Accurate Vibration Research Articles

Prediction of Structural Vibration Induced by Subway Operations Using Hybrid Method Based on Improved LSTM and Spectral Analysis

With the rapid expansion of urban subway networks, vibrations induced by subway operations have become an increasingly significant concern for nearby structures. To assess the influence of subway-induced vibrations on nearby structures, it is essential to predict the vibration effects accurately prior to the construction of the subway system. By combining an improved Long Short-Term Memory (LSTM) model with a spectral analysis, this paper proposes a hybrid method to enhance the accuracy and efficiency of predicting structural vibrations induced by subway operations. The improved LSTM model is composed of BiLSTM, an attention mechanism, and the DBO algorithm. The symmetry inherent in the vibration propagation paths and the structural layouts of subway systems is leveraged to improve the feature extraction and modeling accuracy. Additionally, the hybrid method utilizes the symmetric properties of vibration signals in the spectral domain to enhance prediction robustness and efficiency. Then, the hybrid method is utilized to rapidly achieve highly accurate vibration responses induced by subway operations. The verification results demonstrate the following: (1) The improved LSTM model enhances the ability to recognize patterns in time-series vibration data, leading to improved model convergence and generalization. The improved LSTM mode has a significant improvement in prediction accuracy compared to the standard LSTM network. For numerical simulation and real-world measured signals, values of R2 increased by 3% and 49.37%. (2) The proposed hybrid method significantly reduces computational time while ensuring results consistent with those obtained from the time-history analysis method. Applying the proposed hybrid method for data augmentation enhances the accuracy of the spectral analysis. The hybrid method achieves an improvement of 7% for the prediction accuracy.

Benchmark exact free vibration solutions of two-dimensional decagonal piezoelectric quasicrystal cylindrical shells

Abstract Quasicrystalline materials with piezoelectric effects show significant potential for advancing actuators, sensors and energy harvesters. In this paper, the free vibration characteristics of two-dimensional decagonal piezoelectric quasicrystal cylindrical shells (PQCSs) are investigated in the framework of symplectic mechanics system. By introducing an original vector and its dual variable vector as the fundamental unknowns, the governing equations are reduced into a set of low-order ordinary differential equations system, thus the free vibration analysis is transformed into an eigenvalue problem within the symplectic space. By using the symplectic mathematics, the exact solutions for free vibration of PQCSs are finally obtained and expanded as a series of symplectic eigensolutions. Finally, accurate natural frequency and analytical vibration mode shapes for arbitrary classical boundary conditions are obtained simultaneously. The accuracy of the obtained solutions is verified by comparing with existing results in open literature. In addition, the effects of geometrical parameters, temperature rise, external voltage and coupling fields on the natural frequency and vibration mode shapes are investigated in numerical examples. Results indicate that the phason field exhibits significant influences on the natural frequencies and cannot be neglected in free vibration analysis of PQCSs. Furthermore, all the results can be served as benchmarks for the development of new analytical and numerical approaches.

In-plane vibration analysis of elastically restrained FGM skew plates using variational differential quadrature method

This work presents an accurate in-plane vibration analysis of functionally graded material (FGM) skew plates with elastically restrained boundaries using the variational differential quadrature method (VDQM). The weak form of the governing equations is derived by integrating two-dimensional elasticity theory with Hamilton's principle. The differential and integral operators are directly converted into matrix forms, thereby removing the necessity for higher-order derivative approximations in the displacement field. Transformation matrices are then developed for both interior and boundary nodes to link the governing equations with the boundary conditions, leading to the formulation of the vibration eigenvalue equations for FGM skew plates. Various factors, including aspect ratios, skew angles, boundary restraints, and gradient indices, are considered to investigate the in-plane vibration mode characteristics of FGM skew plates. Detailed solution procedures are provided, and numerical examples using the proposed solutions indicate that the VDQM exhibits superior numerical convergence and stability compared to other existing methods. The study also investigates the influence of highly skewed plates (75°), where stress singularities arise at the corners. This aspect is crucial for in-plane vibration analysis and has garnered limited attention in the existing literature. Furthermore, the dynamic analysis of FGM skew plates reveals a coupling between normal and tangential vibration modes.

A vibration transducer framework for the measurement and reconstruction of vibration signals via low-rate sampling

This study presents a novel, cost-effective, and highly accurate computer vision-based vibration transducer framework, enabling the measurement and reconstruction of vibration signals using video frames captured at a low frame per second (fps) relative to the Nyquist rate of the measured signal. In the proposed framework, an assembly consisting of several spring-mass systems serves as the primary sensor, while a camera module tracks the motion of the seismic masses as the secondary sensor. The setup, along with the modified compressive sensing equation (CS) equation, results in a transducer scheme that can recover the base motion signals from the displacement history of seismic masses sampled at rates much lower than the maximum predominant frequency of the original signal. The performance of the proposed transducer framework has been successfully verified by numerical simulations. In addition, an experimental study has been conducted in order to validate the concept under modelling and measurement error.

Dynamic behaviors of general composite beams using mixed finite elements

A novel mixed finite element method is developed and implemented for analyzing the vibration and buckling behavior of general composite beams which consists both transversely layered and axially jointed materials. The governing state-space equations are derived using the Hamilton's principle, where both displacements and stresses are treated as fundamental variables. This semi-analytical method uses transfer relations in the transverse direction and finite element meshing in the longitudinal direction, overcoming the difficulties for general composite beams analysis and providing computational efficiency and analyzing flexibilities. The developed mixed finite element model ensures continuity of both displacements and stresses across the material interface, thereby resolving interfacial stress singularity issues and offering more reliable simulations of boundary conditions at both ends. The proposed method is formulated and validated for the free vibration and buckling analysis of general composite beams. Additionally, it is observed that material properties such as Young's modulus and density, as well as the stiffness of the interface connecting layers, have significant effects on the free vibration and buckling responses of the composite beams. Analysis of periodically distributed and bi-directional composite beams demonstrates the versatility of this method in handling two types of combination forms. The proposed method serves as a valuable reference for obtaining accurate vibration and buckling results while ensuring stress-compatibility for composite beams in practical applications.

An algorithm for large-span flexible bridge pose estimation and multi-keypoint vibration displacement measurement

Traditional visual vibration measurement methods face limitations in dynamic outdoor environments, making accurate multi-point vibration measurements challenging for the targeted objects. In this paper, a visual vibration measurement algorithm for large-span bridges is proposed by anthropomorphizing the bridge target and introducing key point detection at the same time. Firstly, the algorithm utilizes a convolutional neural network to extract multi-scale feature information from the image sequence of the bridge model. Secondly, to enhance target detection accuracy, a coordinate attention (CA) mechanism is incorporated, and the shape intersection over union (SIoU) loss function replaces the original loss function. Finally, the algorithm integrates the density-based spatial clustering of applications with noise (DBSCAN) and tracking algorithms to achieve precise localization of bridge targets. Vibration displacement data were extracted from a large-span suspension bridge structure in an outdoor environment and analyzed quantitatively and qualitatively. The vibration displacement curves obtained in this study show the highest level of agreement with the standard displacement signals, with a mean absolute percentage error of 0.5170% for the cable-stayed bridge model, 1.3822% for the Humen Bridge, and 3.2263% and 1.6982% for the Longjiang Bridge.

Dynamic examination of closed cylindrical shells utilizing the differential transform method

This article presents an innovative approach using the Differential Transform Method (DTM) to analyze the vibration characteristics of cylindrical shells, integrating Taylor's series with Sander's classical theory. It demonstrates DTM's efficiency, accuracy, and potential as an alternative method. The study introduces a novel application of the DTM in exploring the free vibration of cylindrical shells, detailing a technique to address challenges such as normalization, linear solution methodologies, and parameter derivative modifications. A dimensionless parameter analysis evaluates the impact of length, radius, thickness, and modulus of elasticity. Comparative analysis with Hybrid Finite Element Method (FEM) data and validation against existing literature highlights DTM's precision and reliability. In conclusion, DTM offers a robust solution for the eigenvalue problem in coupled differential equations, providing accurate vibration parameters. Additionally, an important relationship between the modulus of elasticity and frequency in the dimensionless state was obtained.

Accurate Free Vibration Analysis of Stepped Orthotropic Rectangular Cantilevered Plates Under the Framework of Symplectic Mechanics

Accurate Free Vibration Analysis of Stepped Orthotropic Rectangular Cantilevered Plates Under the Framework of Symplectic Mechanics

3D Vibration Analysis using Phase-Based Displacement Estimation and Stereo Cameras

Non-contact sensors are increasingly integral to the field of Structural Health Monitoring (SHM), with camera being widely utilized due to their high spatial resolution and cost-effectiveness in measuring structural displacements. Phase-based optical flow (POF) techniques have proven valuable for accurate and computationally efficient 2D vibration measurements. However, these techniques are typically confined to single-camera systems and encounter challenges in measuring vibrations in out-of-plane directions. Previous research on 3D vibration measurement has predominantly centered on pixel intensity-based techniques using stereo camera systems. Although these methods demonstrate high performance in calculating global strains and stresses, they are less suitable for accurate vibration measurement. To address these limitations, this study proposes a novel 3D vibration measurement approach that integrates POF within the context of stereo camera systems. 2D vibrations are extracted by applying Complex Gabor filters to stereo images, and subsequently, 3D vibration measurements are estimated through camera calibration and triangulation, using computed camera internal and external parameters. To validate the method, modal analysis experiments are conducted on an aluminum plate. The 3D vibration measurement results are compared with those obtained through 3D Digital Image Correlation (DIC) and contact sensors and the accuracy is demonstrated.

Data-Driven Machine Fault Diagnosis of Multisensor Vibration Data Using Synchrosqueezed Transform and Time-Frequency Image Recognition with Convolutional Neural Network

Accurate vibration classification using inertial measurement unit (IMU) data is critical for various applications such as condition monitoring and fault diagnosis. This study proposes a novel convolutional neural network (CNN) based approach, the IMU6DoF-SST-CNN in six variants, for robust vibration classification. The method utilizes Fourier synchrosqueezed transform (FSST) and wavelet synchrosqueezed transform (WSST) for time-frequency analysis, effectively capturing the temporal and spectral characteristics of the vibration data. Additionally, was used the IMU6DoF-SST-CNN to explore three different fusion strategies for sensor data to combine information from the IMU’s multiple axes, allowing the CNN to learn from complementary information across various axes. The efficacy of the proposed method was validated using three datasets. The first dataset consisted of constant fan velocity data (three classes: idle, normal operation, and fault) at 200 Hz. The second dataset contained variable fan velocity data (also with three classes: normal operation, fault 1, and fault 2) at 2000 Hz. Finally, a third dataset of Case Western Reserve University (CWRU) comprised bearing fault data with thirteen classes, sampled at 12 kHz. The proposed method achieved a perfect validation accuracy for the investigated vibration classification task. While all variants of the method achieved high accuracy, a trade-off between training speed and image generation efficiency was observed. Furthermore, FSST demonstrated superior localization capabilities compared to traditional methods like continuous wavelet transform (CWT) and short-time Fourier transform (STFT), as confirmed by image representations and interpretability analysis. This improved localization allows the CNN to effectively capture transient features associated with faults, leading to more accurate vibration classification. Overall, this study presents a promising and efficient approach for vibration classification using IMU data with the proposed IMU6DoF-SST-CNN method. The best result was obtained for IMU6DoF-SST-CNN with FSST and sensor-type fusion.

Dynamic topology optimization of two-phase viscoelastic composite structures considering frequency- and temperature-dependent properties under random excitation

This article proposes an efficient optimum design method of constrained layer damping (CLD) plates characterized by non-proportional damping subjected to random excitation. Considering frequency and temperature dependence, a two-phase viscoelastic composite material composed of stiffness-phase and damping-phase materials is used as the damping layer. The pseudo excitation and hybrid expansion methods are adopted to compute the random responses at the specified frequency intervals as the objective functions. The sensitivity analysis is performed through the adjoint vector method. The similarity index is defined to distinguish the various optimal layouts quantitatively. Several numerical examples clearly demonstrate the robustness and effectiveness of the proposed method, highlighting a remarkable improvement of over 60% in computational efficiency. Furthermore, it is proved that, when subjected to broadband random excitation, the damping materials' temperature- and frequency-dependent properties are critical factors in obtaining accurate vibration responses and desirable optimal layouts. The effects of the layer thicknesses and volume fractions of composite structures on topology optimization are also discussed. The results indicate that both layer thickness and volume fraction can change the mechanical properties of the composite structure and further affect the distribution characteristics of the damping material.

A Low-Frequency Vibration Sensor Based on Ball Triboelectric Nanogenerator for Marine Pipeline Condition Monitoring.

Marine pipeline vibration condition monitoring is a critical and challenging issue, on account of the complex marine environment, while powering the required monitoring sensors remains problematic. This study introduces a vibration sensor based on a ball triboelectric nanogenerator (B-TENG) for marine pipelines condition monitoring. The B-TENG consists of an acrylic cube, polyester rope, aluminum electrodes, and PTFE ball, which converts vibration signals into electrical signals without the need for an external energy supply. The experimental results show that B-TENG can accurately monitor the frequency, amplitude, and direction of vibration in the range of 1-5 Hz with a small error of 0.67%, 4.4%, and 5%, and an accuracy of 0.1 Hz, 0.97 V/mm, and 1.5°, respectively. The hermetically sealed B-TENG can monitor vibration in underwater environments. Therefore, the B-TENG can be used as a cost-effective, self-powered, highly accurate vibration sensor for marine pipeline monitoring.

Uncertainty‐aware nuclear power turbine vibration fault diagnosis method integrating machine learning and heuristic algorithm

AbstractNuclear power turbine fault diagnosis is an important issue in the field of nuclear power safety. The numerous state parameters in the operation and maintenance of nuclear power turbines are collected, forming a complex high‐dimensional feature space. These high‐dimensional feature spaces contain redundant information, which increases the training cost and reduces the recognition accuracy and efficiency of the fault diagnosis model. To address the aforementioned challenges, a vibration fault diagnosis algorithm in nuclear power turbines is proposed. First, a long short‐term memory‐based denoising autoencoder (LDAE) is designed to enhance the capability of uncertainty awareness. Then, a feature extraction method integrating variational mode decomposition (VMD), L‐cliffs‐based effective mode selection, and sample entropy is devised to extract the latent features from the complex high‐dimensional feature space. Furthermore, using extreme gradient boosting (XGBoost) as the classifier, LDAE‐VMD‐XGBoost model is constructed for fault diagnosis of nuclear power turbines. Considering the impact of multiple hyperparameters of LDAE‐VMD‐XGBoost model on the performance, the pathfinder algorithm is used to optimise the model hyperparameter settings and improve the fault diagnosis accuracy. Experimental results demonstrate the performance of the proposed improved LDAE‐VMD‐XGBoost in accurate nuclear power turbine vibration fault diagnosis.

Blasting Vibration Analysis with Micromate Tools: Experimental Study and Characterization at the PT Semen Padang Mine

This research investigates the impact of vibrations produced by blasting activities at the PT Semen Padang Mine, with a focus on vibration analysis and experimental characterization using the Micromate tool. The main objective of this research is to understand the vibrations resulting from blasting, analyze the impact of variations in PVS (Peak Vector Sum) values, and evaluate the effect on the surrounding environment, especially employee housing around the mining area. The research method used involves several important stages, first of which is the use of a Micromate tool to measure the vibrations produced during the blasting process. Micromate is a tool that can record and analyze vibrations with high accuracy, thus providing reliable data for further analysis. Apart from that, this research also uses power regression analysis to understand the relationship between PVS and Scaled Distance variables. This analysis is important to determine how variations in blasting distance and intensity affect the resulting PVS values. Then, a distance analysis was carried out to obtain a PVS value below 3 mm/s, which is considered a safe threshold to prevent structural damage to the building. The research results show several important findings. Even though the measured vibration values ​​are below the Threshold Values ​​set by environmental regulations, it is important to comply with all existing regulations to avoid long-term negative impacts. The power regression analysis carried out shows that there is a significant relationship between PVS and Scaled Distance, which means that the greater the distance between the blasting point and the measurement point, the smaller the PVS value detected. The validation results of this analysis are also in accordance with the empirical data collected during the research. In addition to these main findings, this research also provides several practical recommendations. One of them is setting the explosive charge to reduce the impact of the resulting ground vibrations. These arrangements include reducing the amount of explosive per blast or changing the blasting technique to reduce vibration intensity. In conclusion, this research provides an in-depth understanding of the factors that influence vibrations from blasting in a mining context. The results of this research have important implications for the development of more accurate and effective vibration measurement methods as well as for mitigating environmental impacts caused by blasting activities. This research not only provides new insights for the mining industry but also helps in formulating better policies to protect the environment and communities around mining areas. Thus, this research contributes to ongoing efforts to achieve a balance between industrial activities and environmental sustainability.

New exact free vibration solution of two-dimensional decagonal quasicrystal cylindrical shells in the symplectic framework

An accurate free vibration analysis of two-dimensional decagonal quasicrystal (QC) cylindrical shells is performed under the framework of symplectic mechanics. Unlike the classical Lagrangain system, the governing equations are reduced into a set of lower order ordinary differential equations, and therefore exact solutions of displacements, rotation angles, internal forces and bending moments in the phonon and phason fields are simultaneously obtained and expressed in terms of symplectic vectors. The present results are compared with existing literature and finite element solutions, and excellent agreements are observed. Furthermore, the effects of key influencing factors on the free vibration characteristics are revealed also.

Water Pipeline Leakage Detection Based on Coherent φ-OTDR and Deep Learning Technology

Leakage in water supply pipelines remains a significant challenge. It leads to resource and economic waste. Researchers have developed several leak detection methods, including the use of embedded sensors and pressure prediction. The former approach involves pre-installing detectors inside pipelines to detect leaks. This method allows for the precise localization of leak points. The stability is compromised because of the wireless signal strength. The latter approach, which relies on pressure measurements to predict leak events, does not achieve precise leak point localization. To address these challenges, in this paper, a coherent optical time-domain reflectometry (φ-OTDR) system is employed to capture vibration signal phase information. Subsequently, two pre-trained neural network models based on CNN and Resnet18 are responsible for processing this information to accurately identify vibration events. In an experimental setup simulating water pipelines, phase information from both leaking and non-leaking pipe segments is collected. Using this dataset, classical CNN and ResNet18 models are trained, achieving accuracy rates of 99.7% and 99.5%, respectively. The multi-leakage point experiment results indicate that the Resnet18 model has better generalization compared to the CNN model. The proposed solution enables long-distance water-pipeline precise leak point localization and accurate vibration event identification.

Developing and Testing High-Performance SHM Sensors Mounting Low-Noise MEMS Accelerometers.

Recently, there has been increased interest in adopting novel sensing technologies for continuously monitoring structural systems. In this respect, micro-electrical mechanical system (MEMS) sensors are widely used in several applications, including structural health monitoring (SHM), in which accelerometric samples are acquired to perform modal analysis. Thanks to their significantly lower cost, ease of installation in the structure, and lower power consumption, they enable extensive, pervasive, and battery-less monitoring systems. This paper presents an innovative high-performance device for SHM applications, based on a low-noise triaxial MEMS accelerometer, providing a guideline and insightful results about the opportunities and capabilities of these devices. Sensor nodes have been designed, developed, and calibrated to meet structural vibration monitoring and modal identification requirements. These components include a protocol for reliable command dissemination through network and data collection, and improvements to software components for data pipelining, jitter control, and high-frequency sampling. Devices were tested in the lab using shaker excitation. Results demonstrate that MEMS-based accelerometers are a feasible solution to replace expensive piezo-based accelerometers. Deploying MEMS is promising to minimize sensor node energy consumption. Time and frequency domain analyses show that MEMS can correctly detect modal frequencies, which are useful parameters for damage detection. The acquired data from the test bed were used to examine the functioning of the network, data transmission, and data quality. The proposed architecture has been successfully deployed in a real case study to monitor the structural health of the Marcus Aurelius Exedra Hall within the Capitoline Museum of Rome. The performance robustness was demonstrated, and the results showed that the wired sensor network provides dense and accurate vibration data for structural continuous monitoring.

Accurate free and forced vibration behavior prediction of functionally graded sandwich beams with variable cross-section: A finite element assessment

The current investigation proposes an enhanced finite element beam model to assess the dynamic behavior of functionally graded sandwich beams with variable cross-sections. By integrating the shear effect, this beam model integrates both C0 and C1 continuities to generate elementary matrices using an efficient three-unknown shear deformation beam theory. The model focuses on advanced sandwich beams with functionally graded material face-sheets and metallic cores, exploring various geometric arrangements. The originality of this study addresses variable cross-sections in sandwich beams, evaluating the influence of material composition and geometric configurations on free and forced vibration responses, including different boundary conditions. The methodology uses the eigenvalue method for examining free vibration and applies Newmark’s algorithm for solving forced vibration problems. The current model expands the scope of analysis compared to previous models that primarily focus on uniform cross-sections and conventional material distributions. The developed model incorporates non-uniform geometrics, material gradient parameters, and advanced finite element analysis to enhance precision and efficacy through detailed parametric investigations and comparisons with existing literature. This contribution significantly improves the mechanical modeling of composite sandwich structures, particularly those incorporating functionally graded materials and complex geometrical aspects.

Internet of things (IoT)-based structural health monitoring of laboratory-scale civil engineering structures

Rapid advances in the Internet of Things (IoT) domain have made it a crucial technology for the real-time structural health monitoring (SHM) of civil engineering infrastructures. The availability of quick and accurate vibration data is essential for SHM, and such data can be obtained through IoT devices mounted on the structures. This study proposes a real-time damage prediction and localization approach using a low-cost "do-it-yourself" wireless sensor node with IoT capabilities for SHM. The proposed sensor node comprised a microcontroller (NODE MCU ESP8266) and a 6-axis accelerometer (MPU6050). The IoT devices track the real-time frequency of the laboratory-scale structure indirectly via measurement of acceleration-time history, and their results are compared with conventional industry-standard accelerometers. Promising results, with a <6% average difference from the conventional accelerometer (difference ranging from 1.3 to 14.3%), provided an innovative SHM for vibration-based real-time SHM using the IoT paradigm. The performance of the proposed methodology was validated numerically and experimentally on two laboratory-scale structures, and the potential of IoT technology for enhancing the efficiency of SHM was demonstrated. The proposed method thus can enable the early detection of damages in infrastructures such as buildings and bridges and thus can reduce the likelihood of accidents via continuous SHM.

Modelling of Multi-Storey Cross-Laminated Timber Buildings for Vibration Serviceability

In this study, the vibration serviceability of multi-storey timber buildings is addressed. The core of this study pertains to the preparation of a comprehensive finite element model to predict modal properties for an accurate vibration serviceability checking. To that end, findings obtained from studying three multi-storey timber buildings are summarized and discussed. Two of the buildings (of seven and eight storeys) consist entirely of cross-laminated timber (CLT), while the third is a five-storey hybrid CLT-concrete building. Thanks to the detailed finite element models and modal testing results, one has the capability to conduct sensitivity analyses, classical and Bayesian model updating, and uncertainty quantifications. With these methodologies, influential modelling parameters as well as the sources of modelling error were identified. This allowed for conclusions to be drawn about the in-plane shear stiffness of the constructed walls (whose higher value causes the natural frequencies to increase by up to 25%), the soil deformability (which may cause the natural frequencies to drop by up to 20%), and the perpendicular-to-the-grain deformation of floor slabs (which may lead to an overestimation of a fundamental frequency by up to 8%).