Structural Vibration Measurements Research Articles

Flexible curved multi-functional sensors for air flow and vibration signal measurement of train bogie

Abstract The fluid-solid coupling effects with the aerodynamic noise and structural vibration signal have great part in measurement accuracy and fault diagnosis of the train bogie. It is important to collect aerodynamic noise and structural vibration data for safety evaluation and on-line structural health monitoring (SHM) of the bogie, which brings new challenges to the integration and installation of the sensors in the limited space and complicated operation condition of the train bogie. Flexible curved multifunctional sensors (FCMFS) integrated with the hot film and structural vibration sensor units is designed to the air flow and structural vibration measurement of the train bogie. Bogie vibration and wind tunnel test platforms with FCMFS are built to the air flow velocity and vibration data measurement on the bogie surface under different operation conditions. Piezoelectric Polyvinylidene fluoride(PVDF) sensors have significant output in the wind tunnel and vibration experiments, and the hot film sensor is only sensitive to the air flow signal. The effect of vibration signal to the hot film sensor could be ignored, which is applied to decouple the aerodynamic noise and structural vibration signals for improving the measurement accuracy of the train bogie. The dynamic performance of FCMFS is verified in Rail Transmit Base of East China Jiaotong University, which is applied to SHM and rapid fault diagnosis of train bogie. The air flow distribution and vibration data are collected to safety assessment and fault diagnosis of train bogie, which would promote the intelligent maintenance level of the train bogie.

Global calibration method for multi-view-based vibration measurement of large structures

A multi-view global calibration method is proposed to overcome the limitations of overlapping regions in multi-view structural vibration measurement. This method establishes a mapping relationship from image points to experimental model points at the outset of vibration measurement, enabling a one-time global calibration of multi-view vibration response data. Furthermore, an image point detection method based on shape-function matching is designed to improve global calibration accuracy. Experimental results demonstrate that the proposed method excels in point detection and global calibration. Finally, the feasibility of this method is validated through vibration experiments on large aircraft fuselage structures.

Camera-Based Dynamic Vibration Analysis Using Transformer-Based Model CoTracker and Dynamic Mode Decomposition.

Accelerometers are commonly used to measure vibrations for condition monitoring in mechanical and civil structures; however, their high cost and point-based measurement approach present practical limitations. With rapid advancements in computer vision and deep learning, research into tracking the motion of individual pixels with vision cameras has increased. The recently developed CoTracker, a transformer-based model, has demonstrated excellence in motion tracking, yet its performance in measuring structural vibrations has not been fully explored. This paper investigates the efficacy of the CoTracker model in extracting full-field structural vibrations using cameras. It is initially applied to capture the dense point movements in video sequences of a cantilever beam recorded using a high-speed camera. Subsequently, modal analysis using delay-embedding dynamic mode decomposition (DMD) is conducted to extract modal parameters including natural frequencies, damping ratios, and mode shapes. The results, benchmarked against those from a reference accelerometer and the Finite Element Method (FEM) result, demonstrate CoTracker's high potential for general applicability in structural vibration measurements.

Structural health monitoring on offshore jacket platforms using a novel ensemble deep learning model

Monitoring health condition of offshore jacket platforms is crucial to prevent unexpected structural damages, where a prevailing challenge involves translating available feature information into structural damage patterns. Although the artificial neural network (ANN) models are popular in addressing this challenge, they often fail to capture the temporal correlations between the feature information and the damage patterns, which reduce their capability for discovering the laws governing the structural damage detection. To bridge this research gap, this study proposes a novel ensemble deep learning model to enhance the temporal feature extraction to improve the damage pattern identification. In this approach, a one-dimensional Convolutional Neural Network (CNN) extracts the spatiotemporal features from the structural vibration measurements. Simultaneously, a SENet attention mechanism is introduced to select the most informatic features. Subsequently, a bidirectional long short-term memory network (BiLSTM) is employed to learn the mapping between the extracted features and the structural damage patterns. Furthermore, the particle swarm optimization (PSO) algorithm is used to optimize the BiLSTM hyperparameters to enhance its stability and reliability. Both simulations and experiments are carried out to collect the vibration responses of the offshore jacket structure in different damage scenarios. The analysis results demonstrate that the proposed method produces remarkable improvement with respect to the accuracy and robustness in identifying the structural damages when compared with the ANNs. The overall detection accuracy of the proposed CNN-BiLSTM-Attention ensemble model is beyond 95%, which provides strong applicability to practical structural health monitoring of offshore platforms.

К вопросу о развитии методов измерения вибрации строительных конструкций

Vibration processes contain useful information about the properties of both the signal source and the medium in which they propagate. Control of vibration parameters of structures is an integral part of monitoring. There are various methods and means of measuring vibration processes in building structures, as well as their registration. The development of technology and electronics allows us to steadily improve the parameters of vibration sensors, reduce their dimensions and create fundamentally new sensitive elements. Examples are given of vibration measuring equipment used by the staff of the Institute of Continuous Media Mechanics for testing large-scale building structures over the past 25 years. The results of vibration measurements of large-scale structures are shown, as well as the evolution of the natural frequency of a building structure during its long-term monitoring.

Damage detection of offshore jacket structures using structural vibration measurements: Application of a new hybrid machine learning method

The artificial intelligence (AI) technologies, such as meta-heuristic computing and deep learning, have provides solid technical support for structural health monitoring (SHM) of offshore jackets. In this paper, a physics-enhanced AI method based on the parametric damage identification is developed for SHM of the offshore jacket structures. In this new method, a hybrid kernel function-based kernel extreme learning machine (HKELM) is proposed to construct an AI structure to enhance the SHM detection capacity on the structural modal parameters extracted by the parametric damage identification technique. Simulation analysis is carried out to verify the feasibility of the HKELM-based method using the response signals of a jacket structure under impact force, and the result demonstrates good damage location capability of the proposed method. Furthermore, to select proper parameters for the HKELM, the whale optimization algorithm (WOA) is applied to optimize the values of the regularization coefficient and kernel parameter array. Then, the wavelet denoising (WD) is introduced to preprocess the vibration signals to improve the damage detection ability of the WOA-HKELM. Lastly, experimental tests are performed to validate the effectiveness of the proposed method in utilizing the structural modal parameters for identifying the structural damage. The analysis results illustrate that the proposed method produces satisfactory damage location ability under the influence of actual noise in the vibration signals. Meanwhile, this method has broad application prospects in the SHM of other offshore structures.

Super-sensitivity full-field measurement of structural vibration with an adaptive incoherent optical method

Full-field measurements of structural vibrations have been achieved by incoherent optical methods with video cameras such as digital image correlation and optical flow. In contrast to coherent optical methods such as scanning laser vibrometers, incoherent optical methods are low-cost and easy to set up. However, the sensitivity (minimum measurable displacement) of incoherent optical methods is generally lower than that of coherent optical methods. Typically, the sensitivity of incoherent optical methods is essentially limited by the finite bit depth of the digital camera due to the quantization with round-off errors. Quantitatively, this theoretical sensitivity limit is determined by the bit depth B as δp=1/(2B−1) [pixel] which corresponds to a displacement causing an intensity change of one gray level. Fortunately, natural dithering may be leveraged to overcome the quantization and exceed the sensitivity limit, achieving super-sensitivity.In this work, we first study the mechanism of the sensitivity limit induced by the quantization and the critical limitation of existing super-sensitivity methods in the full-field measurement of structural vibration. Addressing such a limitation, we present a general adaptive weighted averaging method with dithering, achieving super-sensitivity incoherent optical measurement of full-field deformational vibration of flexible structures. Specifically, both spatial and temporal samplings of the pixels are leveraged simultaneously to adaptively identify the motion (deformation) shape of the structure by principal component analysis (PCA) of spatiotemporal pixel intensities. Then, the identified deformation shape is used to perform a deformation-shape-weighted averaging over spatial pixels with dithering, revealing the sub-sensitivity displacement while retaining spatial resolution at full field. Numerical simulations and laboratory experiments are performed for principle explanation and method validation. Particularly, we derive a mathematical model of the minimum measurable vibration amplitude A∗ for the developed super-sensitivity method, which is found to be determined by the number of spatial pixels for averaging Ns and the noise level σn in the imaging system. Thus, this work provides, for the first time, a rigorous quantification of the sensitivity of incoherent optical measurement for structural vibration, and a new quantitative method to achieve super-sensitivity full-field measurement of structural vibration.

Vision-based real-time structural vibration measurement through deep-learning-based detection and tracking methods

Structural vibration measurement is crucial in structural health monitoring and structural laboratory tests. Traditional contact type sensors are usually required to be attached to the test specimens, which may be difficult to install, and may affect the structural properties and response. Non-contact type wireless sensors are usually expensive and require specialized workers to install and operate. In recent years, vision-based tracking methods for structural vibration measurement have gained increasing interests due to their high accuracy, non-contact feature and low cost. However, traditional vision-based tracking algorithms are susceptible to external environmental conditions such as illumination and background noise. In this paper, two real-time methods, YOLOv3-tiny and YOLOv3-tiny-KLT, are proposed to track structural motions. In the first method, YOLOv3-tiny is established based on the YOLOv3 architecture to localize customized markers where structural displacements are directly determined from the bounding boxes generated. The second method, YOLOv3-tiny-KLT, is a more advanced method which combines the YOLOv3-tiny detector and the traditional KLT tracking algorithm. The pretrained YOLOv3-tiny is deployed to localize the targets automatically, which will then be tracked by Kanade‐Lucas‐Tomasi algorithm. YOLOv3-tiny is intended to provide baseline vibration measurement when the KLT tracking gets lost. The proposed methods were implemented for the videos of shake table tests on a two-storey steel structure. Parametric studies were conducted for the YOLOv3-tiny-KLT method to examine its sensitivity to the tracking parameters. The results show that the proposed method is capable of achieving real-time speed and high accuracy, when compared with the traditional displacement sensors including linear variable differential transducer (LVDT) and String Pots. It is also found that the combined YOLOv3-tiny-KLT approach achieves higher accuracy than YOLOv3-tiny only method, and higher robustness than KLT only method against illumination changes and background noise.

Damage Detection in Reinforced Concrete Member Using Local Time-Frequency Transform Applied to Vibration Measurements

Signal processing and analysis of structural vibration measurements are key components of structural damage detection (SDD) in structural health monitoring (SHM). The goal of signal processing is to extract subtle changes in the measured signals, which can be used to infer changes in structural parameters and damage. Time-frequency analysis is one of the most popular characterization methods for studying non-stationary vibration signals. In this article, the local time-frequency transform (LTFT) is applied and evaluated to calculate the time-domain signals because of its excellent time-frequency energy distribution properties. The LTFT matches the input data by the Fourier basis in an inverse problem framework and uses the least squares method to solve the time-varying Fourier coefficients. Subsequently, it defines the time-frequency spectrum as the calculated time-varying Fourier coefficients. While the LTFT has been used in the field of geophysics for seismic data processing, its application to structural vibration signals is novel. Both synthetic signals as well as signals collected from a large-scale laboratory test of a reinforced concrete girder were processed with the LTFT and compared with Rényi entropy for quantifying the time-frequency spectrum, the time-frequency resolution abilities of short time Fourier transform (STFT), and S transform (ST). The results show that the LTFT is superior to the traditional time-frequency analysis schemes, in that it is more effective in identifying the energy changes in the time-frequency spectrum before and after structural damage in the form of cracking has occurred. At the same time, it provides high-precision time-frequency resolution and excellent noise suppression abilities. The effectiveness and feasibility of the LTFT applied to the synthetic and experimental signals are verified.

A framework for computer vision-based health monitoring of a truss structure subjected to unknown excitations

Computer vision (CV) methods for measurement of structural vibration are less expensive, and their application is more straightforward than methods based on sensors that measure physical quantities at particular points of a structure. However, CV methods produce significantly more measurement errors. Thus, computer vision-based structural health monitoring (CVSHM) requires appropriate methods of damage assessment that are robust with respect to highly contaminated measurement data. In this paper a complete CVSHM framework is proposed, and three damage assessment methods are tested. The first is the augmented inverse estimate (AIE), proposed by Peng et al. in 2021. This method is designed to work with highly contaminated measurement data, but it fails with a large noise provided by CV measurement. The second method, as proposed in this paper, is based on the AIE, but it introduces a weighting matrix that enhances the conditioning of the problem. The third method, also proposed in this paper, introduces additional constraints in the optimization process; these constraints ensure that the stiff ness of structural elements can only decrease. Both proposed methods perform better than the original AIE. The latter of the two proposed methods gives the best results, and it is robust with respect to the selected coefficients, as required by the algorithm.

Target-free 3D tiny structural vibration measurement based on deep learning and motion magnification

• A novel target-free tiny vibration displacement measurement approach is proposed. • It is based on deep learning and motion magnification techniques. • Binocular vision system is used for measuring 3D vibration measurement. • Deep learning techniques are used for key-points detection, matching and tracking. • Experimental and in-field studies are conducted to validate the accuracy. In the field of structural health monitoring (SHM), computer vision based methods have been usually developed for 2-dimensional (2D) vibration displacement measurement and crack identification of civil engineering structures. However, the accurate measurement of tiny 3-dimensional (3D) vibrations for real civil engineering structures remains a very difficult task. To overcome this challenge, this paper proposes a target-free full-field 3D tiny vibration measurement approach for civil engineering structures by using a binocular vision system. The proposed approach is based on deep learning and motion magnification. A phase-based video motion magnification algorithm is employed to achieve a high measurement accuracy of tiny vibrations at the submillimeter level. The advanced key point detection, matching and tracking algorithms via deep learning techniques are employed to achieve target-free tiny vibration displacement measurement. The accuracy and performance of the proposed approach are evaluated through experimental tests on a steel cantilever beam in the laboratory. In-field experimental tests are conducted on a pedestrian bridge on a university campus to investigate the accuracy of the proposed approach in practical applications. The measured 3D tiny vibration displacement from the proposed approach is compared with those measured by laser displacement sensors, and the derived acceleration responses are compared with those measured from the installed accelerometers on the testing structures. The results demonstrate that the 3D tiny vibration measurements are obtained accurately by the proposed approach.

Modal Parameter Identification of Time-Varying and Weakly Nonlinear Systems Based on an Improved Empirical Envelope Method

The empirical envelope (EE) method based on the amplitude-modulation and frequency-modulation (AMFM) decomposition is effective for identifying the modal parameters of time-varying and weakly nonlinear systems. However, the identification accuracy of the EE method is sensitive to noises which often exist in vibration measurements of real structures. In this study, an improved empirical envelope (IEE) method is proposed to achieve robust modal parameter identification from noisy measurements. Specifically, the idea of sliding window threshold denoising is introduced to reduce the error in the instantaneous envelope induced by abnormal extreme points, and a moving average filter is utilized to reduce the error in the instantaneous frequency induced by high-frequency noises. Two numerical examples and an experimental example of a full-bridge aeroelastic model are analyzed to validate the accuracy of the IEE method and highlight the superiority of the IEE method relative to the original EE method. It is concluded that the IEE method is robust to measurement noises (the considered signal-to-noise ratios include 5–90[Formula: see text]dB) and that the IEE method is more accurate than the EE method. Hence, the IEE method serves as a promising alternative for modal parameter identification of time-varying and weakly nonlinear systems.

Chatter Monitoring of Machining Center Using Head Stock Structural Vibration Analyzed with a 1D Convolutional Neural Network.

Real-time chatter detection is crucial for the milling process to maintain the workpiece surface quality and minimize the generation of defective parts. In this study, we propose a new methodology based on the measurement of machine head stock structural vibration. A short-pass lifter was applied to the cepstrum to effectively remove components resulting from spindle rotations and to extract structural vibration modal components of the machine. The vibration modal components include information about the wave propagation from the cutter impact to the head stock. The force excitation from the interactions between the cutter and workpiece induces structural vibrations of the head stock. The vibration magnitude for the rigid body modes was smaller in the chatter state compared to that in the stable state. The opposite variation was observed for the bending modes. The liftered spectrum was used to obtain this dependence of vibration on the cutting states. The one-dimensional convolutional neural network extracted the required features from the liftered spectrum for pattern recognition. The classified features allowed demarcation between the stable and chatter states. The chatter detection efficiency was demonstrated by application to the machining process using different cutting parameters. The classification performance of the proposed method was verified with comparison between different classifiers.

Auralizing comparable structural vibration measurements to inform design decisions

The design challenges presented by locating high-impact noise-producing athletic spaces, such as weight rooms and gymnasia adjacent to other occupied spaces are compounded by the difficulty in conveying the resulting experience through narratives or numeric performance criteria. As auralization tools can very effectively convey experiences in room acoustics and sound isolation, that effectiveness prompted an exploration into its possible use for conveying the experiences of impact-induced noise transfer resulting from difficult adjacencies. However, realistic and accurate auralization of a structural vibration path offers far more complex challenges than typical auralizations. For example, structural modeling for building design typically does not reach the resolution required for auralizations, if performed at all. Performance data for comparative floor types typically use tapping machines to comply with the Impact Isolation Class (IIC), which does not represent the response low-frequency content induced by heavy impacts. This paper dissects a case study of a method developed to address these challenges presenting a comparative auralization between athletic and gymnasium floor underlayment systems, enabling a client to experience the relative differences to aid design decisions. This included a method of presenting structural vibration with realistic source noise components and room effects to achieve a full soundscape.

Speckle noise reduction for structural vibration measurement with laser Doppler vibrometer on moving platform

Speckle noise is a major problem for structural vibration measurements with Laser Doppler vibrometer on moving platform (LDVom) due to its highly random, frequent, and broadband nature, especially at high speeds. This paper develops a new post-processing framework to reduce speckle noise based on a case study of LDVom measurements on railway tracks. First, the characteristics of the speckle noise are studied. As the speed increases, the speckle noise occurs more frequently, with shorter intervals, shorter durations, greater amplitudes, and broader frequency bands. Then, a three-step despeckle framework is proposed, consisting of spike detection, imputation, and smoothing. This framework works by detecting and replacing spikes, recovering false positives, and smoothing false negatives and residual noise. To showcase this framework, we use a wavelet-based method for Step 1, an ARIMA-based method for Step 2, and a Butterworth filter for Step 3. Besides, the parameter selection strategies and the alternative methods are discussed. Next, the methods are validated through qualitative comparison and quantitative evaluation using a Monte Carlo-based strategy. We demonstrate that the proposed methods effectively reduce the speckle noise at speeds of at least 20 km/h while avoiding the pseudo vibrations. Finally, we show that the LDVom successfully captures the track vibrations at dominant frequencies of 500 ∼ 700 Hz with good repeatability between different laps and good agreement with trackside measurements.

Augmenting a single-point laser Doppler vibrometer to perform scanning measurements

Laser Doppler vibrometers (LDV) are used for non-contact vibration measurements of various structures and are frequently used for stringed instrument measurements. Single-point LDVs can be used with the roving hammer or LDV method for mode shape measurements, but this is time-consuming and requires constant attention. Scanning LDVs exist but are expensive and often out of reach of musical acoustics researchers. An inexpensive apparatus to modify a common single-point LDV such that it can perform automated scanning measurements is presented. The augmentation consists of a mirror galvanometer, impact hammer controller, and 3D printed mounting hardware. The scanning system is controlled by a microprocessor and can be easily automated. The total cost of the system, excluding the LDV and impact hammer, is under two hundred dollars. Measurements of guitars are presented to validate the scanning system and discuss any shortcomings.

Marker‐free vision‐based method for vibration measurements of RC structure under seismic vibration

AbstractThe seismic vibration measurement of reinforced concrete (RC) structures is an important aspect of post‐disaster reconstruction. Unfortunately, the existing method of installing sensors on walls and columns is prohibitively expensive, and the measurement signals are prone to loss during seismic vibration. In this paper, a marker‐free measurement method based on the Lucas‐Kanade (L‐K) optical flow algorithm for vibration measurements of RC structures under seismic vibration has been developed. In addition, to improve the accuracy of L‐K optical flow in dealing with the RC structure under seismic vibration, a new approach for selecting the feature point that is used to solve the restrictions of manual marking, as well as an adaptive matched window strategy that uses different sizes of matched window along the motion to replace the fixed matched window in the original L‐K algorithm, are proposed. The precision of the proposed method is first validated by comparing it to the original L‐K optical flow algorithm. Then, compare its precision and efficiency to the existing vision‐based methods, including feature matching (FM) and digital image correlation (DIC). Finally, the behaviors of RC structures during seismic vibration, such as inter‐story drift, frequency, and strain, are investigated. The results show that, when compared to the original L‐K optical flow algorithm and other vision‐based methods, the proposed method can accurately and efficiently measure motion without spraying speckle or high contrast markers on the structure's surface, and it enables the rapid diagnosis of structures affected by earthquakes.

Prospects of Structural Damage Identification Using Modal Analysis and Anomaly Detection

Structural health monitoring (SHM) has significant importance in providing reliability, safety, and economical sensibility for structures. High sensitivity and precision of SHM techniques can be quite demanding. Some SHM testing procedures require structures, for example, wind turbine blades or aircraft structures, to be taken out of operation, which increases costs and causes downtime. Other techniques include sophisticated signal processing and data analysis, which require skilled personnel. An alternative is to use a form of system identification technique called Operational Modal Analysis, during which an operator performs structural vibration measurements without disrupting normal operation of structure. The aim of the current study is to develop a machine learning algorithm, which is able to identify damaged states of a structure, based on the assessment of its modal parameters. The assessment of the structural health is performed using machine learning technique called Anomaly Detection. The proposed method establishes a description of normality using features representing undamaged conditions and then test for abnormality which indicates presence of damage in structure. Structure under test is one-meter-long laminated composite beam. The finite element model of the beam is developed using eight-node shear-deformable shell elements. Damage in the beam is introduced as delamination between the plies representing 5% of the total area of the beam. Numerical modal analysis is carried out to obtain modal frequencies and corresponding mode shapes for the first three bending modes of both the healthy and damaged beams. The results of the numerical experiments show possibilities of the proposed structural health monitoring method to identify damage in composite structures under varying modal parameters.

Theory and method of multi-point synchronous deformation and vibration measurement based on millimeter-wave sensing

Although accelerometer-, vision-, and laser-based sensing technologies are widely used in deformation and vibration measurements, the multi-point simultaneous deformation and vibration measurement of large-scale structures with high accuracy remains a challenge. Herein, we developed a novel technology for contactless deformation and vibration measurement using millimeter-wave (mmWave) linear frequency-modulated continuous wave radar, establishing the theory and method for multi-point simultaneous deformation and vibration measurement based on mmWave sensing. The principle of mmWave multi-point vibration measurement and a method of displacement extraction were systematically described using the baseband signal model of multi-target perception. We built a prototype of the proposed mmWave vibration measurement system and a platform for simulated vibration tests. The accuracy of the mmWave multi-point vibration measurement technology was verified and compared with that of laser displacement sensors. Using the prototype system, we conducted experiments on the multi-point synchronous vibration measurement and model identification of the slender hinged flexible rod structure of a certain aerospace equipment in China and then described the dynamic response monitoring of a large bridge structure. Results showed that the deformation and vibration measurement method based on mmWave sensing developed in this work could conveniently and accurately extract the time domain information of multi-target vibration displacements. Our work provides an innovative method and approach for non-contact multi-point synchronous deformation and vibration measurements that could be applied to the mechanical performance test and health monitoring of large structures.

Structural health monitoring by probability density function of autoregressive-based damage features and fast distance correlation method

In this article, the autoregressive time series analysis is used to extract reliable features from vibration measurements of civil structures for damage diagnosis. To guarantee the adequacy and applicability of the time series model, Leybourne–McCabe hypothesis test is used. Subsequently, the probability density functions of the autoregressive model parameters and residuals are obtained with the aid of a kernel density estimator. The probability density function sets are considered as damage-sensitive features of the structure and fast distance correlation method is used to make decision for detecting damages in the structure. Experimental data of a well-known three-story laboratory frame and a large-scale bridge benchmark structure are used to verify the efficiency and accuracy of the proposed method. Results indicate the capability of the method to identify the location and severity of damages, even under the simulated operational and environmental variability.