Journal of Vibration and Control

The study has contributed a novel method in simultaneously optimizing the acceleration and relative displacement of the driver’s seat suspension in multiple load cases (normal, average, and heavy) and road excitations (transient and random) using Pareto-Weighted Square Sum with Particle Swarm Optimization (PSO) while satisfying complex constraints on comfort, stability, and system safety. The highlight of the optimization process lies in considering the dynamic influence of the X-shaped guide system on the behavior of elastic and damping components. The study has proposed a method to design and integrate the Proportional-Derivative Fuzzy Logic (PD FLC) and Decoupled Sliding Mode (DA-SMC) control systems into the seat’s suspension to reduce driver displacement. The novelty of this method is that the controllers are optimized for many technical parameters simultaneously with PSO; the PD FLC set includes 03 coefficients K ₁ , K ₂ , K ₃ ; and the DA-SMC set includes 03 coefficients p, η, Δ. The controllers help reduce seat displacement with an efficiency of over 12.5% when subjected to transient excitation and over 30% when subjected to random excitation.

This study investigates the simulation of real-world load conditions under indoor experimental setups to enhance hydraulic system performance. An electromagnetic proportional relief valve is employed to control the loading gear pump as part of the loading system. Using LabVIEW programming software, a pump-controlled motor hydraulic test bench achieves simulated loading, enabling pressure simulation for variable-speed pump-controlled motor hydraulic systems. To address drawbacks such as steady-state error, overshoot, and hysteresis in the open-loop loading system, a PID (Proportional Integral Derivative,PID)-based pressure closed-loop loading control module is designed and implemented. Results demonstrate that the closed-loop loading system effectively mitigates the steady-state error, overshoot, and hysteresis of open-loop loading. While open-loop loading exhibits good rapidity through typical step, sinusoidal, and ramp flow disturbances, the results show that pressure closed-loop loading exhibits good accuracy and stability. By utilizing an electromagnetic proportional relief valve for controlling the loading gear pump, this study successfully achieves simulation-based loading control for variable-speed pump-controlled motor systems. The findings provide a reliable theoretical foundation for developing loading technologies capable of simulating real-world load conditions.

This study investigates the potential for modulating and tuning the frequency bandgaps of 2D lattice materials using uniform strain fields to provide a path for realizing continuous and predictable tuning over a range of strains. Accordingly, it analyzes the effect of multiple levels of in-plane uniform strain fields on the frequency bandgaps in the range of 0 to 1000 kHz of three lattices comprising hexagonal, Kagome, and anti-chiral topologies. Two strain directions and three levels are considered. A finite element framework is used to evaluate the bandgap behavior of each lattice at relative densities ranging from 5% to 30%. The results show that lattices exhibit a distinct response to stretching. In hexagonal honeycombs, stretching generally increases wave transmissibility by narrowing or eliminating existing bandgaps, particularly in the higher frequency range. Kagome lattices exhibit more complex and strain-directional behavior, where large lateral strains increase the number of bandgaps in a non-monotonic manner, while axial strains significantly reduce the number of bandgaps. The anti-chiral lattices retain a rich bandgap structure under all conditions. Across all lattices, the applied strains switched bandgaps on and off, induced bandgap shifts up to 50% (honeycomb), and narrowed bandgaps widths by as much as 60% (Kagome). Strain-induced activation/deactivation, shifting, and narrowing exhibited a complex and strong dependence on relative density. Overall, the results demonstrate that lattice topology, strain mode, and relative density can be used collectively to tailor and modulate frequency bandgaps, offering a practical path for designing programmable and reconfigurable metamaterials.

The Rijke tube is widely used as an experimental platform for studying thermoacoustic instability. However, environmental changes in the experiment may affect model parameter settings, complicating accurate control design. To tackle this challenge, this paper proposes an ODE-based modeling and parameter identification method for the Rijke tube. Using a sinusoidal sweep signal as the excitation input, we propose a parameter identification method based on the linearized transfer function structure of the ODE model, employing a nonlinear least squares approach. The output of the identified model is compared with that of the original system. Moreover, to address the mismatch between the nonlinear amplitude growth of the system’s output and the limitations of the linear model, a nonlinear growth analysis based on the Sigmoid-Prony approach is developed, in which the exponential envelope of classical Prony is replaced by a sigmoid function to capture the full amplitude saturation dynamics with physical interpretability. It can be applied to analyze the output of the nonlinear DDE system, extracting the effects of time delay and heat release parameters on system behavior. The simulation results indicate that the linear model identification method is limited to address the system uncertainty, whereas the Sigmoid-Prony method effectively captures the nonlinear amplitude growth dynamics lost due to linearization, as well as the impact of time delay and nonlinear heat release on the system’s parameters.

In this study, the crack detection problem in steel cantilever beams with rectangular cross-section has been addressed under extremely limited experimental data conditions. Starting with only 16 experimental configurations (4 beam systems × 4 crack depth levels: 0–3 mm), the effectiveness of Sliding Window-based data augmentation strategy has been systematically investigated. Under baseline conditions, classical machine learning models showed moderate performance: Random Forest achieved 62.5% accuracy in crack depth classification, while XGBoost obtained 25.0% accuracy. Deep learning models could not be trained at all with 16 samples. Sliding window application transformed 16 samples into 304 windows, making model training possible and improving performance dramatically. XGBoost achieved perfect crack depth classification (100% accuracy) and 0.0744 MAE in dimensionless crack location prediction. LSTM became the best deep learning model with 0.1202 MAE in location prediction. Statistical analysis confirmed substantial improvements with large effect sizes (Cohen’s d up to 1.73 for depth classification). The study shows that high-accuracy crack detection is possible with limited experimental data through appropriate data augmentation strategies, proposing a cost-reducing solution for structural health monitoring applications where data collection is expensive and time-consuming.

This paper develops an iterative learning control (ILC) scheme for gantry crane systems subject to external disturbances. The system dynamics are modeled by partial differential equations (PDEs) with boundary conditions. To suppress vibrations and to compensate for unknown boundary disturbances, an ILC law incorporating boundary feedback control is proposed. The operator semigroup theory is employed to establish the well-posedness of the closed-loop system. Exponential stability under the proposed controller is rigorously proven using Lyapunov’s direct method. Comparative simulation results demonstrate the effectiveness and superior performance of the presented ILC scheme, validating its practical applicability.

This paper presents a comprehensive finite element model for analyzing the static and free vibration behavior of laminated composite circular cylinders (LCCCs) using the Carrera Unified Formulation (CUF). The displacement fields are formulated within the equivalent single-layer (ESL) framework of CUF, enhanced with higher-order polynomial expansions to accurately capture both in-plane and through-thickness deformation characteristics. Curved isoparametric shell elements are employed, and the governing equations are derived using the Principle of Virtual Displacement (PVD). The mathematical model accommodates various realistic boundary conditions and loading types, allowing detailed assessment of displacements, stress distributions, and natural frequencies. Parametric studies are carried out to examine the influence of slenderness ratio ( L / h ) , radius ( R ) , fiber orientation angle ( θ ) , and stacking sequence on structural performance. The results show excellent agreement with existing benchmark solutions, validating the reliability and versatility of the CUF-based model for analyzing both thin and moderately thick composite cylinders in structural applications.

This study presents an LSTM-based damage localization framework for building structures. The model is trained exclusively on acceleration responses measured in the healthy state, and damage localization is performed by analyzing, in the frequency domain, the discrepancy between the model-reconstructed response and the corresponding measured signal under the damaged state. To more accurately encode the structural dynamics, multistory acceleration responses from the healthy state are provided with selected story channels intentionally masked at the input, and the network is trained to reconstruct the masked channel at the output. When damaged-state acceleration responses are fed to the trained LSTM, a discrepancy arises between the model predictions and the measured responses. This discrepancy is transformed to the frequency domain via FFT and used to define the Frequency Feature Damage Index. The proposed procedure is implemented on a four-degree-of-freedom (4DOF) shear-frame numerical model subjected to white Gaussian noise excitation, where multiple damage scenarios are generated for training and validation. Its practical applicability is further examined using structural responses obtained from a shaking-table experiment on a three-story steel moment frame, through which the effectiveness and accuracy of the localization strategy are verified.

In this paper, an enhanced vibrational energy harvesting system for freight wagons is developed by the concept of Auxetic resonators. The novel idea of using Auxetic structures is employed to increase the rate of power conversion by simply changing the structure of the beam without any need to change the geometry of the piezoelectric element or its material. The harvesting system is a resonating cantilever beam, and a piezoelectric element is attached on the beginning section of the beam modeled in Finite Element. Two auxetic patterns are proposed on the beam, exactly under the piezoelectric element. The vibrational input is simulated by modeling the motion of a freight wagon on the railway track using the Universal Mechanism software under different track irregularities and train speeds. The harvesting system is mounted on the axle-box where the vibration intensity received from track irregularity has the highest level. Different track irregularities and train speeds are considered in a parametric study to investigate the performance of the harvesting device. The results show that the application of the Auxetic I and Auxetic II structures enhances the output power by approximately 2 times and 4 times, respectively, compared to the plain beam. Experimental validation further confirmed the numerical results, showing up to 3.5 times improvement in harvested power. These findings demonstrate the potential of auxetic structures for efficient energy harvesting in railway vehicle applications.

Nonlinear Energy Sink (NES), as an innovative passive vibration control technology, has attracted attention for broadband vibration suppression owing to its high efficiency. This paper investigates the parametric resonance and bifurcation behavior of a spatial cable coupled with an NES under parametric excitation. An accurate nonlinear dynamic model of the cable-NES system is first formulated, capturing the essential nonlinear coupling mechanisms and energy transfer pathways. Theoretical analysis reveals complex nonlinear dynamics, including bifurcations induced by system parameters. The dynamic responses under primary parametric resonance are systematically examined using the Homotopy Analysis Method (HAM), which highlights the significant influence of subtle NES parameter variations on the cables vibration. Furthermore, the fourth subharmonic resonance of the cable under parametric excitation is explored. Results demonstrate that optimal placement of the NES at the cable end yields superior vibration mitigation compared to mid-span attachment. Moreover, appropriate tuning of NES damping enables the cable response to settle into a stable steady state. This study elucidates the internal mechanism of parametric resonance in the cable-NES system, providing a theoretical basis and practical guidance for vibration control in bridge engineering.

This study conducts a 3D comparison of Triple (TFPI) and Quintuple (QFPI) Friction Pendulum isolators for a six story steel building. Its key innovations are two-fold: first, a performance comparison based on optimally designed isolators with closely matched parameters (such as effective period, effective stiffness, etc.); and second, the novel use of a hybrid design method integrating Capacity Spectrum Analysis with the Equivalent Lateral Force (ELF) procedure for the isolator design. The superstructure was designed in accordance with ASCE/SEI 7-2016 and models developed in SAP2000 were validated against OpenSees and literature. Analysis under seven bidirectional far-field records on stiff soil showed QFPI’s superiority, reducing story acceleration and shear by an additional 34% and 30% versus TFPI. While both isolators controlled drift comparably, QFPI achieved this with a 3.5 times more compact design and fewer constraints, demonstrating greater efficiency and practicality.

This article proposes a robust nonlinear control strategy for a full-vehicle active suspension system equipped with hydraulic actuators to improve ride comfort and enhance vehicle body stability under road disturbances, model nonlinearities, and lumped uncertainties. A seven-degree-of-freedom full-vehicle model is established, including heave, roll, and pitch motions of the sprung mass, vertical dynamics of the four unsprung masses, and hydraulic actuator dynamics, while nonlinear spring and damping characteristics are explicitly considered. To simultaneously guarantee heave, roll, and pitch stability with minimum actuator effort, an optimal active force allocation method based on the Moore–Penrose pseudo-inverse is introduced to distribute the generalized control forces among four independent hydraulic actuators. A cascade control structure is then developed, in which adaptive fixed-time sliding mode control (AFxTSMC), integrated with active disturbance rejection control (ADRC), is employed in the outer loop to generate the desired control force. At the same time, an inner-loop proportional–integral–derivative (PID) controller regulates the hydraulic servo valve for accurate force tracking. Lyapunov stability analysis is performed to verify the boundedness of all closed-loop signals and the fixed-time convergence of the tracking errors. Comparative simulations under sinusoidal and random road excitations are conducted against PID-ACO, ADRC, and SM-DRC methods. Under sinusoidal excitation, the proposed controller achieves the lowest root mean square (RMS) vertical body acceleration of 0.014 m/s², corresponding to reductions of approximately 17.6%, 26.3%, and 75.0% compared with SM-DRC, ADRC, and PID-ACO, respectively. Under random road excitation, the vibration dose value (VDV) decreases to 0.506 m/s^1.75, representing improvements of 35.5%, 39.9%, and 85.2% over SM-DRC, ADRC, and PID-ACO, respectively. Furthermore, the roll and pitch angles are effectively constrained to 0.012° and 0.003°, demonstrating the robustness of the proposed control strategy under complex operating conditions.

This study proposes an intelligent diagnostic framework for high-accuracy, low-latency monitoring of heavy-haul railway wheelset wear under limited computational resources. The framework combines Variational Mode Decomposition (VMD)-based signal decomposition, multi-domain entropy features, and a lightweight attention-based classifier. The Grey Wolf Optimizer (GWO) is used offline to determine the key VMD parameters, which are then fixed for narrowband decomposition of wheel–rail contact acoustic signals during online diagnosis. Adaptive Time-Domain Discrete Entropy (ATDE) and Frequency-Domain Entropy (FDE) are extracted from each modal component to characterize dynamic complexity and energy distribution, thereby enhancing feature robustness and class separability. A One-Dimensional Convolutional Neural Network (1D-CNN) with an attention mechanism then reweights the features and identifies the wear stage. In the four-stage wheelset wear task, the method achieves 99.88% test accuracy and 99.713% mean accuracy over ten independent runs. Compared with time–frequency image-based deep models, the classifier reduces forward inference time by about 99.74% and uses only 13,848 parameters. These results demonstrate improved computational efficiency without sacrificing accuracy, supporting real-time deployment in resource-constrained conditions. Validation on the HUST bearing dataset further suggests its applicability to different mechanical diagnostic tasks.

The accurate identification of abnormal impact loads generated by under-vehicle equipment is recognized as critical for assessing structural integrity in high-speed trains. This inverse problem is addressed through a novel load identification approach, which leverages sparse regularization principles. The precision of impact load identification is enhanced through a weighted l₁-norm regularization framework, aimed at mitigating solution ill-posedness and minimizing peak force errors. This framework is efficiently solved by an optimized TwIST algorithm, into which an iterative reweighting technique is incorporated. Furthermore, an improved AIC is employed for optimal regularization parameter selection, and an analysis of sample size selection is complemented. The method proved accurate and robust, as verified by comprehensive simulations and scaled carbody experiments. The proposed method is demonstrated to significantly outperform classical l₁-norm regularization in identifying impact loads, with superior accuracy and increased robustness exhibited with respect to measurement point selection and noise contamination.

The vibrations generated by vehicles passing through cross-river bridges can be transmitted to underwater foundations, which in turn generates underwater environmental vibrations, and these long-lasting underwater environmental vibrations can be harmful to aquatic organisms. Therefore, effective vibration control measures must be applied to reduce the effects of underwater environmental vibrations. As such, the bandgap properties of phononic crystals are exploited in the present study for this vibration control. Specifically, scaled models of bridge tower and periodic piles are designed and constructed according to the design plan of a suspension bridge. Then, a reasonable phononic crystal structure is selected, and the bandgap properties are investigated by using the finite element method. Finally, the vibration attenuation test of the scale model is carried out to study the attenuation effects of the phononic crystal structure. The research shows that the optimized vibration attenuation effect of the bridge tower and the periodic piles reaches more than 80% in the theoretical band gap range of the phononic crystal. Meanwhile, the frequency range of the actual vibration attenuation is consistent with the results of the finite element calculations, which validates the reliability of the simulation and also proves the feasibility of the phononic crystal structure in the underwater low-frequency environmental vibration control of the suspension bridge.

In heat exchangers, cold and hot fluid flow during heat exchange generates noise. This noise propagates through connected piping systems and considerably affects the acoustic stealth performance of vessels. With increasing flow rates and the trend toward larger-scale heat exchangers, noise issues have become more pronounced, highlighting the urgent need for effective noise control strategies. However, the underlying excitation mechanisms, noise sources, and radiation characteristics of flow-induced noise in liquid-cooled heat exchangers remain poorly understood. This gap renders vibration and noise control efforts ineffective and insufficient to support the low-noise design of heat exchangers. To address this gap, herein, we performed numerical analysis using computational fluid dynamics and acoustic analogy theory. Noise levels were calculated under both shell-side and tube-side flow conditions. A comparative analysis revealed that heat exchanger noise is primarily driven by shell-side flow excitation, specifically, the sound pressure level of noise induced by shell-side flow is approximately 26 dB higher than that induced by tube-side flow. Furthermore, under shell-side excitation, the radiated noise at the tube-side inlet and outlet is about 5 dB higher than that at the shell-side ports. Considering that the tube-side passages are directly connected to the vessel’s seawater piping system, this finding underscores the critical necessity of investigating and mitigating heat exchanger noise. Further, the contributions of different structural panels within the heat exchanger were analyzed. Color-mapped diagrams depicted the noise contributions of individual panels. Based on vector projection, a normalized panel contribution evaluation method was established to enable quantitative comparisons of the contributions of structural panels across different frequencies. The results indicated that acoustic radiation generated by heat exchanger tube vibrations constitutes the dominant noise source. Finally, experimental measurements were conducted to verify the accuracy of the numerical calculations. Overall, this study’s findings clarify the mechanism of flow-induced noise generation in heat exchangers and guide low-noise heat exchanger design, thereby supporting improvements in acoustic stealth performance.

This paper proposes a new structural damage identification method based on data-driven variational nonlinear chirp mode decomposition (DDVNCMD) and radial basis function (RBF) neural network. Firstly, a novel time-frequency analysis methodology, termed DDVNCMD, is put forward for intrinsic mode function (IMF) decomposition. Next, an RBF neural network is successively constructed, where the extracted IMF components serve as the input and the structural stiffness coefficients are taken as the output. Then, damage localization and quantification are carried out through the trained RBF neural network. Finally, the proposed method is validated by a jacket structure under three environmental loads and three damage scenarios. Results show accurate damage localization and quantification, with maximum absolute errors ranging from 2.516% to 3.279% across all cases, which indicates that the proposed method can effectively locate and quantify damage of jacket structure and exhibits potential for practical structural health monitoring applications.

To address the issue of large residual vibration amplitude and slow decay at the end of the stator measurement arm during intermittent rotation for fixed-point measurements in hydro-generators, which significantly affects measurement accuracy and efficiency, this study proposes an active-passive hybrid control method combining a dynamic vibration absorber (DVA) and a smooth input shaper (SIS) to suppress residual vibrations. First, through theoretical derivation, simulation, and experimental validation, the optimal pendulum length (22 mm) of the DVA was determined. Under this configuration, the DVA reduced the residual vibration peak by 17.1% and shortened the decay time by 25.3%. Subsequently, the proposed SIS shaper was compared with traditional zero vibration (ZV), zero vibration and derivative (ZVD), and extra-insensitive (EI) shapers in terms of residual vibration suppression, time delay, and sensitivity. Simulations and experiments demonstrated that the SIS shaper reduced the residual vibration peak by 91.3% and decreased the decay time by 49.5%. Finally, the integration of these two methods formed the SIS-DVA hybrid control technique. Experimental results confirmed that this hybrid approach further enhanced the suppression capability compared to standalone DVA or SIS, achieving a 94.1% reduction in vibration peak and a 50.9% reduction in decay time.

In order to further improve the accuracy and efficiency of fault diagnosis for rotating machinery such as rolling bearings, A fault diagnosis method for rolling bearings based on Polar Lights Optimization (abbreviated as PLO) for optimizing Feature Mode Decomposition (abbreviated as PLO-FMD), Sparrow Search Algorithm (abbreviated as SSA) and Adaptive Boosting (Adaboost) for Least Squares Support Vector Machine (abbreviated as LSSVM) is proposed. Firstly, in order to reduce the noise interference in the signal, PLO-FMD is used to filter the signal and the filter size and the number of mode components in the feature mode decomposition are optimized by the polar lights optimization algorithm. Afterwards, in order to improve the comprehensiveness and completeness of the feature parameters, the composite multi-scale attention entropy (abbreviated as CMATE) of the reconstructed signal is extracted and used as the feature vector. Again, the sparrow search algorithm with high convergence accuracy and fast speed is introduced to optimize the regularization parameters and kernel function parameters of the least squares support vector machine, and combine the adaptive boosting algorithm to construct the optimal SSA-LSSVM-Adaboost model. The rolling bearing dataset of Case Western Reserve University (CWRU) and Jiangnan University (JN) was introduced for case analysis, the results show that the accuracy rate of this method can reach 98.6111% and 97.9167%, respectively, and even after adding additional noise, it still maintains a high diagnostic accuracy. Compared with different features and learning models (MATE, MFE, SVM, etc.), its computing speed is very fast, it has strong noise resistance, and the diagnostic efficiency is also high. It has certain engineering application value.

The traditional deconvolution method can filter out the interference caused by the propagation path and noise from the complex vibration signal to restore the fault excitation characteristics. In order to solve the influence of the initial filter coefficients on the convergence results of the algorithm and improve the accuracy of fault identification, this paper designed filter optimized minimum entropy deconvolution (FOMED) method. Firstly, the method of mode location and initial filter coefficient construction based on spectrum trend is studied to achieve the rough estimation of frequency domain mode. Then, the filter coefficients based on Meyer wavelet are introduced to update the initial filter banks. Simulation results show that the new iterative updating method has better robustness. Finally, through kurtosis of unbiased autocorrelation of square envelope (AC), the fault information in the filtering results is screened and diagnosed. The experimental data verify that the proposed FOMED is suitable for fault detection of planetary gearbox in wind turbine transmission system and rolling bearing in rotating machinery equipment.