Search Results (9,467)

Rotating machinery is a key component in modern industry, and its operating condition directly affects equipment safety and production reliability. However, discrepancies among different machines cause source–target distribution shifts, while fault annotation for target machines is costly, limiting the performance of deep learning-based diagnosis under cross-machine scenarios with limited labels. To address these issues, this paper proposes a multi-scale time–frequency semantic consistency model based on self-supervised transferable representation learning, termed MTFSC. First, augmented waveform views and multi-scale frequency-domain views are constructed from unlabeled source-domain vibration signals for self-supervised pre-training without source labels. Then, a time-domain impulse-aware feature extractor and a time–frequency decoupled spectral feature extractor are designed to enhance local impulsive responses and emphasize fault-sensitive time–frequency patterns. Furthermore, a semantic-aware soft contrastive loss is developed to mine potential semantic neighbors from multi-scale frequency-domain structural similarity, reducing false-negative effects in conventional hard-label contrastive learning. Finally, the pre-trained time-domain extractor is transferred to the target machine and fine-tuned with limited labeled samples. Experimental results show that MTFSC outperforms comparison methods under different labeled sample ratios and achieves an average accuracy of 97.5% across four cross-machine diagnostic tasks. Full article

This work examines how well a Negative Derivative Feedback (NDF) controller suppresses vibration in a nonlinear electromechanical oscillator that is subjected to mixed excitations. Coupled nonlinear ordinary differential equations are used to model the system and show how mechanical and electrical components interact. The method of multiple scales (MMS) is used to develop analytical approximate solutions up to the second order, specifically for the primary resonance scenario. This study’s main contribution is a thorough bifurcation analysis and proof of the NDF controller’s high efficacy, which effectively lowers the first and second mode resonance amplitudes by roughly $99.8\%$ and $98\%$., respectively, with impressive reported effectiveness values of roughly 590 and 51.5. Additionally, the quantitative error analysis between the numerical simulation and the analytical approximation solution demonstrates a high degree of agreement, with a maximum error of less than ${10}^{-5}\%$ for the second mode and just $0.01\%$ for the first mode. Furthermore, we present the impact of parameters on FRCs. Frequency response curves (FRCs) are used in a thorough comparison analysis to assess the behavior of the system both before and after the controller is activated. A strong degree of connection between the analytical conclusions and numerical simulations carried out using the “fourth-order Runge–Kutta method” rigorously validates the accuracy of the perturbation analysis. Additionally, a performance benchmark between different control techniques, such as the NDF controller, Positive Position Feedback (PPF), and Linear Negative Position Feedback (LNPF), is shown in the paper. When compared to alternative approaches, the NDF controller shows the greatest reduction in oscillation amplitudes and higher robustness, as shown by transient response analysis (time history) at various time intervals. The outcomes validate the NDF approach’s dependability and efficiency in stabilizing intricate nonlinear electromechanical systems. The chaotic response and system periodicity were demonstrated through bifurcation diagrams and Poincaré maps. Full article

This paper proposes an integrated attitude control framework for flexible spacecraft subject to external disturbances, rigid–flexible dynamic coupling, and actuator faults. The control framework combines the Twin Delayed Deep Deterministic Policy Gradient (TD3) reinforcement learning algorithm with an adaptive fault-tolerant (AFT) compensator. First, a rigid–flexible coupling dynamic model is formulated using Modified Rodrigues Parameters. Second, an observer-based TD3 attitude controller is designed, where a hierarchical reward function incorporating the observer-estimated flexible modal displacement $\widehat{\mathit{\eta }}$ is constructed to train the agent for simultaneous attitude convergence and vibration suppression. Third, a composite fault-tolerant control structure is developed by integrating the trained TD3 policy with an adaptive sliding mode compensator that handles both partial loss-of-effectiveness faults and time-varying additive faults. The proposed framework is evaluated under a progressive five-scenario uncertainty evaluation framework encompassing measurement noise, parameter mismatch, external disturbances, and actuator faults. Simulation results demonstrate that (i) the $\widehat{\mathit{\eta }}$-augmented reward enables substantial improvements in vibration suppression over the baseline reward, achieving a better balance between pointing accuracy and vibration attenuation; (ii) under the most demanding fault scenario, the AFT compensator proves essential for precise convergence, and the composite TD3+AFT architecture achieves the best overall performance among the four compared control schemes. Full article

Horizontally buried rectangular hollow pipe barriers are investigated as a potential solution for mitigating ground-borne vibrations in densely built environments. This study combines high-fidelity three-dimensional finite-element analyses, a computationally efficient two-dimensional plane-strain modeling strategy, and a Python-based automated optimization framework to evaluate the effects of barrier geometry and material properties on vibration isolation performance. The results show that vertical vibration attenuation is consistently better than horizontal attenuation. Among the geometric variables, burial depth and barrier width are the dominant factors, with isolation benefits becoming marginal when the burial depth exceeds approximately 3 m and with barrier widths smaller than about 0.5 m leading to poor performance. The material parametric study indicates a threshold behavior for stiffness contrast: the improvement in isolation gradually saturates when the Young’s modulus ratio of barrier to soil exceeds about 5.12, suggesting that reinforced concrete provides a practical balance between structural reliability and engineering applicability. A comparison between the three-dimensional and two-dimensional models shows that the plane-strain approximation can reproduce the three-dimensional results with acceptable accuracy while substantially reducing the computational demand. The automated optimization further identifies high-performing design configurations for practical application. Overall, the study offers numerical insight and computational guidance for the preliminary design and evaluation of rectangular hollow pipe barriers for ground vibration mitigation. Full article

Given the widespread application of multi-story modular container building structures, this article proposes a new seismic isolation system called the “sliding friction isolation system (IS)” that utilizes friction energy dissipation between containers. Firstly, lateral stiffness tests were conducted on a 20 ft container, a 40 ft container, and 20 ft connected containers. The constraint consists of four fixed-bottom corner pieces, and the load is achieved using a symmetrical longitudinal concentrated loading method. Their stiffness values were 58.07 kN/mm, 33.41 kN/mm, and 60.03 kN/mm, respectively, providing the necessary parameters for IS. Secondly, an IS model was established, and based on the theory of random vibration, the relationship between c_ei (the equivalent damping of i layer of the structure) and μ (the inter-layer friction coefficient) of the system was obtained. Thirdly, a nonlinear finite element model of a six-story container building was established. Namely, the non-isolation system with standard damping ratios (NIS-sdr), the non-isolation system with equivalent damping ratio (NIS-edr), and the IS. Elastic-plastic nonlinear time-history analyses were then conducted to study the dynamic responses of three systems under strong earthquakes. The analyses yielded the top displacement of the structure, each structural layer’s maximum displacement and displacement angle, the slip of each layer, the hysteresis loops, and the cumulative dissipated energy of IS. The results show that compared to NIS sdr and NIS edr, IS can effectively reduce the maximum interlayer displacement. The largest angular displacement between the structural layer of IS and NIS-edr is far less than that of NIS-sdr. The spectral characteristics of seismic waves (the EL-Centro wave, Taft wave, and artificial wave) can significantly affect the dynamic response of IS. Additionally, the length of the sliding hole on the corner piece can be set to 35 mm based on the displacement of each layer under the Taft wave to meet the standards for container houses (T/CECS 1932-2025). Full article

Changes in wood moisture content significantly affect its dimensions, mechanical properties, and vibrational characteristics. Thermal treatment is one of the most convenient approaches for improving the moisture resistance of wood; however, the effects of treatment conditions on moisture content and vibrational characteristics after short-term cyclic moisture absorption have not been clearly investigated. In this study, dry thermal treatment at 160–220 °C for three different durations was applied to Japanese cedar specimens. Higher thermal treatment temperatures and longer treatment times decreased the equilibrium moisture content (EMC). The fundamental resonant frequency of the free–free flexural vibration (f₁) increased with increasing treatment temperature, whereas it decreased over a longer duration. All specimens were subjected to three cycles of moisture change tests from 60%RH to 98%RH at 40 °C to track the change in moisture content, f₁ and its loss tangent (tanδ). The specimens treated at higher temperatures maintained a lower moisture content and higher f₁. Under most treatment conditions, the moisture content at 98%RH increased from the first to the second cycle and remained constant in the third cycle. On the other hand, the resonant frequency at 98%RH remained unchanged from the first to the second cycle but increased in the third cycle. This indicates that the moisture surface became saturated in the second cycle, and moisture diffusion from the surface to the inside of the specimen increased with the number of cycles. Near-infrared absorption revealed that high-temperature treatment caused thermal decomposition of hemicellulose and an increase in apparent crystallinity due to a reduction in the amorphous region of cellulose. These changes enhance the hydrophobicity of the cell wall, contributing to moisture resistance and vibrational stability. Full article

Early fault diagnosis of ball bearings is essential for maintaining the reliability of rotating machinery and preventing unexpected downtime. This study proposes a fault diagnosis framework that combines non-contact ultrasonic visualization with a lightweight convolutional neural network (Light CNN). Seven bearing conditions, including ferrous particle contamination and grease starvation, were investigated using ultrasonic, vibration, and acoustic emission (AE) sensors under identical experimental conditions. Sa-liency-map extraction and two-dimensional histogram analysis were applied to ultrasonic RGB images to generate compact feature representations, which were compressed into 20 × 20 feature maps and used as inputs to a three-layer Light CNN. The proposed method achieved an average classification accuracy of 99.98% and an F1-score of 99.98%. In addition, an average inference throughput of 11.47 IPS was obtained, representing approximately ten times higher computational efficiency than vibration- and AE-based approach-es. Stable diagnostic performance was also maintained under a low-speed operating condition of 500 rpm. These results demonstrate the effectiveness of combining ultrasonic visualization and a lightweight CNN for accurate and computationally efficient bearing fault diagnosis. Full article

Recent advancements in structural engineering have driven the development of sophisticated damping mechanisms aimed at reducing the detrimental effects of structural vibrations. As a result, accurate numerical modeling and analytical evaluation have become essential for assessing structural stability and enhancing seismic resilience. This study introduces a model-updating framework to develop analytical constitutive models for structural damping systems. The proposed approach employs a genetic algorithm (GA) to calibrate model parameters by minimizing the discrepancy between analytical predictions and experimental responses. Experimental force–displacement hysteresis data and displacement time-history records are used at both the element and system levels for model calibration. The methodology is applied to a rubber isolator, a 10-story structure equipped with Pall friction dampers, and a 6-story structure with friction dampers to evaluate its performance under different dynamic characteristics and damping mechanisms. The results indicate that the proposed approach achieves very high accuracy, with prediction errors reduced to negligible levels for both force and displacement responses in all cases. Consistent performance is observed using both global and local displacement measures in friction-damped systems, indicating the robustness of the proposed method. Overall, the findings indicate that the GA-based model-updating framework provides an efficient and reliable tool for improving the predictive capability of analytical models of structures with nonlinear damping devices and is suitable for practical structural engineering applications. Full article

Local impedance variations in structural waveguides partially reflect and absorb incident
flexural waves, motivating wave-based strategies for passive vibration control. This study
develops and experimentally validates a wave-energy framework to quantify and optimize
flexural wave absorption by Kelvin–Voigt attachments on a finite Timoshenko beam.
A finite element model is validated against Scanning Laser Doppler Vibrometry measurements
from a clamped–clamped aluminum beam with a passive damper mounted near
one end, with dashpot parameters identified through two independent approaches and
the discrepancies attributed to parameter uncertainty. Wave decomposition of the simulated
and measured velocity fields yields the power reflection coefficient ρ(ω) and power
absorption coefficient α(ω) over the 0–15.3 kHz band. The spring stiffness and damping
coefficient exhibit frequency-dependent optima and act as complementary, jointly tuned design
variables. Expressing dashpot location in wavelength-normalized coordinates reveals
a recurring spatial pattern in which absorption minima cluster around half-wavelength
multiples, while multiple spanwise positions yield near-peak absorption at any given
frequency. This pattern is governed primarily by the flexural wavelength, decoupling
placement from parameter tuning, and persists across clamped–clamped, clamped–free,
and free–free boundary conditions. Two independently tuned dampers further broaden the
effective absorption band by suppressing local minima in α(ω). These results demonstrate
that measurement-driven wave decomposition provides compact, physically grounded
guidelines for passive damper placement in beam structures. Full article

To address the non-stationary and non-linear characteristics of vibration signals collected by sensors in pumped-storage units, as well as their susceptibility to strong background noise interference, this paper proposes a joint signal denoising method combining parameter-adaptive Variational Mode Decomposition (VMD) and wavelet thresholding. First, the Improved Particle Swarm Optimization (IPSO) algorithm is utilized to adaptively optimize the key parameters of VMD using a comprehensive fitness function as the objective, thereby achieving the optimal decomposition of the signal. Subsequently, a cross-correlation analysis method is introduced to screen the decomposed components, followed by a secondary denoising process using a wavelet threshold to accomplish the final signal denoising. Experimental validations using simulated run-out signals and field-measured sensor data from a pumped-storage power station, along with comparisons against other methods, demonstrate that the proposed method can eliminate noise more effectively. It significantly improves the signal-to-noise ratio (SNR) and reduces the root mean square error (RMSE). Consequently, this study provides a reliable data foundation for the subsequent research and analysis of the units, demonstrating substantial practical engineering significance. Full article

This work presents a systematic investigation on dry milling of TC18 forged alloy using longitudinal ultrasonic vibration assistance. The effects of key parameters (cutting speed, feed per tooth, cutting depth and ultrasonic amplitude) on three-axis cutting forces, cutting temperature and surface quality are explored, and orthogonal experiments are conducted to determine the optimal parameter combination. Results reveal that increasing ultrasonic amplitude reduces cutting temperature by 31.8% and suppresses cutting forces effectively. Cutting depth and feed per tooth act as major influencing factors; the three-directional cutting forces drop by 31.1%, 56.7% and 22.9%, respectively. Surface roughness rises to 0.435 μm and 0.29 μm with growing feed per tooth and cutting depth, and decreases to 0.24 μm at higher cutting speeds. Under ultrasonic assistance, roughness increases slightly first and then declines remarkably. A threshold value exists for ultrasonic amplitude, and periodic tool–workpiece contact transforms strip textures into fish-scale morphologies. Proper parameter matching for ultrasonic milling lowers cutting forces and temperature, and improves surface quality of TC18 alloy. This study offers experimental data and theoretical references for relevant machining research. Full article

To address the critical challenge of 0–100 Hz low-frequency vibration control for marine hydraulic pipelines, this paper proposes a dedicated fiber-reinforced magnetorheological elastomer (MRE) isolator and a genetic algorithm-optimized fuzzy control strategy utilizing the magnetically tunable properties of MREs. An upper-lower split-type isolator is designed to suppress axial and radial vibrations through the shear and Compression Modes of MRE, respectively, and a two-degree-of-freedom (2-DOF) dynamic model is established to analyze the effects of mass ratio and natural frequency ratio on the system’s amplitude magnification factor. A Mamdani-type fuzzy controller, with acceleration error and its rate of change as inputs and control voltage as output, is optimized via a genetic algorithm. Simulation and experimental results show that 31–56.5% amplitude attenuation is achieved under 25–35 Hz single-frequency excitation; 12 dB isolation in the 5–23 Hz band at the input end and a maximum 15 dB isolation in multiple bands for the suspended pipeline section are obtained without external forced excitation; and efficient 0–100 Hz full-band isolation is realized at an applied current of 1.5 A. This work verifies the effectiveness of the proposed scheme for low-frequency vibration control of marine hydraulic pipelines. Full article

In this study, free vibration analysis of three-layer sandwich panels with cores based on a triply periodic minimum surface (TPMS) and graphene platelet-reinforced composite (GPLRC) faces is performed. Four different geometries including cylindrical, spherical, saddle and flat panels were investigated and the governing equations were solved using higher-order shear deformation theory (HSDT) extracted from Hamilton’s principle. The accuracy and precision of the presented analytical method is verified by comparing the dimensionless natural frequencies with reference studies. Then, the effect of various parameters including panel geometry, core topology type and graphene weight percentage on the vibration response was investigated. The results show that adding graphene to the face layers significantly increases the natural frequencies and improves the overall stiffness of the structure. In addition, the frequencies of the panel may be controlled through different patterns and topologies. Also, double-curved panels, especially spherical geometries, present the highest fundamental natural frequency. The findings of this research could play an important role in the design and performance evaluation of advanced structures with TPMS cores and nanoscale reinforcement. Full article

A Negative Derivative Feedback (NDF) controller is designed for vibrations suppression of an atomic force microscope (AFM) model. The controlled system is modeled as a two-degree-of-freedom (2-DOF) closed-loop dynamic system. The average method was used to derive approximate analytical solutions. All possible resonance conditions were identified, with particular attention given to the simultaneous resonance case $\mathsf{\Omega }={\omega }_1, {\mathsf{\Omega }}_1={2\mathsf{\omega }}_1, {\mathsf{\omega }}_2={\mathsf{\omega }}_1$, identified as the most critical. For validation and proper insights, the system was also solved numerically using the fourth-order Rung–Kutta method. The time response of the AFM system in contact mode was analyzed before and after applying the NDF controller under the worst-case resonance conditions. A comprehensive parametric study was conducted to evaluate the controller’s robustness and effectiveness. The results demonstrate a high degree of agreement between the numerical simulations and the analytical approximations, confirming the reliability of the approach. Full article

We present a review of theoretical studies of the structure and reactions of N = 7 and N = 8 nuclei in the vicinity of ¹¹Li, carried out within a framework based on Nuclear Field Theory. The coupling of valence nucleons to low-lying surface vibrations of the spherical core plays a central role, giving rise to self-energy processes that renormalize single-particle states and transfer form factors, as well as to an induced pairing interaction arising from the exchange of collective vibrations, which renormalizes the bare pairing force. Excitation spectra and cross sections for one- and two-nucleon transfer reactions populating states in the quasi-continuum are calculated and compared with available experimental data. Collective excitations in the particle-particle channel are investigated, with particular emphasis on Giant Pairing Vibrations and on their damping mechanisms arising from coupling to more complex configurations and continuum states. Comparisons with other theoretical schemes are also presented. We conclude that a coherent understanding of experimental data requires the detailed consideration of particle-vibration coupling effects. Full article