Actual Vibration Research Articles

Differential Evolution-based Time Domain Decomposition Method for Multi-impact Vibration Signals of Reciprocating Machinery

Abstract To solve the problem of extracting the impact component from the complex time-domain vibration signal of reciprocating machinery vibration signals, a differential evolution-based time domain decomposition method is proposed to achieve adaptive extraction of impact components. The method establishes new decomposition window containing three adjustment parameters to adapt to multiple forms of impact components. Furthermore, with the optimization objectives of minimizing reconstruction loss, amplitude moment loss, and similarity loss, a decomposition parameter optimization algorithm based on differential evolution is established to achieve the optimization process of decomposition parameters. The results of processing simulated and actual vibration signals of diesel engines show that the new method can adaptively and accurately identify the impact component and impact time center in the vibration component, with a signal reconstruction loss of less than 2.5% and a decomposition time of only 54.1s.

Vortex-Induced Vibration Performance Analysis of Long-Span Sea-Crossing Bridges Using Unsupervised Clustering

Vortex-induced vibration is a type of wind-induced vibration occurring frequently in large-span sea-crossing bridges under relatively low wind speeds, posing a threat to the structural fatigue performance and driving comfort. Identifying the instantaneous occurrence moments of vortex-induced vibration is a prerequisite for establishing a data-driven prediction model for vortex-induced vibration, and it is of great significance for the monitoring and early warning of vortex-induced vibration performance in bridges. To automatically detect the occurrence moments of vortex-induced vibration and establish a correlation model between vortex-induced vibration amplitude and environmental factors, this study proposes a fuzzy C-means clustering-based classification method. In order to detect the occurrence moments of vortex-induced vibration more finely, only short-term or even instantaneous structural vibration indicators were selected and transformed for distribution as clustering features. The entire detection process could be carried out unsupervised, reducing the manual cost of obtaining vortex-induced vibration information from massive monitoring data. Finally, actual vortex-induced vibration test data from a certain overseas bridge was utilized to verify the feasibility of this method. Based on the classification results, the correlation between vortex-induced vibration amplitude and environmental variables was determined, providing valuable guidance for predicting vortex-induced vibration amplitudes.

Vibrator Rack Pose Estimation for Monitoring the Vibration Quality of Concrete Using Improved YOLOv8-Pose and Vanishing Points

Monitoring the actual vibration coverage is critical for preventing over- or under-vibration and ensuring concrete’s strength. However, the current manual methods and sensor techniques fail to meet the requirements of on-site construction. Consequently, this study proposes a novel approach for estimating the pose of concrete vibrator racks. This method integrates the Linear Spatial Kernel Aggregation (LSKA) module into the You Only Look Once (YOLO) framework to accurately detect the keypoints of the rack and then employs the vanishing point theorem to estimate the rotation angle of the rack without any 3D datasets. The method enables the monitoring of the vibration impact range for each vibrator’s activity and is applicable to various camera positions. Given that measuring the rotation angle of a rack in reality poses is challenging, this study proposes employing a simulation environment to validate both the feasibility and accuracy of the proposed method. The results demonstrate that the improved YOLOv8-Pose achieved a 1.4% increase in accuracy compared with YOLOv8-Pose, and the proposed method monitored the rotation angle with an average error of 6.97° while maintaining a working efficiency of over 35 frames per second. This methodology was successfully implemented at a construction site for a high-arch dam project in China.

Research on the vibration load spectrum extraction method for electric drive assembly

Abstract It is vital to precisely extract the actual vibration load spectrum of the electric drive assembly since it is a crucial piece of fundamental load data for vibration testing and vibration fatigue performance development. A simulation iteration-based vibration load spectrum extraction method for electric drive assemblies is proposed by actually measuring the six-component load spectrum. The entire vehicle system dynamical model was developed and validated using an electric car’s electric drive assembly as the study object. A simulated iterative system is established, and the frequency response function of the system is obtained. The excitation signals of the simulated iterative system are obtained by the simulated iterative algorithm and verified, with all relative errors within 5.0%. The results indicate that the simulation iteration-based method for extracting the vibration load spectrum of the electric drive assembly can accurately simulate the loads on the electric drive assembly during actual vehicle operation.

Research on a novel non-uniform and asymmetric ultrasonic vibration system

Abstract A new type of non-uniform and asymmetric ultrasonic vibration system is proposed and applied to the turning process. The research results show that the design theory of the non-uniform and non-symmetric ultrasonic vibration system is accurate and reliable.The theoretical design frequency and modal simulation frequency error of Ultrasonic Vibration Turning System (UVTS) is only 2.5%. The designed non-uniform and non-symmetric ultrasonic vibration system has excellent vibration performance and stability, and the established simulation model can predict surface microtexture accurately. The vibration mode output by the non-uniform and non-symmetric ultrasonic vibration system is longitudinal-bending vibration, and the actual vibration performance is excellent. Compared with conventional cutting, the addition of longitudinal-bending ultrasonic vibration under the same conditions can give the machined surface a microscopic textured structure, reduce surface roughness and water contact angle, and improve surface wettability.The minimum surface roughness obtained in the experiment is 1.005 μm.The orthogonal experiment results show that there is a correlation between the machining parameters, surface micro-structure parameters, surface wettability, and surface roughness.

Rotating machinery early fault detection integrating variational mode decomposition and multiscale singular value decomposition

Abstract Security and reliability are important issues that must be paid attention to during the operation of rotating machinery. If defects can be found in the early stage, there will be enough time to take maintenance measures and realize the stable operation of equipment. However, the presence of noise, shaft rotation signals, gear meshing signals, and other interfering factors often obfuscate fault signals, rendering the early detection of defects an arduous undertaking. Against this backdrop, this study presents an advanced approach for early defect detection, integrating the virtues of variational mode decomposition (VMD) and multiscale singular value decomposition (MSVD). Initially, a novel evaluation index is constructed by combining envelope entropy and envelope spectrum sparsity. Based on this a method is proposed to adaptively determine the critical parameters of VMD, enabling the adaptive decomposition of vibration signals into a series of modal components. The optimal sensitive components are then discerned utilizing the characteristic frequency intensity coefficient index. Subsequently, to address the limitations of single VMD methods in effectively suppressing low-frequency noise, the MSVD method is proposed for effective noise reduction, which reconstructs the signal after SVD of the signal within each segment through the operation of successive signal segmentation. Ultimately, envelope spectrum analysis is conducted on the reconstructed signal, facilitating the precise extraction of fault characteristic frequency information and enabling early fault identification. The efficacy of this novel methodology is evaluated through simulations and actual vibration signals, successfully discerning early faults afflicting rotating machinery.

Digital twin-driven senseless cutting force monitoring and vibration stability control of a rotary ultrasonic machining system

Rotary ultrasonic machining (RUM) emerges as a superior technique for processing difficult-to-cut materials due to its capacity to improve machining efficiency and surface quality. The tool vibration amplitude and its stability in the machining process are critical to guarantee the performance of RUM. However, due to the lack of a reliable status monitoring and control method, the operator cannot identify and solve the problem of vibration loss in time, greatly restricting the wider industrial application of RUM. This study proposes a digital twin-driven method to monitor and control the status of the RUM system. A twin model of the RUM system is first established, which describes the relationship among the driving electrical variables, the vibration amplitude, and the external load when the system works at the resonant state. A frequency tracking algorithm using the electrical signals is used to make the RUM work at the resonant state, which ensures the twin model is always aligned with the physical RUM system. And the actual vibration amplitude and the external load can be derived in real time. Then, a voltage-adjusting method is proposed to make the actual vibration amplitude approach its idle value and the cutting force is updated. Applying the above route, the cutting force and vibration amplitude can be in-process monitored without extra sensors, and vibration stability can be improved. Finally, loading and drilling experiments are carried out to verify the efficacy of the proposed digital twin-driven method. The experimental results demonstrate that the proposed method can monitor the cutting force accurately (average deviation during the whole drilling process <10 %) without using any additional force sensor like dynamometer, and ensure the stability of vibration amplitudes under load (amplitude variation <10 %), enabling the RUM system to become intelligent.

Characterization of inter-panel connections in CLT floors using finite element model updating

Large cross-laminated timber (CLT) floors are inevitably composed of multiple CLT panels assembled on the construction site. Due to the sheer lack of adequate numerical characterization of the inter-panel connections, CLT floors are normally modelled as monolithic structures or independent “piano keyboards”, yielding errors between simulated and the actual vibration response. This paper presents a benchmark study for a deterministic finite element (FE) model updating of so called “an equivalent elastic strip” representation of the inter-panel connections. Fitting modelling parameters of the strip, i.e. its width and material properties, featured FE modelling and modal testing of two full-scale CLT floors composed of one and two panels connected with a half-lapped joint. The floors were made of (almost) identical panels and placed on elastic mats along their shorter edges. First, the single-panel floor was used to fit material properties of the timber and the boundary conditions. Then, the FE model updating (FEMU) of two-panel floor yielded the actual values of the strip modelling parameters. Finally, these were successfully verified by direct comparison between numerically calculated and experimentally measured modal properties of a full-scale model of a simply supported three-panel floor. The results implied that the rotational stiffness of the inter-panel connection depends strongly on its vertical vibration displacement. Consequently, values of the rotational stiffness derived from rare static tests published so far cannot be used reliably in a much subtler vibration response analysis. Future studies should feature other types of the inter-panel connections in various floor layouts and a probabilistic FEMU.

Predicting ground vibration during rock blasting using relevance vector machine improved with dual kernels and metaheuristic algorithms

The ground vibration caused by rock blasting is an extremely hazardous outcome of the blasting operation. Blasting activity has detrimental effects on both the ecology and the human population living in proximity to the area. Evaluating the magnitude of blasting vibrations requires careful evaluation of the peak particle velocity (PPV) as a fundamental and essential parameter for quantifying vibration velocity. Therefore, this study employs models using the relevance vector machine (RVM) approach for predicting the PPV resulting from quarry blasting. This investigation utilized the conventional and optimized RVM models for the first time in ground vibration prediction. This work compares thirty-three RVM models to choose the most efficient performance model. The following conclusions have been mapped from the outcomes of the several analyses. The performance evaluation of each RVM model demonstrates each model achieved a performance of more than 0.85 during the testing phase, there was a strong correlation observed between the actual ground vibrations and the predicted ones. The analysis of performance metrics (RMSE = 21.2999 mm/s, 16.2272 mm/s, R = 0.9175, PI = 1.59, IOA = 0.8239, IOS = 0.2541), score analysis (= 93), REC curve (= 6.85E−03, close to the actual, i.e., 0), curve fitting (= 1.05 close to best fit, i.e., 1), AD test (= 11.607 close to the actual, i.e., 9.790), Wilcoxon test (= 95%), Uncertainty analysis (WCB = 0.0134), and computational cost (= 0.0180) demonstrate that PSO_DRVM model MD29 outperformed better than other RVM models in the testing phase. This study will help mining and civil engineers and blasting experts to select the best kernel function and its hyperparameters in estimating ground vibration during rock blasting project. In the context of the mining and civil industry, the application of this study offers significant potential for enhancing safety protocols and optimizing operational efficiency.

Intelligent Diagnosis Method for Typical Co-frequency Vibration Faults of Rotating Machinery Based on SAE and Ensembled ResNet-SVM

Intelligent fault diagnosis is an important method in rotating machinery fault diagnosis and equipment health management. To deal with co-frequency vibration faults, a type of typical fault in rotating machinery, this paper proposes a fault diagnosis method based on the stacked autoencoder (SAE) and ensembled ResNet-SVM. Furthermore, the time- and frequency-domain features of several co-frequency vibration faults are summarized based on the mechanism analysis and calculated using actual vibration data. To realize and validate the high-precision diagnosis method of rotating equipment with co-frequency faults proposed in this study, the following three criteria are required: First, to improve the effectiveness and robustness of the ensembled model and the sliding window using data augmentation, adding noise, autoencoder (AE) and SAE methods are analyzed in terms of principle and practical effects. Second, ResNet is used as the feature extractor for the ensembled ResNet-SVM model. Feature extraction is carried out twice, and the extracted co-frequency fault features are more comprehensive. Finally, the data augmentation method and ensemble ResNet-SVM are combined for fault diagnosis and compared with other methods. The experimental results show that the accuracy of the proposed method can exceed 99.9%.

Deep discriminative sparse representation learning for machinery fault diagnosis

The high complexity of actual machinery vibration environments introduces various interferences into vibration signals, making it challenging to eliminate redundant information and extract discriminative features of mechanical faults. Shallow-structured sparse classification models enable intelligent fault diagnosis but struggle with deeper feature extraction, especially in small-sample cases. In this paper, we introduce a deep discriminative sparse representation learning (DDSRL) framework with a deep architecture for this issue. In DDSRL, the first-layer dictionary atoms are automatically learned from raw signals and borrowed for sparse coding to enhance sparsity and interclass discriminability. Then, the weight matrix is defined to screen high-energy atoms from the upper dictionary as new training samples for the next dictionary learning and sparse coding layers, thereby eliminating interclass redundant atoms and mining deeper discriminative representations of cyclostationary impulses or meshing harmonics from the shallow dictionary. Finally, DDSRL extracts feature sequences sensitive to fault-induced waveforms via the learned dictionaries and coding coefficients for intelligent fault identification. Experimental verifications on two datasets with high-speed bearing and compound gear-bearing faults demonstrate that DDSRL outperforms six popular sparse classification models, sparse autoencoder, convolutional neural network, and principal component analysis network. Furthermore, the layer-by-layer feature extraction process of DDSRL and feature visualization analysis provide more credible fault diagnosis results.

Vibration Response of Metal Plate and Shell Structure under Multi-Source Excitation with Welding and Bolt Connection

There are many excitation sources and complex vibration environments in combine harvesters. The coupling and superposition of different vibration signals on the plate and shell seriously affect the working parts of the body. This also reduces the reliability of the whole machine. At present, domestic and foreign research on existing harvesters mainly focuses on harvesting performance, with less research on vibration characteristics. Therefore, in this paper, the vibration response of the metal plate–shell under the two connection modes of bolt connection and welding is studied, in order to optimize the design and structure of the plate–shell structure of the combine harvester and improve the overall performance. First, the welded and bolted plates are numerically modeled using Hypermesh pre-processing functions. Then, the boundary conditions are simulated by continuous variable stiffness elastic constraint experiments. Finally, the intrinsic vibration dynamic model of the four-sided simply supported plate and four-sided solidly supported plate is established using the modal superposition method. By analyzing the modal frequencies and vibration patterns, the following results are obtained. The connection method between the plate and the frame has a significant impact on the inherent vibration characteristics of the plate. The bolt connection will make the plate’s intrinsic vibration frequency higher than that of the welding method, but the effect on the plate’s intrinsic vibration pattern is more minor. At the same time, in order to verify the accuracy of the model, the actual modal vibration patterns and frequencies of the same proportion of plates in the modal test are compared with the results of modal vibration patterns and frequencies obtained by Ansys. The errors of the two dynamic model analytical methods are within 1% and 3%, respectively. This result verifies the accuracy of the dynamic model of the metal plate and shell structure under different connection methods.

Digital twin‐driven online intelligent assessment of wind turbine gearbox

AbstractRemaining useful fatigue life monitoring of wind turbine drivetrains is important. However, the implementation of real‐time monitoring often faces efficiency and accuracy challenges. In order to resolve this, this paper proposes a vibration‐based damage monitoring digital twin (VBDM‐DT) that enables the online intelligent evaluation of wind turbine gearboxes, using gear tooth surface durability as an example fatigue mode. The VBDM‐DT integrates a random wind load model, a high‐fidelity dynamics model, and a fatigue damage model. The random wind load model takes the wind speed from the supervisory control and data acquisition (SCADA) as input to estimate the input torque of the drivetrain in real time. Simultaneously, VBDM‐DT uses the vibration signals from the condition monitoring system (CMS) to intelligently calibrate the dynamics model, allowing it to be continuously adjusted and optimized in response to actual vibrations. The fatigue damage model takes the real‐time dynamic loads estimated by the high‐fidelity dynamic model as input to achieve real‐time fatigue damage monitoring of key components in the wind turbine gearbox. Applying the VBDM‐DT model to a 2 MW wind turbine gearbox, the results indicate that the model provides satisfactory accuracy in estimating input loads and good adaptability in intelligent calibration of the dynamic model. Based on this model, the fatigue life estimation for gears and bearings is more credible and reliable.

Sparse Fast Fourier Transform Analysis of Aero-Engine Vibration Data

Sparse fast Fourier transform is a new spectrum analysis method based on signal sparse characteristics in recent years. Sparse fast Fourier transform can quickly and accurately process large data by identifying and discarding frequency signals that have no effect on the analysis results. In order to explore the practicality of Sparse fast Fourier transform in aero-engine vibration data analysis, this paper combines the actual flight test vibration data, and studies the sparse fast Fourier transform method and the traditional fast Fourier transform method in terms of spectral characteristics, running time, algorithm stability. By comparison, it can be found that the sparse fast Fourier transform algorithm can greatly improve the efficiency of vibration data analysis while ensuring accuracy.

Development and implementation of vibroseismic protection of buildings and structures from external dynamic loads

The article considers the results of vibration dynamic tests of the vibration acceleration levels of a vibration insulated reinforced concrete slab and floors of a residential building, which have confirmed the effectiveness of the seismic vibration isolation system using rubber elements. The registered levels of vibration accelerations in residential premises on different floors do not exceed permissible levels according to sanitary standards, which ensures comfortable living conditions in the presence of dynamic influences. To determine the actual vibration levels of the soil and piles vibration and dynamic studies were carried out. Based on the results of these studies, numerical calculations were carried out to determine the compliance of the predicted vibration levels in residential premises with the existing sanitary standards when they are exposed to real anthropogenic loads.

Analysis of Ground Vibration Levels Due to the Blasting Process at PT. Bumi Suksesindo

Ground vibration is one of the effects of the blasting process; when the ground vibration reaches the highest level, it will disturb comfort and even cause damage to the surrounding building structure. This research aims to determine the magnitude of ground vibrations in Pit A and Pit C, as well as determine the relationship between Peak Particle Velocity (PPV) and scaled Distance, and determine the maximum explosive charge weight per delay based on the SNI 7571: 2010 reference. Actual ground vibration measurement data during research based on PPV theory and the actual PPV power regression relationship with scaled distance was used to obtain a ground vibration prediction formula to be a reference for determining the amount of explosive filling per delay. The ground vibration produced in the blasting process is hoped not to exceed the safe threshold. Prediction of the ground vibration formula at 100 m to 1500 m according to the US Bureau of Mines where the Mean Squared Error (MSE) value is 0.54, the MSE value from the Langefors-Kihlstrom equation is 1.85 while the MSE value from the Ambersays-Hendorn equation is 0.31 with the slightest deviation is very good to use as a reference for predicting ground vibrations with the predicted PPV formula. Hence, the maximum explosive charge with a PPV limit of 2 mm/s is 2.452 kg, a PPV limit of 3 mm/s is 11.332 kg, and a PPV limit of 5 mm/s is 23.040 kg. The factors that influence ground vibration are the Distance from the blasting location to the measurement location and the maximum number of explosives per delay, so the results taken from this research are that blasting in Pit A and Pit C is still categorized as safe for infrastructure and community housing.

Determination of elastic loss of piezoelectric materials by impedance curve fitting using intelligent algorithms

Understanding the loss parameters of piezoelectric materials is crucial for designing effective piezoelectric sensors. Traditional elastic loss parameter measurement techniques mainly rely on three methods: 3 dB bandwidth, impedance fitting, and ultrasonic attenuation. However, the elastic losses obtained through these methods are constant and frequency-independent, which does not align with the actual vibration characteristics of piezoelectric materials. Therefore, there is a need for a fast, accurate, and frequency-dependent method to obtain the elastic loss of piezoelectric materials. This paper introduces an approach that utilizes intelligent algorithms for fitting impedance curve to calculate elastic loss parameters. A frequency-dependent second-order energy loss model for piezoelectric materials is established. Then, a genetic algorithm is introduced to obtain the optimal elastic loss parameters. The results demonstrate a high consistency between theoretical and experimental impedances, with an error less than 5%. The elastic loss parameters obtained through intelligent algorithm-based impedance curve fitting match well with stress experiment results, with an error less than 6%. This method provides a rapid, accurate, and cost-effective way to obtain frequency-dependent second-order elastic loss parameters for piezoelectric materials.

Experimentally Determined Force Density Spectra for Admittance-Based Vibration Predictions along Railways

The planning application and approval process of railway tracks is generally accompanied by a vibration immission assessment. Starting with the source spectrum, which is ideally obtained through measurements, the German guideline VDI 3837 recommends a series of multiplications using transfer spectra which account for the various subdomains of the wave propagation path, such as the effect of the superstructure, the free field propagation, the soil-structure coupling and the transmission inside buildings. Typically, these one-third octave spectra are an average over empirical reference values. While simplified empirical relations are prone to a large variance, the use of artificial vibration sources allows the actual vibration transmission behavior from the tracks to the immission points to be quantified. Using so-called transfer admittances, also known as transfer mobilities, which account for all dynamic interactions along the transmission path (track, tunnel structures, foundations, structural properties), together with force density spectra for relevant rail vehicles, the authors investigate the practical application of the method presented in Report No. 0123 of the Federal Transit Administration (2018) for the frequency range 5–200 Hz. The article demonstrates how such force density spectra were obtained for the most common train types in the Austrian rail network at two different track sections using artificial vibration sources. Furthermore, practical aspects are discussed and a recently developed approximation method for estimating line transfer admittances from point transfer admittances using simplified models is introduced.

An Automatic Frequency Control System for Transmission Machinery Based on Back-Propagation Neural Network Algorithm in the Internet of Things Environment

Abstract In order to reduce the error of automatic frequency control of transmission machinery under different disturbances, an automatic frequency control system of transmission machinery based on back-propagation (BP) neural network algorithm is designed. The gradient descent method and Newton method are combined to optimize the BP neural network. Aiming at the periodicity and trend of the actual mechanical vibration frequency time series, the first order backward difference is processed by the difference method, and the autoregressive sequence is derived to obtain the control model. The experimental results show that the system can realize the automatic control of transmission mechanical frequency with small error under different types of interference, and the control effect is ideal.

Research on vibration response of 550 kV GIL isolation compensation unit against earthquake loading

To analyze the vibration response of Gas Insulated Transmission Lines (GIL) of urban power grid under different seismic intensity levels, based on the actual structure of the standard isolation compensation unit (isolation unit and compensation unit) of 550kV GIL, A fine 3D model of isolation compensation unit segment of 550 kV GIL was established, and the actual vibration response of each standard unit segment of GIL under different seismic conditions was studied by response spectrum analysis. This study clarified the action mechanism and law of GIL vibration characteristics under seismic conditions of urban power grids, provided the relative support for the research of shock absorption of GIL, and was great significance to the actual project of GIL.