Cov) is adopted to estimate the modal parameters, and consequently establish the initial stabilization diagram. From there, the use of a two-stage algorithm involving a DP-GMM is proposed to non-parametrically perform an automated cleaning of the stabilization diagram. The contributions of the paper are five-fold: (1) A probabilistic approach based on a DP-GMM is proposed to analyze the stabilization diagram. To the best knowledge of the authors, this study presents one of the first attempts of DP-GMM for full automation of Operational Modal Analysis (OMA). The method is validated using the field test data from a large-scale operating cable-stayed bridge, which has two closely-spaced modes around 3 Hz. Not only are these two complicated scenarios consistently identified, but the performance of the method with respect to the problem of missing modes is compared against a reference method based on the conventional multi-stage clustering technique used in OMA, wherein superior performance of the proposed method is demonstrated. (2) The method does not require specification of any threshold or parameter at any stage of the algorithm for cleaning the stabilization diagram, making the approach a potential for robust and fully automated modal identification. (3) Compared to many conventional multi-stage clustering techniques, the proposed approach is computationally efficient as intelligent updates are made to the model using multiple linear algebra properties. (4) New feature vectors are developed which are justified using a combination of mathematical rigor, visual understanding, and engineering intuition. (5) Due to the probabilistic nature of the method, the identification results are accompanied with uncertainty bounds. Several mathematical proofs are presented to explain the observed behavior of the uncertainty bounds.

Metamodel-based pattern recognition approach for real-time identification of earthquake-induced damage in historic masonry structures

Damage localization/quantification through vibration-based Structural Health Monitoring (SHM) is commonly performed by inverse calibration of a numerical model. Nevertheless, the numerous simulations required in the associated optimization problem pose a daunting obstacle when applied to real-time SHM. Particularly critical are heritage buildings, whose complex geometries often require computationally intensive modellings. In this light, this paper presents a novel earthquake-induced damage identification approach for historic masonry structures. This relies upon the use of a computationally efficient meta-model suited for real-time system identification. The optimization problem is formulated accounting for discrepancies between numerical and experimental resonant frequencies and mode shapes. Damage localization/quantification is enabled by multivariate analyses of continuously identified model parameters. A real medieval tower is presented as a case study, and several damage scenarios are simulated and used for validation. The reported results pave the way for the development of next-generation long-term vibration-based SHM systems with real-time damage identification capabilities.

Vibration-based structural health monitoring of a RC-masonry tower equipped with non-conventional TMD

This article presents the development and the results of three years of implementation of an automated vibration-based structural health monitoring system for modal tracking the dynamic characteristics of a concrete-masonry Civic Tower in Rieti (Italy) equipped with a passive vibration control system, a Non-Conventional Tuned Mass Damper. The system is based on the data recorded by a small number of high-sensitivity accelerometers set on remote automated modal parameters tracking. The analysis of monitoring data highlights the main characteristics of the response of the tower to operational vibrations and low-return period earthquakes. Despite the low levels of vibration in operational conditions, the system can track the evolution of the structural frequencies along time and successfully capture their dependence from temperature, both daily and seasonally. Moreover, the robustness of the modal identification procedure allowed the detection of anomalous variation from the validated reference dynamics of the structure.

FS-LSTM-Based Sensor Fault and Structural Damage Isolation in SHM

A few sensing techniques have been developed for detecting defects in civil infrastructures in structural health monitoring (SHM) instead of onsite human inspection. Many false alarms observed are due to faults in sensors, and some of them may lead to incorrect interpretation of the measured data. In this paper, the differentiation of different sources of variability, i.e. the sensor fault and structural damage, is investigated for a vibration-based SHM system. A deep learning-based method with a new architecture of Fully connected Stateful Long Short Time Memory (FS-LSTM) Neural Networks for differentiating the sensor fault and structural damage without information on details of the fault is proposed. Information from the sensor fault is assumed local, and that from structural damage is relatively global affecting several sensors. Since the LSTM are capable to learn data features automatically, the proposed method performs well without an accurate mathematical model. Stationary random acceleration responses of a three-span continuous bridge when under operational conditions are studied. A deep network model is applied to the measured data with estimation on four types of sensor fault. The response residues between the true and predicted values of the deep FS-LSTM NN is statistically analyzed to determine the threshold of sensor fault. An experimental study with a steel frame in the laboratory is also performed to validate the proposed method.

Mitigating effects of temperature variations through probabilistic-based machine learning for vibration-based bridge scour detection

This paper presents a novel approach to mitigating the effect of temperature variations on the bridges’ dynamic modal properties for more reliably detecting scour damage around bridge piles based on the vibration-based measurements. The novelty of the presented approach lies in its ability to reasonably remove the impacts on the modal properties of bridges, particularly caused by changes in material properties and structural boundary conditions due to temperature variations without explicitly modeling these complex effects. The main idea is to adopt the probabilistic-based machine learning method, Gaussian Process Model, to learn the correlation between the changes of modal properties of a monitored bridge and the corresponding temperature variations from in situ sensor measurements, and probabilistically infer the bridge scour based on the modified vibration measurements, which have mitigated the identified impacts of temperature variations, by applying Bayesian inference through the Transitional Markov Chain Monte Carlo simulation. The proposed approach and its applicability are presented and validated through the numerical simulation of a prototype bridge, demonstrating its potential for practical application for mitigating effects of temperature variations or other environmental impacts for vibration-based Structural Health Monitoring. The limitation of the presented study and future research needs are also discussed.

The role of modal parameters uncertainty estimation in automated modal identification, modal tracking and data normalization

During the last decade, many vibration-based structural health monitoring systems have been successfully implemented in different structures such as bridges, towers, stadia and wind turbines, with the aim of studying the structure dynamics and its evolution over time, eventually detecting the occurrence of novel structural behaviour that may indicate the presence of damage.Such vibration-based monitoring systems generally rely on the identification of modal properties, which are then used as monitoring features. Therefore, from operational modal analysis to the tracking of those features and finally to data normalization, many processing steps occur that depend on the accuracy of the identified modal properties. Thus, the estimation of the uncertainties associated with the identified modal properties increases the robustness of this process.In this context, data obtained from the continuous dynamic monitoring of a concrete arch dam has been used to evaluate the gains of quantifying the uncertainties of modal properties, evaluating in particular the effect of taking these uncertainties into consideration when performing automated operational modal analysis, modal tracking and data normalization. Nevertheless, it is observed that the most significant gains of considering estimates uncertainties occur when these quantities are used for removing outliers during modal tracking.