Search Results (263)

To address the pronounced issue of noise and vibration within the combine harvester cab, this study proposes a hybrid simulation and experimental validation approach that integrates the pivot noise transfer function (NTF) with a finite element method (FEM)-based vibroacoustic coupling analysis. A coupled finite element model combining the cab structure and its internal acoustic cavity was developed, with the excitation path characteristics explicitly defined. The coupled interaction between structural and acoustic modes, along with its influence on noise transmission, was systematically examined. The analysis revealed a significant transmission peak near 18 Hz at critical pivot Point D under specific excitation directions, indicating strong directional sensitivity in the excitation–response relationship. Experimental validation showed that the discrepancy between simulated and measured responses, including the NTFs, remained within 15%, confirming the accuracy and applicability of the proposed method. This research offers a reliable analytical framework and practical reference for noise and vibration reduction in agricultural machinery cab design. Full article

Detecting structural defects is one of the primary challenges engineers face. Consequently, the development of techniques and methods capable of detecting structural defects has always been critical. It should be emphasized that crack detection is only meaningful if it occurs before the final stages of structural failure. Accordingly, the early identification of structural defects has become a significant research challenge, motivating the development of techniques and diagnostic parameters that can effectively capture and reflect the structure’s nonlinearity or non-uniform behavior. This study aims to provide a more detailed examination of modulation phenomena observed in the measured response using the vibro-acoustic modulation (VAM) method, and propose a new model that simultaneously incorporates all three conventional modulation types (amplitude, frequency, and phase), which may offer a more accurate representation of the response signal behavior. Both theoretical and experimental results clearly confirm that the phase shifts of individual frequency components in the frequency domain vary throughout the lifetime of the tested specimen. This behavior, as anticipated by the proposed model, reveals a strong correlation between phase shifts and modulation indices (MIs). Furthermore, the relative sensitivity analysis indicates that the phase shift is more sensitive than the modulation index (MI), suggesting its strong potential as an indicator for early defect detection in structural components. Full article

This study presents a rigorous experimental investigation of in-cabin acoustic comfort across a heterogeneous set of road and special-purpose vehicles. Interior noise measurements were conducted on a total of 35 vehicles, comprising five vehicles from each of seven operational categories, grouped according to RNTR-2 regulations into three distinct vehicle classes: N1, N2, and N2G. The adopted research methodology ensures a unified, phenomenological, and experimental approach to the assessment of interior vehicle acoustics, enabling consistent data acquisition and comparative analysis across vehicle classes. Measurements were performed under both stationary and dynamic operating conditions using Class 1 precision instrumentation. The experimental results reveal systematic differences in acoustic performance between vehicle classes. While N1 and N2 vehicles generally comply with recommended comfort thresholds, N2G special-purpose vehicles exhibit significantly elevated interior noise levels, reaching up to 90 dBA during dynamic operation, together with increased variability at higher engine regimes. These findings highlight the influence of vehicle architecture, operational conditions, and mission-oriented design constraints on vibro-acoustic behavior. Passive noise control solutions based on advanced sound-absorbing and sound-insulating materials were further evaluated, demonstrating interior noise reductions of up to 10 dBA. The scientific contribution of this work lies in the establishment of a unified, reproducible methodology that enables direct cross-category comparison of in-cabin acoustic comfort while explicitly integrating special-purpose vehicles into a comfort-oriented analytical paradigm. By moving beyond regulatory compliance toward a multidimensional interpretation of acoustic comfort, the study provides a robust foundation for vehicle design optimization and supports the future development of dedicated comfort assessment standards. Full article

This paper presents a methodology for monitoring the structural condition of remotely controlled demolition robots to prevent failures and extend service life. The approach integrates finite element method (FEM) simulations with strain gauge and vibroacoustic measurements. Iterative calibration of numerical models enabled accurate mapping of stress distribution, while optimal sensor placement improved monitoring precision. The study examined the impact of operational loads on durability and vibration resistance of critical components. The developed system enhances safety, operational efficiency, and structural reliability, providing a practical framework for predictive maintenance of demolition robots. Full article

Accurate characterization of rail corrugation is essential for the operation and maintenance of urban rail transit. To enhance the representation capability for rail corrugation, this study proposes a sound–vibration feature fusion method based on Laplacian manifold learning. The method constructs a multidimensional feature space using real-world acoustic and vibration signals measured from metro vehicles, introduces a Laplacian manifold structure to capture local geometric relationships among samples, and incorporates inter-class separability into traditional intra-class compactness metrics. Based on this, a comprehensive feature evaluation index L_r is developed to achieve adaptive feature ranking. The final fusion indicator, LWVAF, is generated through weighted feature integration and used for rail corrugation characterization. Validation on in-service metro line data demonstrates that, after rail grinding, LWVAF exhibits a more pronounced reduction and higher sensitivity to changes compared with individual acoustic or vibration features, reliably reflecting improvements in rail corrugation. The results confirm that the proposed method maintains strong robustness and physical interpretability even under small-sample and weak-label conditions, offering a new approach for sound–vibration fusion analysis and corrugation evolution studies. Full article

Otitis media with effusion (OME) is a common middle ear disease characterized by fluid accumulation without acute infection, leading to conductive hearing loss. Conventional diagnostic tools, such as tympanometry and otoscopy, have limited sensitivity and rely on expert interpretation. This study investigates vibro-acoustic radiation (VAR) as a novel, non-invasive, and objective method for OME detection. VAR signals were obtained from 36 OME patients (43 ears) and 15 normal ears using bone-conduction excitation and stereo microphones, and the frequency response functions were analyzed. OME increases the mechanical loading of the tympanic membrane and ossicular chain, thereby modifying sound transmission across the middle ear. Using a simplified theoretical model, we estimated acoustic parameters of the ear canal, eardrum, and middle ear, including specific acoustic impedance and resonance frequency ranges, to interpret changes in VAR. VAR analysis revealed significantly reduced signal amplitude in the 8–10 kHz range in OME ears compared with normal ears (p < 0.05). A classification algorithm based on these features achieved 86.7% accuracy, 85.0% sensitivity, and 80.0% specificity, with an area under the ROC curve of 0.986. These findings suggest that VAR has strong potential as a non-invasive diagnostic tool for OME, warranting validation in larger clinical studies. Full article

Quiet drivetrains have become a central requirement in modern electric vehicles, where the absence of engine masking makes even subtle gear tones clearly audible. As a result, manufacturers are looking for more reliable ways to understand how design choices, manufacturing variability, and operating conditions shape gear noise and vibration. Digital Twin (DT) approaches—linking high-fidelity models with measured data throughout the product lifecycle—offer a potential route to achieve this, but their use in gear NVH is still emerging. This review examines recent work from the past decade on DT concepts applied to gears and drivetrain NVH, drawing together advances in simulation, metrology, sensing, and data exchange standards. The survey shows that several building blocks of an NVH-oriented twin already exist, yet they are rarely combined into an end-to-end workflow. Clear gaps remain. Current models still struggle with high-frequency behavior. Real-time operation is also limited. Manufacturing and test data are often disconnected from simulations. Validation practices lack consistent NVH metrics. Hybrid and surrogate modeling methods are used only to a limited extent. The sustainability benefits of reducing prototypes are rarely quantified. These gaps define the research directions needed to make DTs a practical tool for future gear NVH development. A research Gap Map is presented, categorizing these gaps and their impact. For each gap, we propose actionable future directions—from multiscale “hybrid twins” that merge test data with simulations, to benchmark datasets and standards for DT NVH validation. Closing these gaps will enable more reliable gear DTs that reduce development costs, improve acoustic quality, and support sustainable, data-driven NVH optimization. Full article

This paper presents a comparative analysis of the influence of Fused Deposition Modeling (FDM) parameters—specifically material type, infill geometry, and density—on the vibro-acoustic characteristics of loudspeaker enclosures. The enclosures were designed as exponential horns to intensify resonance phenomena for precise evaluation. Twelve unique configurations were fabricated using three materials with distinct damping properties (PLA, ABS, wood-composite) and three internal geometries (linear, honeycomb, Gyroid). Key vibro-acoustic properties were assessed via digital signal processing of recorded audio signals, including relative frequency response and time-frequency (spectrogram) analysis, and correlated with a predictive Finite Element Analysis (FEA) model of mechanical vibrations. The study unequivocally demonstrates that a material with a high internal damping coefficient is a critical factor. The wood-composite enabled a reduction in the main resonance amplitude by approximately 4 dB compared to PLA with the same geometry, corresponding to a predicted 86% reduction in mechanical vibration. Furthermore, the results show that a synergy between a high-damping material and an advanced, energy-dissipating infill (Gyroid) is crucial for achieving high acoustic fidelity. The wood-composite with 10% Gyroid infill was identified as the optimal design, offering the most effective resonance damping and the most neutral tonal characteristic. This work provides a valuable contribution to the field by establishing a clear link between FDM parameters and acoustic outcomes, delivering practical guidelines for performance optimization in personalized audio systems. Full article

In recent years, switched reluctance motors have emerged as a promising option for various applications due to their low manufacturing cost, rare-earth-free construction, and mechanical robustness. However, their widespread adoption is often limited by high torque ripple and acoustic noise. To address these challenges, this paper presents a comparative study of the acoustic noise performance of an 18/12 switched reluctance motor under various current control techniques. This comparison offers valuable insight into the motor’s vibroacoustic characteristics, which is essential for optimizing SRM performance, particularly in applications where noise reduction is critical. Dynamic simulations of an SRM are carried out in MATLAB/Simulink, and multi-physics analyses are performed in ANSYS Workbench. The multi-physics modeling includes electromagnetic, modal, and harmonic response analyses for four current control techniques evaluated across different operating speeds under light-load conditions. The simulation results are validated experimentally using an actual motor mounted on a dynamometer setup. The corresponding acoustic signatures for each control technique are presented as 2D plots of equivalent radiated power from simulations and sound power level from experimental tests. In addition, experimental waterfall diagrams are provided for each control technique. Full article

Acoustic cavities play a role in many technological applications in civil, naval, and aerospace engineering. This study examines the vibroacoustic performance of a forced oscillating top membrane of a cylindrical container fully filled with a compressible and nonviscous fluid. For the case of harmonic motion and using Helmholtz’s equation, the velocity potential is deduced, and the acoustic pressure is obtained using Bernoulli’s linearized equation. Taking into account the dynamic equation for the membrane with the interacting fluid with the different terms expanded in a modal series and after an integration procedure over the membrane surface, a simple analytical quadratic equation is deduced, and the coupled natural frequencies of the membrane are obtained. For the case of forced vibrations, a transfer function is obtained for calculating the frequency spectrum response of the fluid–membrane interacting system. In particular, the membrane deformation spectrum and the acoustic cavity pressure spectrum are obtained for different location points. Moreover, the spectrum of the mean quadratic values of the membrane deflexion and acoustic pressure are deduced, along with its variation with different parameters such as drum height, membrane radius, fluid density, load position, sound speed, and membrane tension. The variation in sensitivity with frequency and other different parameters is also analysed. The results are contrasted with those obtained by other authors to validate the present work. Full article

This work develops and validates a high-order, three-dimensional Carrera Unified Formulation (CUF) framework for coupled structural–acoustic eigenanalysis, aiming at accurate low-frequency modal characterization of interior cavity-structure systems with significantly reduced degrees of freedom. The proposed approach employs high-order polynomial expansions to discretize both the structural and fluid domains. The methodology integrates fully coupled fluid-structure analyses into a unified variational formulation, enabling the systematic assembly of global stiffness and mass matrices via sophisticated numerical integration techniques. Validation against a Comsol Multiphysics benchmark model confirms that the CUF-based high-order frameworks converge with significantly fewer degrees of freedom and reliably capture the intricate interactions at the fluid–structure interface. In addition, the approach is versatile, accommodating a range of boundary conditions and material models, underscoring its broad applicability in modern engineering design. Overall, this work advances the state of the art in vibroacoustic analysis by offering a robust tool for predicting natural frequencies and mode shapes, and it lays the groundwork for future extensions to nonlinear, transient, and data-driven applications. Full article

Diesel engines remain the foundation for obtaining mechanical energy in sectors where autonomy and reliability are required; however, predictive diagnostics under real-world conditions remain challenging. The purpose of this scoping review is the investigation and systematization of published scientific data on the application of vibration methods for monitoring the technical condition of diesel engines in industrial or controlled laboratory conditions. Based on numerous results of publication analysis, sensor configurations, diagnosed components, signal analysis methods, and their application for assessing engine technical condition are considered. As methods for determining vibration parameters, time-domain and frequency-domain analysis, adaptive decompositions, and machine and deep learning algorithms predominate; high accuracy is more often achieved under controlled conditions, while confirmations of robustness on industrial installations are still insufficient. Key limitations for the application of vibration monitoring methods include the multicomponent and non-stationary nature of signals, a high level of noise, requirements for sensor placement, communication channel limitations, and the need for on-site processing; meanwhile, the assessment of torsional vibrations remains technically challenging. It is concluded that field validations of vibroacoustic data, the use of multimodal sensor platforms, noise-immune algorithms, and model adaptation to the specific environment are necessary, taking into account fuel quality, transient conditions, and climatic factors. Full article

This study proposes a hybrid analytical framework for detecting chondromalacia using vibroacoustic (VAG) signals from patients with knee osteoarthritis (OA) and healthy controls (HCs). The methodology combines nonlinear signal decomposition, feature extraction, and deep learning classification. Raw VAG signals, recorded with a custom multi-sensor system during open (OKC) and closed (CKC) kinetic chain knee flexion–extension, underwent preprocessing (denoising, segmentation, normalization). Ensemble Empirical Mode Decomposition (EEMD) was used to isolate Intrinsic Mode Functions (IMFs), and Detrended Fluctuation Analysis (DFA) computed local (α₁) and global (α₂) scaling exponents as well as breakpoint location. Frequency–energy features of IMFs were statistically assessed and selected via Neighborhood Component Analysis (NCA) for support vector machine (SVM) classification. Additionally, reconstructed α₁/α₂-based signals and raw signals were converted into continuous wavelet transform (CWT) scalograms, classified with convolutional neural networks (CNNs) at two resolutions. The SVM approach achieved the best performance in CKC conditions (accuracy 0.87, AUC 0.91). CNN classification on CWT scalograms also demonstrated robust OA/HC discrimination with acceptable computational times at higher resolutions. Results suggest that combining multiscale decomposition, nonlinear fluctuation analysis, and deep learning enables accurate, non-invasive detection of cartilage degeneration, with potential for early knee pathology diagnosis. Full article

To address the engineering challenges of powertrain excitation noise and aggravated low-frequency interior noise caused by armored structures in special-purpose vehicles, this study proposes an in-vehicle acoustic response analysis method based on vibro-acoustic coupling theory. This study presents a method for analyzing in-vehicle acoustic response under engine excitation, integrating Panel Acoustic Contribution Analysis (PACA) with a vibro-acoustic coupling model tailored for armored vehicles. The framework experimentally reveals a condition-independent resonance at 26.5 Hz and reproduces engine-order peaks at 40 Hz, 93.3 Hz, and 140 Hz. Quantitative comparison shows ΔSPL ≤ 2.5 dB and RMSE ≤ 2.2 dB between simulation and experiment, confirming model robustness. Based on these results, conceptual Dynamic Vibration Absorber (DVA) placement guidelines are proposed for dominant panels, providing practical engineering insights for NVH mitigation in armored vehicles. Full article

This study presents a comparative performance analysis of three sensor technologies—microphone, magnetic pickup, and laser Doppler vibrometer—for capturing string vibration under varied excitation conditions: striking, plectrum plucking, and wire plucking. Two different magnetic pickups are included in the comparison. Measurements were taken at multiple excitation levels on a simplified electric guitar mounted on a stable platform with repeatable excitation mechanisms. The analysis focuses on each sensor’s capacity to resolve fine-scale waveform features during the initial attack while also taking into account its capability to measure general changes in instrument dynamics and timbre. We evaluate their ability to distinguish vibro-acoustic phenomena resulting from changes in excitation method and strength as well as measurement location. Our findings highlight the significant influence of sensor choice on observable string vibration. While the microphone captures the overall radiated sound, it lacks the required spatial selectivity and offers poor SNR performance 34 dB lower then other methods. Magnetic pickups enable precise string-specific measurements, offering a compelling balance of accuracy and cost-effectiveness. Results show that their low-pass frequency characteristic limits temporal fidelity and must be accounted for when analysing general sound timbre. Laser Doppler vibrometers provide superior micro-temporal fidelity, which can have critical implications for physical modeling, instrument design, and advanced audio signal processing, but have severe practical limitations. Critically, we demonstrate that the required optical target, even when weighing as little as 0.1% of the string’s mass, alters the string’s vibratory characteristics by influencing RMS energy and spectral content. Full article