Search Results (154)

To address the limitations of traditional elasticity theory in analyzing the dynamic response of soft coal under external impact, this study establishes a vibration control equation with an analytical solution based on Hamiltonian mechanics. Key control parameters within the equation were solved to determine the theoretical dominant vibration modes and natural frequencies of the weakest coal layer. Triangular and rectangular waves were transformed via FFT to analyze their harmonic components, and the superposition of the first four harmonics was selected as the input impact signal. The modal and natural frequency changes during the fragmentation of the central weak zone under external impact were simulated, and the dynamic displacement response was analyzed. The results indicate a strong response frequency range of 4.4–5.2 Hz, with the rectangular wave identified as the most effective response waveform. A similarity simulation platform was constructed, and experimental data showed that the velocity and displacement response trend of the coal mass aligned closely with theoretical predictions. Therefore, in actual underground operations, emphasis should be placed on monitoring low-frequency vibrations in mines, minimizing rectangular wave disturbances in the low-frequency range, and implementing pressure relief measures in high-risk zones to reduce the likelihood of coal and gas outbursts. By separately modeling high-risk zones and analyzing their dynamic response under external impact, this study explains the outburst mechanism of the weakest layer in soft coal from a dynamic perspective. Combining theoretical and experimental approaches, it provides a new theoretical basis for understanding and preventing coal and gas outbursts. Full article

In order to enhance the tracking accuracy and disturbance rejection capability in the speed loop of an optoelectronic tracking servo control system, a parameter self-adjusting disturbance rejection generalized predictive control method (STGPC) based on a continuous-time model is proposed in this paper. First, a dynamic model of the servo turntable system is established, and a linear extended state observer (LESO) is designed to perform real-time estimation of internal and external disturbances in the system. Second, a generalized predictive control law incorporating the predictive model, performance metrics, and rolling optimization is systematically derived, where the reference trajectory is generated by a tracking differentiator and the system state is provided in real time by the LESO. Furthermore, a gradient descent method is innovatively introduced to achieve adaptive adjustment in the predictive time domain, and the stability of the closed-loop system is rigorously proven based on Lyapunov theory. Finally, simulation experiments were conducted to verify the tracking performance, disturbance rejection performance, and time-domain parameter self-adjustment effects of the control method. Simulation results show that compared with PID control and traditional linear generalized predictive control (LGPC), the proposed STGPC method reduces speed tracking residuals by 73.79% and 51.04%, respectively, enhances disturbance suppression capability for speed vibration disturbances by 50.55% and 47.55%, respectively, and enhances compensation capability for friction torque disturbances by 68.03% and 59.33%, respectively. The system demonstrates outstanding velocity tracking accuracy and disturbance rejection while exhibiting good robustness against system parameter perturbations. Full article

Tunnel drilling and blasting will cause large vibrations in the surrounding rock and structures. This vibration effect weakens the rock, greatly threatening the surrounding rock’s structural integrity and the safety of tunnel construction. Based on an analysis of the status quo of rock blasting, this study performs on-site monitoring of blasting vibration and examines the characteristics of the vibration velocity in the tunnel’s surrounding rock. A load-time history diagram is used to establish a three-dimensional numerical model of the tunnel to analyze the distribution characteristics of the vibration velocity. The applicability of the model is verified by field monitoring data. The simulation revealed a maximum vertical vibration velocity of 48.6 cm/s near the blast source. The response of the rock mass to the blasting load is analyzed at each key position, and the particle vibration velocity law is studied. On this basis, the corresponding Sadovsky formula is thus derived, which can be used to determine the site coefficient, K, and the attenuation exponent, α (with values ranging from 1.268 at the arch waist to 1.594 at the vault and invert), and to predict the vibration velocity in the far blasting area. The maximum charge dose and safety distance are derived under different control standards based on these data. For a control standard of 15 cm/s and a maximum charge of 20 kg, the required safety distance was determined to be 11.8 m. The findings can contribute to blasting scheme design and enhance the security management of construction sites. Full article

This study experimentally investigates the wake-induced vibration (WIV) behavior of a bio-inspired harbor seal whisker model subjected to upstream propeller-generated unsteady flows. Vibration amplitudes, frequencies, and wake–whisker interactions were systematically evaluated under various flow conditions. The test matrix included propeller rotational speed N_p = 0~5000 r/min, propeller diameter D_p = 60~100 mm, incoming flow velocity U_∞ = 0~0.2 m/s, and separation distance between the whisker model and the propeller L/D = 10~30 (D = 16 mm, diameter of the whisker model). Results show that inline (IL) and crossflow (CF) vibration amplitudes increase significantly with propeller speed and decrease with increasing separation distance. Under combined inflow and wake excitation, non-monotonic trends emerge. Frequency analysis reveals transitions from periodic to subharmonic and broadband responses, depending on wake structure and coherence. A non-dimensional surface fit using L/D and the advance ratio (J = U_∞/(N_pD_p)) yielded predictive equations for RMS responses with good accuracy. Phase trajectory analysis further distinguishes stable oscillations from chaotic-like dynamics, highlighting changes in system stability. These findings offer new insight into WIV mechanisms and provide a foundation for biomimetic flow sensing and underwater tracking applications. Full article

Blast-induced ground vibrations present substantial safety and environmental hazards in surface mining operations. This study proposes and evaluates the Sparrow Search Algorithm-optimized ANN (SSA-ANN) against artificial neural network (ANN), Genetic Algorithm-optimized ANN (GA-ANN), and empirical formula (USBM) to estimate peak particle velocity (PPV). In addition, the input parameters include key blasting design parameters and rock mass features (GSI and UCS). The SSA-ANN demonstrated superior prediction accuracy, attaining an average R² of 0.51 using bootstrap validation, surpassing GA-ANN (0.41) and standard ANN (0.26). Furthermore, the incorporation of GSI enhanced the model’s geotechnical sensitivity. These results illustrate that the application of SSA-ANN alongside comprehensive rock mass characteristics can substantially decrease uncertainty in PPV prediction, therefore enhancing safety within the blast area and improving vibration control methods in blasting operations. Full article

Random parametric errors (RPEs) are introduced into the model establishment of a laminated composite cantilever beam (LCCB) to demonstrate the accuracy and robustness of a recurrent neural network (RNN) in predicting the chaotic vibration of a LCCB, and a comparative analysis of training performance and generalization capability is conducted with a convolutional neural network (CNN). In the process of dynamic modeling, the nonlinear dynamic system of a LCCB is established by considering RPEs. The displacement and velocity time series obtained from numerical simulation are used to train and test the RNN model. The RNN model converts the original data into a multi-step supervised learning format and normalizes it using the MinMaxScaler method. The prediction performance is comprehensively evaluated through three performance indicators: coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE). The results show that, under the condition of introducing RPEs, the RNN model still exhibits high prediction accuracy, with the maximum R² reaching 0.999984548634328, the maximum MAE being 0.075, and the maximum RMSE being 0.121. Furthermore, performing predictions at the free end of the LCCB verifies the applicability and robustness of the RNN model with respect to spatial position variations. These results fully demonstrate the accuracy and robustness of the RNN model in predicting the chaotic vibration of a LCCB. Full article

The traction behavior in cryogenic solid-lubricated ball bearings (CSLBBs) used in liquid rocket engines (LREs) affects not only the dynamic response of the bearing but also the lubricity and wear characteristics of the solid lubrication coating. The traction coefficient between the ball and raceway depends on factors such as contact material, relative sliding velocity, and contact pressure. However, existing traction curve models for CSLBBs typically consider only one or two of these factors, limiting the accuracy and applicability of theoretical predictions. In this study, a novel traction model for CSLBBs is proposed, which incorporates the combined effects of contact material, relative sliding velocity, and contact pressure. Based on this model, a tribo-dynamic framework is developed to investigate the tribological and dynamic behavior of CSLBBs. The model is validated through both theoretical analysis and experimental data. Results show that the inclusion of solid lubricant effects significantly alters the relative sliding and frictional forces between the rolling elements and the raceway. These changes in turn influence the impact dynamics between the rolling elements and the cage, leading to notable variations in the bearing’s vibrational response. The findings may offer valuable insights for the wear resistance and vibration reduction design of CSLBBs. Full article

Studying nonlinear oscillations in mechanical systems is fundamental to understanding complex dynamic behavior in engineering applications. While classical analytical methods remain valuable for systems with limited complexity, they become increasingly inadequate when nonlinearities are strong and geometrically induced, as in the case of large-amplitude oscillations. This paper presents a combined numerical and experimental investigation of a mechanical system composed of two coupled pendulums, exhibiting significant nonlinear behavior due to elastic deformation throughout their motion. A mathematical model of the system was developed using the MatLab/Simulink ver.6.1 environment, considering gravitational, inertial, and nonlinear elastic restoring forces. One of the major challenges in accurately modeling such systems is accurately representing damping, particularly in the absence of dedicated dampers. In this work, damping coefficients were experimentally identified through decrement measurements and incorporated into the simulation model to improve predictive accuracy. The simulation outputs, including angular displacements, velocities, accelerations, and phase trajectories over time, were validated against experimental results obtained via high-precision inertial sensors. The comparison shows a strong correlation between numerical and experimental data, with minimal relative errors in amplitude and frequency. This research represents the first stage of a broader study aimed at analyzing forced and parametrically excited oscillations. Beyond validating the model, the study contributes to the design of a robust experimental framework suitable for further exploration of nonlinear dynamics. The findings have practical implications for the development and control of mechanical systems subject to dynamic loads, with potential applications in automation, vibration analysis, and system diagnostics. Full article

The demand for large-scale, heavy-duty autonomous pavement-sweeping robots is rising due to urban growth, hygiene needs, and labor shortages. Ensuring their health and safe operation in dynamic outdoor environments is vital, as terrain unevenness and slope gradients can accelerate wear, increase maintenance costs, and pose safety risks. This study introduces an AI-driven condition monitoring (CM) framework designed to detect terrain unevenness and slope gradients in real time, distinguishing between safe and unsafe conditions. As system vibration levels and energy consumption vary with terrain unevenness and slope gradients, vibration and current data are collected for five CM classes identified: safe, moderately safe terrain, moderately safe slope, unsafe terrain, and unsafe slope. A simple-structured one-dimensional convolutional neural network (1D CNN) model is developed for fast and accurate prediction of the safe to unsafe classes for real-time application. An in-house developed large-scale autonomous pavement-sweeping robot, PANTHERA 2.0, is used for data collection and real-time experiments. The training dataset is generated by extracting representative vibration and heterogeneous slope data using three types of interoceptive sensors mounted in different zones of the robot. These sensors complement each other to enable accurate class prediction. The dataset includes angular velocity data from an IMU, vibration acceleration data from three vibration sensors, and current consumption data from three current sensors attached to the key motors. A CM-map framework is developed for real-time monitoring of the robot by fusing the predicted anomalous classes onto a 3D occupancy map of the workspace. The performance of the trained CM framework is evaluated through offline and real-time field trials using statistical measurement metrics, achieving an average class prediction accuracy of 92% and 90.8%, respectively. This demonstrates that the proposed CM framework enables maintenance teams to take timely and appropriate actions, including the adoption of suitable maintenance strategies. Full article

The reliable operation of rotating machinery is critical in industrial production, necessitating advanced fault diagnosis and maintenance strategies to ensure operational availability. This study employs supervised machine learning algorithms to apply multi-label classification for fault detection in rotating machinery, utilizing a real dataset from multi-sensor systems installed on a suction fan in a typical manufacturing industry. The presented system focuses on multi-modal data analysis, such as vibration analysis, temperature monitoring, and ultrasound, for more effective fault diagnosis. The performance of general machine learning algorithms such as kNN, SVM, RF, and some boosting techniques was evaluated, and it was shown that the Random Forest achieved the best classification accuracy. Feature importance analysis has revealed how specific domain characteristics, such as vibration velocity and ultrasound levels, contribute significantly to performance and enabled the detection of multiple faults simultaneously. The results demonstrate the machine learning model’s ability to retrieve valuable information from multi-sensor data integration, improving predictive maintenance strategies. The presented study contributes a practical framework in intelligent fault diagnosis as it presents an example of a real-world implementation while enabling future improvements in industrial condition-based maintenance systems. Full article

This study presents a high-order shear deformation theory (HSDT)-based model for evaluating the energy harvesting performance of functionally graded material (FGM) beams integrated with a piezoelectric layer and subjected to a moving concentrated load at constant velocity. The governing equations are derived using Hamilton’s principle, and the dynamic response is obtained through the State Function Method with trigonometric mode shapes. The output voltage and harvested power are calculated based on piezoelectric constitutive relations. A comparative analysis with homogeneous isotropic beams demonstrates that HSDT yields more accurate predictions than the Classical Beam Theory (CBT), especially for thick beams; for instance, at a span-to-thickness ratio of h/L = 12.5, HSDT predicts increases of approximately 6%, 7%, and 12% in displacement, voltage, and harvested power, respectively, compared to CBT. Parametric studies further reveal that increasing the load velocity significantly enhances the strain rate in the piezoelectric layer, resulting in higher voltage and power output, with the latter exhibiting quadratic growth. Moreover, increasing the material gradation index n reduces the beam’s effective stiffness, which amplifies vibration amplitudes and improves energy conversion efficiency. These findings underscore the importance of incorporating shear deformation and material gradation effects in the design and optimization of piezoelectric energy harvesting systems using FGM beams subjected to dynamic loading. Full article

Predicting flow-induced vibration (FIV) of multiple slender structures remains a modern challenge in science and engineering due to the phenomenon’s sensitivity to layout parameters and the emergence of oscillations driven by multiple mechanisms. The present study examines the FIV of five circular cylinders with two degrees of freedom arranged in a ‘cross’ configuration and subjected to a uniform current. A computational fluid dynamics approach, solving the transient, incompressible 2D Navier–Stokes equations, is employed to analyze the influence of the spacing ratio and reduced velocity $U_r$ on the vibration response and wake dynamics. The investigation includes model verification and parametric studies for several spacing ratios. Results reveal vortex-induced vibrations (VIVs) in some of the cylinders in the arrangement and combined vortex-induced and wake-induced vibration (WIV) in others. Lock-in is observed at $U_r$ = 7 for the upstream cylinder, while the midstream and downstream cylinders exhibit the highest vibration amplitudes due to wake interference. Larger spacing ratios amplify the oscillations of the downstream cylinders, while the side-by-side cylinders display distinct frequency responses. Motion trajectories transition from figure-of-eight patterns to enclosed loops as $U_r$ increases, with specifically complex oscillations emerging at higher velocities. These findings provide insights into multi-body VIV, relevant to offshore structures, marine risers, and heat exchangers. Full article

The deep learning method of the recurrent neural network (RNN) is applied to predict the chaotic vibrations of a laminated composite cantilever beam. The RNN model converts time series data into a multi-step supervised learning format and normalizes it using MinMaxScaler. The cantilever structure is subjected to an evenly distributed load, and a series of chaotic vibrations are observed corresponding to different amplitudes and angular velocities of the load. Then, the RNN data-driven model is applied to predict chaotic vibrations, and the chaotic vibration prediction of RNN is evaluated. The prediction results are primarily evaluated using two metrics: mean absolute error (MAE) and root mean square error (RMSE). The analysis results show that the maximum MAE is 0.041 and the maximum RMSE is 0.067. Even under perturbed initial conditions, the RNN model maintained high prediction accuracy, with a maximum MAE of 0.022 and RMSE of 0.038, highlighting its robustness and reliability in predicting chaotic vibrations. The error analysis indicates that the RNN accurately predicts chaotic vibrations with a high degree of precision. Full article

Blasting excavation can pose significant risks to adjacent structures, particularly during concrete pouring. Therefore, evaluating their safety is crucial. In addition, the influence of blasting vibration on the vibration of the foundation and the superstructure is different. Currently, there are only allowable vibration values in the time domain range affected by blasting construction on the foundation structure at vibration frequencies of 1–10 Hz and 50 Hz. There is a lack of allowable vibration values in the range of 10–50 Hz. Based on a liquefied natural gas (LNG) project in Zhejiang, China, this paper studies the safety evaluation index for the vibration of piles under the storage tank through in situ blasting tests and numerical simulations. The vibration velocity attenuation curve of the site, which can accurately predict the pile vibration velocity induced by blasting, is obtained by fitting the experimental results using Sodev’s formula. It is found that the vibration velocity gradually increases from the pile toe to the pile top. As the distance to the blasting source increases, the maximum vibration velocity of the pile top gradually decreases. The peak vibration velocity at the pile top is different from that at the ground surface around the pile. Their ratio, which can reach up to 1.33, gradually increases with the decreasing distance to the blasting source and the increasing concrete strength. The predominant frequency is greater than 10 Hz. For the pile whose concrete strength is lower than 50% of the design strength, blasting has little impact when the vibration velocity is less than 10.16 mm/s. The experimental results supplement the relevant experimental data within the range of 10–50 Hz. This study can provide references for similar projects. Full article

The objective of this work is to study the equilibrium stability of a switched linear model with time-delayed control and additive disturbances, that in subsidiary represents the control of wing vibrations in the presence of the turbulence disturbances in an aerodynamic tunnel. The state system is modeled as a collection of subsystems, each corresponding to different levels of air velocity in the wind tunnel. The problem is closely related to the gain scheduling approach for stable control synthesis and to the design of stable, switched systems with time-delay control. A state-predictive feedback method is employed to compensate for actuator delay, resulting in closed-loop free delay switching systems both in presence and absence of disturbances. The main contribution of this study is a thorough analysis of system stability in the presence of disturbances. Finally, numerical simulation results are provided to support and complement the findings. Full article