To ensure the working performance of mistuned blisks, a hybrid vibration reduction strategy using the material damping and intentional mistuning capacities induced by the hard coating was proposed here. For solving vibration characteristics of mistuned blisks deposited hard coating efficiently, a novel reduced-order model was established using the improved component modeling method with dual reduction of degrees of freedoms (DOFs) firstly, and then validated by comparing the simulation results from the testing. Next, by the material damping capacity of the uniform-thickness hard coating, the passive vibration reduction strategy for mistuned blisks was investigated, and the specific influence of hard coating on vibration characteristics of mistuned blisks was discussed for the selection of intentional mistuning pattern. Finally, the small intentional stiffness and damping mistuning capacities were constructed by various coating thicknesses of different sectors, and then the hybrid vibration reduction analysis for mistuned blisks, which utilizes the material damping capacity and the small intentional mistuning induced by hard coating simultaneously, was further investigated for strategy validation.
This paper presents a systematic investigation into the dynamical characteristics of a randomly excited multi-degree-of-freedom (MDOF) discrete product packaging system (DPPS) featuring hyperbolic tangent nonlinearity, with emphasis placed on an acceleration response analysis. Because the analytical solution of the acceleration response spectrum for the hyperbolic tangent DPPS has rarely been investigated, then the approximate analytical solution is established to predict the acceleration response of the Gaussian vibration excited system. Secondly, Hermite polynomial is adopted to simulate non-Gaussian vibration excitation, and verified by the actual road excitation signal. On this basis, the non-Gaussian random vibration analysis method is introduced to analyze the acceleration response of the system, the sensitivity of the response is discussed. Then, the failure analysis for the desktop computer package via the first-passage failure probability of the acceleration response is given. The approximate analytical solution can effectively predict the acceleration response of the Gaussian random vibration excited system. The proposed non-Gaussian vibration analysis approach can conveniently reproduce actual non-Gaussian vibration excitation and analyze the dynamic properties. Since the "soft spring" effect of hyperbolic tangent cushion material, there exists an optimal nonlinear parameter beta & lowast;, which can minimize the acceleration response of critical components. The failure mechanism of critical components in a desktop computer package subjected to non-Gaussian vibration is elucidated, and a reliability analysis is presented based on a predetermined product fragility. Moreover, an appropriate cushioning design can be determined for a specified reliability level. This study offers guidance for the vibration absorption design of packaging systems.
Twin-screw pumps are critical fluid transfer equipment in the petrochemical industry. The rolling bearings are one of the core basic components of twin-screw pumps, which operate under demanding conditions such as high temperature, variable speed, and alternating loads. Failure of these bearings significantly compromises operational reliability and service life. This paper investigates a multi-sensor fault diagnosis method based on Improved Feature Mode Decomposition (IFMD) and feature-fused images, aiming to detect incipient damage in rolling bearing faults to ensure equipment operates safely. Firstly, this method employs a correlation coefficient and a Shannon entropy threshold to automatically terminate iteration and filter modal components, enabling accurate extraction of weak fault features of rolling bearings. Secondly, the feature fusion strategy converts multi-channel vibration signals into polar coordinate-based images, in which polar angles integrate multi-channel information, while polar radius distributions reflect state variations caused by faults. Finally, a convolutional neural network (CNN) model integrated with a Convolutional Block Attention Module (CBAM) is constructed, and polar coordinate images are used as dataset inputs to achieve classification of rolling bearing fault types. Experimental results on both laboratory-collected and public datasets demonstrate a diagnostic accuracy of over 96.88%, confirming the robustness and superiority of the proposed method in fault diagnosis.
The nonlinear energy sink (NES), a passive control device, leverages nonlinear stiffness to facilitate targeted energy transfer and localized dissipation of vibrational energy. As variations in stiffness forms and their combinations can significantly affect the NES's dynamic behavior and energy dissipation efficiency. This paper investigates the energy transfer mechanisms and vibration suppression performance of an NES with piecewise linear stiffness, as well as an NES that combines piecewise linear and cubic stiffness. First, the complex-variable averaging method is used to derive the slow-flow dynamics equations for the systems. This analysis reveals how the SIM governs the global dynamics of the system under varying parameter conditions. Additionally, Poincare maps and the topological shape of the slow invariant manifold (SIM) are examined to identify the occurrence of the strongly modulated response (SMR) regime. Finally, multi-parameter synergistic optimization is conducted using the grey wolf optimizer (GWO). A comparative analysis of vibration suppression performance under periodic excitation is performed for three types of NES: the piecewise linear stiffness NES combined with cubic stiffness, the piecewise linear stiffness NES, and the traditional cubic stiffness NES. The results show that the NES with a combination of piecewise linear and cubic stiffness achieves a higher energy dissipation rate and demonstrates greater robustness in vibration suppression under periodic excitation.
The vehicle seat rail is a crucial connection between the passenger seats and the vehicle chassis, featuring complex adjustment mechanisms that result in intricate, nonlinear vibration behavior. Accurate characterization of these dynamics is essential for optimizing designs to meet noise, vibration, and harshness requirements. Traditional linear modal analysis methods are often inadequate in capturing amplitude-dependent behaviors, while nonlinear methods typically require extensive measurements and prior knowledge of the system's nonlinearities. This study employs an advanced experimental modal analysis that combines response- and force-controlled stepped-sine testing with harmonic force surface techniques. This approach extracts quasi-linearized frequency response functions, including amplitude and phase information, without prior assumptions about nonlinearity. A comprehensive measurement campaign, supported by computer tomography, investigates variability in bearing positions and their effects on vibration, quantifying key sources of uncertainty. The presented seat rail analysis demonstrates the possibilities and limitations of linear and nonlinear approaches for weakly nonlinear systems. It also provides practical recommendations for their appropriate application areas. These findings provide valuable insights into the dynamics of automotive seat rails and establish a practical experimental framework. Future work will enhance the accuracy of force identification and extend the method to more complex nonlinear systems, enabling better predictive modeling and design optimization.
In the field of rotating machinery, rotor vibration is an important issue. The most common vibration signal is sinusoidal. Only by accurately identifying the frequency and phase can the vibration be effectively suppressed. For the problem of solving the frequency and phase of rotating machinery vibration signals, a solver is constructed based on an adaptive notch filter (ANF), realizing the solution of frequency and phase of vibration signals. Firstly, the basic principle of ANF is introduced, and the frequency and phase solvers are constructed. Secondly, the influence of ANF parameters on the solution is analyzed. Finally, simulated and experimental signals are used to verify the method. The results show that the method can accurately solve the frequency and phase of sinusoidal signals and achieve zero error for simulated signals. For experimental signals, there is a small deviation from the theoretical value, which may be caused by factors such as sensor installation angle and mechanical assembly.
The ongoing expansion of Chinese cities has led to the growth of urban fringe residential areas. However, green landscapes and soundscapes in these zones are under pressure owing to factors such as housing prices and high-speed traffic. Therefore, the focus of this study is to explore how green landscape factors influence soundscape in urban fringe residential areas. On-site sound environment measurements and a questionnaire survey on perceptions of green landscapes and soundscapes are conducted in the public spaces of 15 typical urban fringe residential areas near Tianjin's outer ring road. The results indicate that: (1) The overall evaluation of soundscape perception in urban fringe residential areas is low, which vary significantly across different spatial types; the core green areas present moderate pleasantness (0.02) and low eventfulness (-0.20), the central squares exhibit high pleasantness (0.05) and moderate eventfulness (-0.15), and public spaces along the streets demonstrate low pleasantness (-0.02) and high eventfulness (0.02); (2) Shannon's diversity index (SHDI) and greening rate (GR) have a positive effect on soundscape pleasantness; the GR and the landscape shape index (LSI) are significantly negatively correlated with soundscape eventfulness; and subjective perception factors have a positive impact on pleasantness; (3) The path analysis of the structural equation model (SEM) support that green landscape perception factors exert a direct positive effect on soundscape pleasantness, whereas objective green landscape factors and spatial environmental perceptions influence pleasantness indirectly through the perception of green landscapes. This study offers theoretical and methodological support for optimizing the green space planning and soundscape of public areas in high-density urban fringe neighborhoods.
Amidst the advancement of intelligent transportation systems, heightened demands have emerged regarding vehicular acoustic environments. Contemporary research has shifted its focus from merely reducing noise to optimizing subjective auditory comfort. Although deep learning techniques have been applied to objective Sound Annoyance Evaluation (SAE), conventional audio feature inputs (e.g., spectrograms, time-series data) demonstrate limitations in sound annoyance recognition efficacy due to their inadequate representation of dynamic acoustic characteristics. To address these challenges, this study proposes: 1) Adoption of Multi-order Differential Mel-Frequency Cepstral Coefficients (MOD-MFCC) that precisely emulate human auditory perception characteristics, facilitating deeper computational comprehension of sound annoyance information representation; 2) Design of a Multiple Auditory Attention (MAA) module employing a hybrid parallel-serial fusion strategy to concurrently extract channel-domain attention and spatial-domain attention, effectively mitigating inherent information loss in single-strategy fusion approaches. The implemented MAA-SAE model achieves a comprehensive recognition accuracy of 98.4%. This research provides critical technical references for intelligent vehicle interior sound annoyance evaluation.
Structural coupling vibrations induced by large-mass underframe equipment in high-speed trains have become increasingly prominent, particularly due to the unresolved first-order vertical bending characteristics. The use of conventional rubber isolators often leads to in-phase and anti-phase vibration modes near 10 Hz. To address this issue, a nonlinear broadband damping strategy based on particle dampers is proposed. The damping ratio of the particle damper is first determined using the Discrete Element Method (DEM), and subsequently integrated into a coupled dynamic model of the car body and underframe equipment for model correction. A finite element (FE) model is then developed and validated through scaled experimental testing to systematically evaluate the influence of particle dampers on low-frequency vertical bending behavior. Results demonstrate that iron-based alloy particle dampers (90% filling ratio, 3 mm diameter) significantly broaden the resonance bandwidth, reduce dual-peak amplitudes by 33.51% and 26.02%, and markedly enhance the overall stability of the system.
The variation of the open-cell polyurethane foam thickness within the seating system can affect the dynamic response of the occupant-seat system and the riding discomfort. This study was aimed to develop and optimize a finite element model of the occupant-seat system incorporating the skeleton and muscle tissue for predicting the seat transmissibility and assessing the riding discomfort with different foam thicknesses. The key model parameters influencing the prediction of the seat transmissibility were investigated with the sensitivity analysis, and the correlation between the parameters and the seat transmissibilities was quantified by fitting the polynomial functions. The genetic algorithm was also utilized to iterate the squared error between the predicted and the measured seat transmissibilities, thereby obtaining the optimal parameter values. The best fit of the vertical inline and fore-and-aft cross-axis seat transmissibility predicted by the optimized model increased by 13.34 % and 14.41 %, respectively. When the dynamic stiffness of the foam decreased with the increase of the thickness, the peak frequency of seat transmissibilities, the weighted root-mean-square acceleration and the SEAT value exhibited the same downward trend.
For the current rolling bearing rolling degradation trend prediction problem, its general use of a single timefrequency domain features as the degradation characteristics, the effect is often not satisfactory. To improve the prediction accuracy, a rolling bearing degradation trend prediction method based on multi-scale Boltzmann-Shannon interaction entropy (abbreviated as MBSIE) and Transformer-TCN is proposed. The original rolling bearing signals are decomposed and noise-reduced by EEMD, the filtered IMF components are used for signal reconstruction, and the degradation indicators are constructed by fusing the filtered time-frequency domain features with MBSIE, the Transformer-TCN neural network is introduced for prediction. The data from PHM2012 is used for instance analysis. The results show that the prediction accuracy of the proposed method reaches 94%, which is better than the traditional entropy such as arrangement entropy and sample entropy as the degradation features, proving the feasibility and relative superiority of this paper in predicting the bearing degradation trend.
This study investigates the application of acoustic emission tomography (AET) using the algebraic reconstruction technique (ART) algorithm for damage detection and visualization in homogeneous and multilayered materials. The research employed a systematic four-stage experimental approach to evaluate how sensor configuration, acoustic signal (ray) coverage, and source signal frequency affect reconstruction accuracy. The first stage examined signal propagation along the surface of a homogeneous aluminum specimen. The second stage analyzed signals propagating through the material volume to provide comprehensive assessment of internal damage. The third stage extended this acoustic emission (AE) tomographic approach to multi-layered structures, revealing additional challenges including impedance mismatches and wave distortions that increased reconstruction errors. The final stage addressed those limitations by implementing higher-frequency source signals, which reduced reconstruction errors and improved damage detection precision due to shorter wavelengths providing finer resolution and greater sensitivity to the defects. The findings demonstrate the effectiveness of AET in identifying damage across diverse material and sensor configurations, ray coverage, and highlight its adaptability as a non-destructive evaluation (NDE) technique. This research provides elaborate and valuable insights into optimizing AET methodologies for structural health monitoring (SHM) and enhancing its applicability to complex material systems using ART algorithm.
Accurate impact localization remains challenging for structural health monitoring (SHM) systems due to wave propagation complexities and high measurement uncertainties. This study presents a novel baseline-free framework that combines simulated annealing (SA) and genetic algorithm (GA) to characterize low-velocity impacts in plate structures. The proposed framework provides robust impact localization without relying on baseline signals, material properties, or detailed geometric parameters. Experimental impact tests were conducted using a coarse network of piezoelectric sensors arranged in multiple configurations to evaluate the performance of the proposed framework for impact characterization, full-field group velocity mapping, and computational efficiency. The framework predicted impact locations with an average relative error of less than 5% and R-values of about 0.98 under the optimal sensor configuration. The analysis of sensor placement indicated asymmetric sensor layouts yield 50% higher performance compared to symmetric layouts. The combined SA-GA framework successfully identified various impacts both inside and outside the regions confined by active sensor network with probabilities exceeding 90%. Additionally, the results demonstrated that the proposed framework consistently outperformed standalone SA and GA algorithms in both impact identification accuracy and the consistency of the velocity field.
In this study, the ungrounded and grounded virtual-inerter-based dynamic vibration absorbers (VIDVAs) are proposed by using an inertial actuator with relative or absolute acceleration feedback. The relative acceleration feedback signal is created by using two accelerometers (on the proof-mass and the base of the inertial actuator), whereas the absolute acceleration feedback is generated by an accelerometer on the proof-mass alone. The natural frequencies of the proposed VIDVAs can be tuned by using a virtual inerter without changing its physical design. The tunable VIDVAs in this study aim to reduce resonant vibrations as well as vibrations at a single forcing frequency. The tuning capabilities and control performances of the proposed VIDVAs are investigated numerically and experimentally. The results show that both VIDVAs can reduce resonant vibration significantly, while the grounded VIDVA exhibits broader mode splitting. The experimental results also show that a high level of vibration reduction was achieved by using grounded VIDVA in a wide range of forcing frequencies. Compared to ungrounded VIDVA, the study indicates that the grounded VIDVA is easier to implement and can achieve better control performance.
This study develops a weighted cost function-based optimization framework for tuning the time delay parameter in a nonlinear vehicle suspension with time-delayed acceleration feedback control. A quarter-vehicle model is first established to characterize suspension dynamics. The steady-state response is derived analytically using the harmonic balance method, while a stability analysis of the linearized system delineates the feasible ranges of time delay and feedback gain coefficient. A composite cost function is then formulated by integrating weighted contributions from the sprung mass acceleration, suspension dynamic deflection, and tire dynamic load. Optimization of the time delay parameter is carried out for both positive and negative feedback control configurations. A comparative performance evaluation between constant and frequency-dependent time delay strategies is conducted across the human-sensitive frequency band (4-8 Hz). Numerical simulations validate the theoretical predictions, demonstrating average reductions of 45.97% in the cost function, 35.54% in the sprung mass acceleration, and 30.65% in the tire dynamic load. Although a slight increase in the suspension dynamic deflection is observed, the results confirm that optimized time delay can simultaneously improve ride comfort and driving safety.
This paper proposes a computational model for analyzing an acoustic scattering from an underwater elastic shell structure constrained by acoustic boundaries. The method derives the underwater acoustic field expression using the wave superposition method (WSM). It incorporates the image source principle to establish equivalent source configurations for acoustic boundary modeling, and couples the shell's vibrational response with the acoustic field through acoustic-structure interaction conditions on the shell surface. The dynamic response of the shell under acoustic excitation is obtained via the precise transfer matrix method. The proposed approach is not constrained by the geometry of the object or the frequency band of interest, while effectively avoiding the singularity issues inherent in conventional WSM. The method's accuracy has been verified through comparisons with numerical solutions. Furthermore, this work presents an experimental study of underwater target scattering under laboratory conditions, introducing a systematic experimental methodology and data processing protocol specifically designed for theoretical validation. Additionally, a detailed analysis of acoustic field patterns at varying immersion depths provides further insights into the effects of acoustic boundaries.
Variational Mode Decomposition (VMD) requires the manual setting of decomposition algorithm parameters, leading to low fault identification accuracy. To address this issue, this paper proposes a fault diagnosis method based on the Whale Optimisation Algorithm (WOA)-optimized VMD, combined with Multi-scale Permutation Entropy (MPE) and Support Vector Machine (SVM). The specific steps are as follows: Firstly, the Whale Optimisation Algorithm is introduced to optimize the number of modal decompositions (K) and the quadratic penalty factor (alpha) of Variational Mode Decomposition, obtaining the optimal VMD parameters. After decomposing the vibration signal, the multi-scale permutation entropy of each Intrinsic Mode Function (IMF) component is calculated separately. Finally, the extracted feature data are input into the SVM classifier, and the Whale Optimisation Algorithm is used to optimize the parameters of SVM to obtain the WOA-SVM optimization model. Meanwhile, ten-fold cross-validation is introduced to train the WOA-SVM, ultimately achieving accurate classification and diagnosis of fault signals. Analyses are conducted using datasets from the Bearing Data Centers of Jiangnan University and the University of Western Ontario (Canada) respectively. The results show that the fault identification accuracy of this method reaches 98.36% and 99.13%, respectively, and its performance is superior to that of methods such as VMD-PE, EMD-MPE, VMD-SVM, and VMD-MPE.
The rapid growth of electric-vehicle (EV) technology makes in-cabin vibration and noise control critical to ride comfort. The high operating frequency of traction motors, combined with the absence of a conventional engine block, intensifies mid-frequency (approximate to 500-2000 Hz) vibro-acoustic coupling. Lightweight body structures further complicate control and call for more accurate hybrid finite-element/statistical-energy-analysis (FE-SEA) models. This work integrates an edge-based smoothed finite-element technique (ES-FEM) into a hybrid FE-SEA framework (ES-FE-SEA) to improve boundary continuity and smoothing-domain construction, thereby increasing the accuracy and stability of mid-frequency acoustic-vibration predictions. A reduced-scale cabin model is built in VA One to conduct parametric studies on mesh density, element aspect ratio, mesh type, and damping-loss factor; predictions are assessed with Monte-Carlo analyses. Results show that mesh density strongly affects vibration response, smaller aspect ratios shift peak energy toward higher frequencies, and damping substantially changes both energy levels and peak velocity. The ES-FE-SEA model consistently improves mid-frequency predictions over a conventional FE-SEA formulation and is suitable for acoustic-package optimization and EV-cabin NVH management. Future work will validate model parameters experimentally and extend the framework to multi-physics coupling for broader engineering applicability.
