Vibration-based Methods Research Articles

Damage identification of long-span bridges based on the correlation of probability distribution of monitored quasi-static responses

Structural health diagnosis is one of the most critical issues in structural health monitoring (SHM). Vibration-based methods have been extensively investigated in the last few decades in the SHM community, but they encounter the challenges of local damage insensitivity and environmental sensitivity. However, the study of structural health diagnosis based on structural quasi-static response data, which has well-defined spatial coordinates and is sensitive to the local condition of the structure, is insufficient. It is difficult to extract structural models or parameters that directly indicate structural damage owing to the coupling effect of the damage and unknown external loads in the quasi-static response data. This study proposes a method based on the correlation of the probability distribution of the quasi-static response data for damage identification, and it is proved that the variation in the structural condition can be inferred from the variation in the correlation of the probability distribution of multiple quasi-static response data. The earth mover’s distance (EMD) is introduced as a quantitative indicator of the variation in the probability distribution function. The difference in the EMD (DEMD) between two monitored quasi-static responses was adopted as the damage indicator. The monitored strain on the steel box girder of a multi-tower cable-stayed bridge and the monitored cable tension of the cable-stayed bridge were employed to validate the proposed method. The results show that the proposed method successfully identifies the damage to the steel box girders and stay cables.

Extending the vibration-based sound power method to determine the sound power radiated from acoustic sources

A vibration-based sound power (VBSP) method has been developed as an alternative method for determining the acoustic energy radiated from structures. Many acoustic sources, such as a blender or motor, have sound power contributions that originate from within the structure and therefore cannot be scanned properly using a vibrometer. For these types of sources, a rectangular enclosure with five rigid sides and a single mylar side was fabricated to enclose the acoustic source, so that the VBSP method could be utilized to obtain the radiated sound power. A calibration curve was developed to account for the enclosure effects on these sources, and this curve is used to adjust the measured results to give the true free-field radiated sound power. Experimental results will be shown comparing the sound power obtained from the adapted VBSP method with the sound power of the source obtained in a reverberation chamber using the ISO 3741 standard. Furthermore, computational results will be shown comparing the sound power obtained through the boundary element method (BEM) with the sound power obtained by using the velocities calculated from that model processed with our VBSP method. Funding for this work was provided by the National Science Foundation (NSF).

A Review of Road Surface Anomaly Detection and Classification Systems Based on Vibration-Based Techniques

Road surfaces suffer from sources of deterioration, such as weather conditions, constant usage, loads, and the age of the infrastructure. These sources of decay generate anomalies that could cause harm to vehicle users and pedestrians and also develop a high cost to repair the irregularities. These drawbacks have motivated the development of systems that automatically detect and classify road anomalies. This study presents a narrative review focused on road surface anomaly detection and classification based on vibration-based techniques. Three methodologies were surveyed: threshold-based methods, feature extraction techniques, and deep learning techniques. Furthermore, datasets, signals, preprocessing steps, and feature extraction techniques are also presented. The results of this review show that road surface anomaly detection and classification performed through vibration-based methods have achieved relatively high performance. However, there are challenges related to the reproduction and heterogeneity of the results that have been reported that are influenced by the limited testing conditions, sample size, and lack of publicly available datasets. Finally, there is potential to standardize the features computed through the time or frequency domains and evaluate and compare the diverse set of settings of time-frequency methods used for feature extraction and signal representation.

Load Transfer Efficiency Assessment of Concrete Pavement Joints Using Distributed Optical Vibration Sensor

This paper presents a method to assess the load transfer efficiency (LTE) of concrete pavement joints using distributed optical vibration sensors. First, a theoretical analysis of concrete pavement vibration was conducted to investigate how to reflect LTE by spectral amplitude. Second, distributed optical vibration sensor (DOVS) was applied to measure vibration around joints distributedly. Third, the corresponding processing method for DOVS data was proposed to calculate the ratio of spectral amplitude from different slabs through power spectral density (PSD) analysis. Then, field tests were conducted on nine concrete pavement slabs with three different types of joints (dummy joint, rabbet joint, and dowel bars). The deflection-based method as well as the proposed vibration-based method were employed to assess the LTE of eleven joints on two different dates. The comparative analysis results indicate the deflection-based LTE (DLTE) and the ratio of PSD (RPSD) have a strong correlation (0.871) and a slight difference (<±0.03) overall. The correlation is robust in different dates and types of joints (0.844~0.88). These findings prove the accuracy and effectiveness of the proposed vibration-based method.

Real-time online intelligent perception of time-varying cable force based on vibration monitoring

During the service period of cables, the corrosion fatigue is the principal damage form under the combined action of traffic loading and external environment. The real-time and accurate perception of the time-varying cable force in service is the foundation for exactly evaluating the safety status of cable. However, most of the existing methods identifying cable force can only obtain the average cable force over a period of time, besides, although a few methods can detect the time-varying cable force, they cannot meet the requirements of time-frequency resolution. This paper proposes a real-time intelligent vibration-based perception method of the time-varying cable force based on the block recursive APES method and cable dynamic stiffness analysis theory. Through the efficient recursive APES method, the real-time intelligent perception of time-varying frequencies is abstracted from the real-time monitoring vibration signal of the cable. Based on the accurate dynamic analysis model of the cable, the cable forces and modal frequencies calculated by the theoretical formula are fitted to obtain an accurate identification method for the time-varying cable force, which can meet the real-time and accuracy requirements at the same time. Combining the above two methods, a real-time intelligent perception method for perceiving the time-varying cable force is proposed. In order to verify the feasibility and accuracy of the proposed method, cases on real bridge cables were analyzed, and the selection methods of order, range and accuracy of time-varying frequency and cable force were proposed. The results demonstrated that the proposed method can realize the real-time accurate perception of time-varying cable force, and the temperature effect has an obvious influence on the time-varying cable force. In the statistical analysis of cable stress amplitude, the influence of temperature trend should be removed.

Detecting Changes in Boundary Conditions based on Sensitivity-based Statistical Tests

Structural health monitoring is a promising technology to automatically detect structural changes based on permanently installed sensors. Vibration-based methods that evaluate the global system response to ambient excitation are suited to diagnose changes in boundary conditions, i.e. changes in member prestress or imposed displacements. In this paper, these changes are evaluated based on sensitivity-based statistical tests, which are capable of detecting and localizing parametric structural changes. The main contribution is the analytical calculation of sensitivity vectors for changes in boundary conditions (i.e., changes in prestress or support conditions) based on stress stiffening, and the combination with a numerically efficient algorithm, i.e. Nelson’s method. One of the main advantages of the employed damage diagnosis algorithm is that, although it uses physical models for damage detection, it considers the uncertainty in the data-driven features, which enables a reliability-based approach to determine the probability of detection. Moreover, the algorithm can be trained and the probability of detecting future damages can be predicted based on data from the undamaged structure—in an unsupervised learning mode—making it particularly relevant for unique structures, where no data from the damaged state is available. For proof of concept, a numerical case study is presented. The study assesses the loss of prestress in a two-span reinforced concrete beam and showcases suitable validation approaches for the sensitivity calculation.

Structural health monitoring of railway bridges using innovative sensing technologies and machine learning algorithms: a concise review

Abstract Railway bridges are a vital element of railway infrastructures, and their safety can directly affect the regional economy and commuter transportation. However, railway bridges are often subjected to severe loading and working conditions, caused by rising traffic levels and heavier vehicles, and increases in train running speeds makes the bridges extremely susceptible to degradation and failure. One of the promising tools for evaluating the overall safety and reliability of railway bridges is the bridge structural health monitoring (SHM) system, which not only monitors the structural conditions of bridges and maintains the safety of train operations, but also helps to expand the lifespan of bridges by enhancing their durability and reliability. While a multitude of review papers on SHM and vibration-based structural damage detection methods have been published in the past two decades, there is a paucity of literature that provides a review or overview on the SHM of railway bridges. Some of the review papers have become obsolete and do not reflect the state-of-the-art research. Therefore, the main goal of this article is to summarize state-of-the-art SHM techniques and methods that have been widely used and popular in recent years. First, two state-of-the-art SHM sensing technologies (i.e. fiber optic sensing (FOS) technology and computer vision-based (CV) technology) are reviewed, including the working principles of various sensors and their practical applications for railway bridge monitoring. Second, two state-of-the-art machine learning algorithms (i.e. convolutional neural networks (CNN) and transfer learning (TL)) and their applications for railway bridge structural condition assessment are exemplified. Third, the principle of digital twin (DT) and its applications for railway bridge monitoring are presented. Finally, issues related to the future direction and challenges of the monitoring technologies and condition assessment methods of railway bridges are highlighted.

Detection of transversal cracks in prismatic cantilever beams with weak clamping using machine learning

Because our infrastructure is aging and approaching the end of its intended functioning time, the detection of damage or loosening of joints is a topic of high importance in structural health monitoring. The most desired way to assess the health of engineering structures during operation is to use non-destructive vibration-based methods that can offer a global evaluation of the structure’s integrity. A comparison of using different modal data for training feedforward backpropagation neural networks for detecting transverse damages in beam-like structures that can also be affected by imperfect boundary conditions is presented in the current paper. The different RFS, RFSmin, and DLC training datasets are generated by applying an analytical method, previously developed by our research team, that uses a known relation, based on the modal curvature, severity estimation of the transverse crack, and the estimated severity for the weak clamping. The obtained dataset values are employed for training three feedforward backpropagation neural networks that will be used to locate transverse cracks in cantilever beams and detect if the structure is affected by weak clamping. The output from the three ANN models is compared by plotting the calculated error for each case.

Dictionary learning-based damage detection under varying environmental conditions using only vibration responses of numerical model and real intact State: Verification on an experimental offshore jacket model

Monitoring structural damage is critical for preserving the service life of engineering systems. In varying operational environments, the working loads are changing all the time and they are typically unknown; in such environments, access to damage data is difficult and sometimes even impossible, and generally, intact data of the system is available. From this standpoint, this study aims to propose a novel vibration-based method for damage detection of real systems using Dictionary Learning (DL) based on a FE model and real intact state under different uncertainties such as varying working loads. A Frequency Domain Sparse Representation-based Classification (FDSRC) model is designed via DL to learn the features and damage detection of real systems. The main contribution of this study is the training process of the proposed FDSRC using the raw frequency data of a real intact system and various states of the related Finite Element (FE) model under simple working loads (one working load), which is then tested using the raw frequency data of the various states of the real system under complex working loads (varying working loads), to make more realistic assumptions and simulate the operational conditions. The raw frequency data is generated using the Frequency Domain Decomposition (FDD) method. The performance of the proposed method is validated using real measurements from an experimental offshore jacket model. The results of the study indicate that in the case of changing the working load, the proposed method can achieve higher accuracy than other comparative methods. As a result, the proposed method is robust for structural damage monitoring under different uncertainties.

Using Deep Neural Networks for On-Load Tap Changer Audio-Based Diagnostics

This paper proposes a sound separation methodology based on deep learning neural network (DLNN) to extract useful diagnostic material in non-invasive audio-based On-Load Tap Changer (OLTC) diagnostics. The proposed methodology has been experimentally verified on both artificial mixtures (created by reproducing the targeted data by the speakers placed next to the active transformers) and actual mixtures (recorded by the microphone during OLTC live operation in the field). The results show that the method produces high-quality estimates (correlation to referent fingerprints <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\rho &gt; 0.9$</tex-math></inline-formula> ) compared to other sound separation methods, e.g. Independent Component Analysis (ICA). The proposed framework can also perform source separation from a monaural mixture (mixture recorded with a single microphone only), which is impossible for ICA methods. Moreover, the results show that DLNN trained with healthy OLTC data produces diagnostically valuable estimates even when fed with a faulty OLTC audio mixture. For that reason, once trained, the DLNN can produce the diagnostic signal estimates from monaural mixtures that can be used with existing vibration-based diagnostic methods.

Vibration-Based Damage Detection Using Finite Element Modeling and the Metaheuristic Particle Swarm Optimization Algorithm

The continuous development of new materials and larger and/or more complex structures drives the need for the development of more robust, accurate, and sensitive Structural Health Monitoring (SHM) techniques. In the present work, a novel vibration-based damage-detection method that contributes into the SHM field is presented using Metaheuristic algorithms coupled with optimal Finite Element Models that can effectively localize damage. The proposed damage-detection framework can be applied in any kind of detailed structural FE model, while requiring only the output information of the dynamic response of the structure. It can effectively localize damage in a structure by highlighting not only the affected part of the structure but also the specific damaged area inside the part. First, the optimal FE model of the healthy structure is developed using appropriate FE model updating techniques and experimental vibration measurements, simulating the undamaged condition. Next, the main goal of the proposed method is to create a damaged FE model that approximates the dynamic response of the damaged structure. To achieve this, a parametric area is inserted into the FE model, changing stiffness and mass to simulate the effect of the physical damage. This area is controlled by the metaheuristic optimization algorithm, which is embedded in the proposed damage-detection framework. On this specific implementation of the framework, the Particle Swarm Optimization (PSO) algorithm is selected which has been used for a wide variety of optimization problems in the past. On the PSO’s search space, two parameters control the stiffness and mass of the damaged area while additional location parameters control the exact position of the damaged area through the FE model. For effective damage localization, the Transmittance Functions from acceleration measurements are used which have been shown to be sensitive to structural damage while requiring output-only information. Finally, with proper selection of the objective function, the error that arises from modeling a physical damage with a linear damaged FE model can be minimized, thus creating a more accurate prediction for the damaged location. The effectiveness of the proposed SHM method is demonstrated via two illustrative examples: a simulated small-scale model of a laboratory-tested vehicle-like structure and a real experimental CFRP composite beam structure. In order to check the robustness of the proposed method, two small damage scenarios are examined for each validation model and combined with random excitations.

Estimations of the elastic moduli of concrete at different temperatures and humidities by mesomechanics methods

Changes of environmental temperature and humidity affect the elastic modulus of concrete, which makes the vibration-based bridge health monitoring method unreliable. This research studied the effects of environmental temperature and moisture content on the effective elastic modulus of concrete by the mesomechanics methods. In meso level, for the two components of concrete, pores and micro cracks, effects of water in them on the effective elastic modulus at different temperatures were studied separately. Then estimations of the effective elastic moduli at different temperatures and moisture contents were proposed. To verify this, the calculated effective elastic moduli were compared with the experimental values. Finally, the mechanism of the variations of the effective elastic moduli with temperature was revealed. The proposed estimations of the effective elastic modulus can offer a simple and reliable approach to obtain the accurate elastic modulus of concrete based on the environmental temperature and concrete moisture content, which can eliminate their effects in bridge health monitoring.

Comparative study of stator current-based and vibration-based methods for railway traction motor bearing cage fault diagnosis at high-speed condition

Traction motor’s rolling element bearings are one of the most critical components for the structural health monitoring of high-speed train vehicles. Most structural health monitoring techniques for railway rolling element bearings are using vibration signal. However, the vibration characteristic of cage defect is difficult to be transmitted to external monitoring point owing to the location of cage in bearing. Hence, the cage defect of Railway Traction Motor Bearing (RTMB) is usually difficult to be recognized with motor housing vibration acceleration signal. Considering Induction Motor (IM) itself a sensor, the mechanical imbalance of bearing cage defect can reflect in stator current through the motor gap magnetic field rather than a solid transmission path. A novel strategy with stator current for monitoring the traction motor bearing cage health status in High-Speed Train (HST) is proposed in this paper. This method requires no additional sensor in the practical application. To prove the strategy is feasible and more effective than vibration-based method for the cage fault diagnosis of RTMB, a comprehensive experiment investigation with a real railway traction motor of a HST is completed. Artificial cage defects are processed on bearings at both ends of the induction motor, respectively. Three high-speed (≥250 km/h) conditions are observed. The data analysis results show that vibration-based method is almost invalid at high-speed condition and the proposed current-based method is feasible and effective for the fault diagnosis of high-speed railway traction motor bearing cage.

Structural damage detection via phase space based manifold learning under changing environmental and operational conditions

The feasibility and performance of existing vibration-based damage detection methods to real world civil engineering structures are inevitably affected by the varying environmental and operational conditions. Reliable damage detection methods with damage features that are sensitive to structural condition change but robust to environmental and loading effects are desirable for practical applications. This paper proposes a novel structural damage detection approach based on manifold learning for the effective condition assessment of real-world structures under environmental and operational conditions. The phase space representation of the vibration characteristics is reconstructed using the identified natural frequencies of structures. Then, the intrinsic nonlinear manifold between the environmental variables and natural frequencies in the high dimensional phase space is projected to a low-dimensional representation via manifold learning. The Gaussian process regression technique is introduced to extract reliable damage index from the learned manifold structure. The effectiveness and superiority of the proposed approach are demonstrated by two real-world engineering structures, that is, the Dowling Hall Footbridge and Z24 bridge. Damage detection results obtained from the proposed approach are compared with those from the current state-of-the-art Kernel PCA method, which is a representative nonlinear dimensionality reduction method to alleviate the environmental effects. The results demonstrate that the proposed approach is sensitive to structural damage but insensitive to changes in environmental and operational conditions. More importantly, the nonlinear environmental effects can be efficiently characterized by the proposed approach, using only partial datasets with environmental variations in the training datasets.

Low-story damage detection of buildings using deep neural network from frequency phase angle differences within a low-frequency band

Lower stories of buildings are much more vulnerable due to the critically dead load from high stories. Vibration-based damage detection methods are commonly used to quantify damage with indicators and early assess structural deterioration. However, these indicators are usually not directly related to structural performance and can be distorted by the modeling errors or sensing uncertainties. In addition, damage detection of structures can be realized from sensor data by the remaining useful life estimation framework in machine learning. Meanwhile, a machine learning model, which detects structural damage, can be trained by the generated data from a numerical/identified model, e.g., phase angle differences of structural transfer functions. Moreover, the bounded nature of phase angles coincides with the needs of machine learning algorithms, and the combined stiffness and damping changes involved in damaged structures can also be considered in a learning model. Therefore, this study proposes a neural network damage detection method based on frequency response phase angle differences for low-story damage of buildings. In this proposed method, a simplified model is established from identified modal parameters of a structure and optimized using the least-squared stiffness approach, and the transfer function phase angle differences are generated from the model considering the stiffness and damping changes in various damage scenarios. The generated data are exploited to train a deep learning model which predicts the residual stiffness percentages as the condition parameters in the remaining useful life estimation framework. In this study, a numerical example provides detailed procedure to develop the neural network damage detection model and evaluate the effectiveness of the learning model. The proposed method is also employed in a scaled six-story steel-frame building for experimental verification. As seen in the results, the neural network damage detection method can estimate the residual stiffness percentages in the lower stories of a building based on frequency response phase angle differences.

Review of Recent Automated Pothole-Detection Methods

Potholes, a kind of road defect, can damage vehicles and negatively affect drivers’ safe driving, and in severe cases can lead to traffic accidents. Efficient and preventive management of potholes in a complex road environment plays an important role in securing driver safety. It is also expected to contribute to the prevention of traffic accidents and the smooth flow of traffic. In the past, pothole detection was mainly performed via visual inspection by human experts. Recently, automated pothole-detection methods apply various technologies that converge basic technologies such as sensors and signal processing. The automated pothole-detection methods can be classified into three types according to the technology used in the pothole-recognition process: a vision-based method, a vibration-based method, and a 3D reconstruction-based method. In this paper, three methods are compared, and the strengths and weaknesses of each method are summarized. The detection process and technology proposed in the latest research related to automated pothole detection are described for each method. The development plans of future technology that is connected with those studies are also presented in this paper.

Review on quick safety assessment of building structures in complex urban environment after extreme explosion events

Extreme explosion events result in demands for emergency rescue service. From the civil engineering perspective, a quick safety assessment of building structures in the explosion’s vicinity will provide the emergency rescue committee with concrete support to make scientific decisions. In this paper, three primary issues, namely, inverse analysis of explosive characteristics, blast wave propagation in complex urban areas and blast-induced damage identification, are reviewed. These are often performed stepwise and form a multi-step whole to assist the emergency rescue service. The paper begins by introducing the inverse analysis of explosives based on craters, building damages and seismic or acoustic records. In this step, explosive characteristics, for example, charge type, original time, yield and location, could be produced and input into blast load calculation in the next step. Then, the existing literature on blast wave propagation and blast load determination is presented with close attention to complex urban environments. It shows that the current study remains in its infancy and relies on advancement in computational fluid dynamics (CFD). Besides, pressure–impulse (P-I) diagrams which predict the structural damage based on the calculated blast loads are illustrated. Onsite damage detection techniques, such as visual inspection, non-destructive testing (NDT) and vibration-based methods, are also discussed. The paper ends with a discussion of the shortcomings of previous work and the outlooks of further work.

Microvibration-based orderly redistribution of wear particles in lubricating oil

On-line monitoring of wear particles in lubricating oil has become an important requirement for engine health management. This paper proposes a new online analysis method for oil wear particles. Exciting with micro-vibration, wear particles in the oil are redistributed on the surface of the vibrating film according to their size such that the collection and analysis of wear particles is realized in an orderly manner. A monolayer horizontal vibrational particle separation model was developed. The distribution law of wear particles in multi-physics field is theoretically deduced. Both a numerical simulation model and a simplified experimental system are established to illustrate the redistribution process of wear particles with different sizes. The numerical and experimental results show that the micro vibration-based redistribution method can separate the aggregation bands of different wear particles under the excitation of a certain frequency.

Damage detection on the blade of an operating wind turbine via a single vibration sensor and statistical time series methods: Exploring the performance limits of robust methods

The study aims at damage detection on the blade of an operating Vestas V27 wind turbine via a single vibration response sensor under varying Environmental and Operating Conditions (EOCs), and through this, at exploring the performance limits of robust vibration-based Statistical Time Series type methods. Three trailing edge progressive, 15 cm, 30 cm, and 45 cm long, damage scenarios are examined using signals from a 104-day-long measurement campaign. The study is based on three robust methods: an Unsupervised Principal Component Analysis AutoRegressive model–based (U-PCA-AR) method, an Unsupervised Multiple Model AutoRegressive model–based (U-MM-AR) method, and an Unsupervised Principal Component Analysis Multiple Model AutoRegressive model–based (U-PCA-MM-AR) method. Comparisons with a nominal Unsupervised AutoRegressive model–based (U-AR) method and an 8-sensor–based method are also reported. The results of the study are based on a systematic and thorough assessment procedure using 7000 inspection experiments and confirm that single-sensor-based detection is indeed feasible via the aforementioned robust methods, with the best performance achieved by the U-PCA-MM-AR method which reaches up to 100% True Positive Rate at 4%, 1%, and 0% False Positive Rate for the 15, 30, and 45 cm damage scenarios, respectively. This performance is on par with that of the 8-sensor–based method and indicative of the high capabilities offered by the robust vibration-based Statistical Time Series type methods. The sensitivity of the methods with respect to the sensor location and the AutoRegressive model order is also examined.

Structural damage identification employing hybrid intelligence using artificial neural networks and vibration-based methods

The basis of vibration-based damage detection method is that when there are changes in physical properties of a structure, there will also be changes in vibration characteristics such as natural frequencies, mode shapes and damping ratios. Artificial neural network has become one of the most powerful approaches capable of pattern recognition, classification, and nonlinear modelling employing computational intelligence techniques to tackle damage detection as a complex problem. In this paper, ensemble neural networks based damage identification techniques were developed and applied for damage localization and severity identification in I-beam structures using dynamic parameters. Experimental modal analysis and numerical simulations of I-beam with triple-point damage cases were carried out to generate the natural frequencies and mode shapes of structures. In the procedure of damage identification, five different neural networks corresponding to mode 1 to mode 5 were constructed, and then an approach based on a neural network ensemble was proposed to combine the outcomes of the individual neural networks into a single network. The ensemble neural network has the superiorities of all the individual networks from different vibrational modes. The ensemble network produced better damage identification outcomes than the individual networks and the results revealed the ability of the ensemble neural networks to identify damage.