Search Results (483)

In the evaluation of vehicle noise, vibration and harshness (NVH) performance, interior noise control is the core consideration. In the early stage of automobile research and development, accurate prediction of interior noise caused by road surface is very important for optimizing NVH performance and shortening the development cycle. Although the data-driven machine learning method has been widely used in automobile noise research due to its advantages of no need for accurate physical modeling, data learning and generalization ability, it still faces the challenge of insufficient accuracy in capturing key local features, such as peaks, in practical NVH engineering. Aiming at this challenge, this paper introduces a forecast approach that utilizes an empirical-informed neural network, which aims to integrate a physical mechanism and a data-driven method. By deeply analyzing the transmission path of interior noise, this method embeds the acoustic mechanism features such as local peak and noise correlation into the deep neural network as physical constraints; therefore, this approach significantly enhances the model’s predictive performance. Experimental findings indicate that, in contrast to conventional deep learning techniques, this method is able to develop better generalization capabilities with limited samples, while still maintaining prediction accuracy. In the verification of specific models, this method shows obvious advantages in prediction accuracy and computational efficiency, which verifies its application value in practical engineering. The main contributions of this study are the proposal of an empirical-informed neural network that embeds vibro-acoustic mechanisms into the loss function and the introduction of an adaptive weight strategy to enhance model robustness. Full article

This paper evaluates a novel series-type suspension mount designed for electric vehicles (EVs), in which the spring and damper are arranged in series rather than in a conventional parallel configuration. This structurally simple yet innovative design avoids the need for additional mechanical components, such as inerters or costly active devices, while effectively mitigating vibration. Comparative quarter-car simulations demonstrated that the series-type configuration provided a faster reduction in transmissibility across the analyzed frequency range, highlighting its superior isolation capability compared to conventional mounts. An extended series-type model was also investigated by incorporating auxiliary sub-mount elements to assess the parametric effects. The results showed that damping variations had a limited influence, whereas the sub-mount stiffness played a decisive role in shaping the transmissibility curves and generating the secondary resonance behavior. To validate the concept experimentally, a prototype consisting of four coil springs and a vibration isolation pad was prepared and tested using impact-hammer excitation. The measured transmissibility confirmed improved vibration isolation up to 100 Hz under the given specimen conditions, with resonance features attributable to the inherent stiffness of the isolation pad. Overall, the findings verified that a simple series-type mount can provide efficient and practical vibration isolation tailored to EV applications. Full article

Induction motors (IMs) are crucial in many industrial applications, offering a cost-effective and reliable source of power transmission and generation. However, their continuous operation imposes considerable stress on electrical and mechanical parts, leading to progressive wear that can cause unexpected system shutdowns. Bearings, which enable shaft motion and reduce friction under varying loads, are the most failure-prone components, with bearing ball defects representing most severe mechanical failures. Early and accurate fault diagnosis is therefore essential to prevent damage and ensure operational continuity. Recent advances in the Internet of Things (IoT) and machine learning (ML) have enabled timely and effective predictive maintenance strategies. Among various diagnostic parameters, vibration analysis has proven particularly effective for detecting bearing faults. This study proposes a hybrid diagnostic framework for induction motor bearings, combining vibration signal analysis with Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs) in an IoT-enabled Industry 4.0 architecture. Statistical and frequency-domain features were extracted, reduced using Principal Component Analysis (PCA), and classified with SVMs and ANNs, achieving over 95% accuracy. The novelty of this work lies in the hybrid integration of interpretable and non-linear ML models within an IoT-based edge–cloud framework. Its main contribution is a scalable and accurate real-time predictive maintenance solution, ensuring high diagnostic reliability and seamless integration in Industry 4.0 environments. Full article

Underground high-voltage transmission cables, especially high-pressure fluid-filled (HPFF) pipe-type cable systems, are critical components of urban power networks. These systems consist of insulated conductor cables housed within steel pipes filled with pressurized fluids that provide essential insulation and cooling. Despite their reliability, HPFF cables experience faults caused by insulation degradation, thermal expansion, and environmental stressors, which, due to their subtle and gradual nature, complicate incipient fault detection and subsequent fault localization. This study presents a novel, proactive, and retrofit-friendly predictive condition monitoring method. It leverages distributed accelerometer sensors non-intrusively mounted on the HPFF steel pipe within existing manholes to continuously monitor vibration signals in real time. A physics-enhanced convolutional neural network–long short-term memory (CNN–LSTM) deep learning architecture analyzes these signals to detect incipient faults before they evolve into critical failures. The CNN–LSTM model captures temporal dependencies in acoustic data streams, applying time-series analysis techniques tailored for the predictive condition monitoring of HPFF cables. Experimental validation uses vibration data from a scaled-down HPFF laboratory test setup, comparing normal operation to incipient fault events. The model reliably identifies subtle changes in sequential acoustic patterns indicative of incipient faults. Laboratory experimental results demonstrate a high accuracy of the physics-enhanced CNN–LSTM architecture for incipient fault detection with effective data feature extraction. This approach aims to support enhanced operational resilience and faster response times without intrusive infrastructure modifications, facilitating early intervention to mitigate service disruptions. Full article

Electric vehicle (EV) drivetrains operate at high rotational speeds, which makes the noise, vibration, and harshness (NVH) performance of gear transmissions a critical design factor. Without the masking effect of an internal combustion engine, gear whine can become a prominent source of passenger discomfort. This paper provides the first comprehensive review focused specifically on gear tooth surface waviness, a subtle manufacturing-induced deviation that can excite tonal noise. Periodic, micron-scale undulations caused by finishing processes such as grinding may generate non-meshing frequency “ghost orders,” leading to tonal complaints even in high-quality gears. The article compares finishing technologies including honing and superfinishing, showing their influence on waviness and acoustic behavior. It also summarizes modern waviness detection techniques, from single-flank rolling tests to optical scanning systems, and highlights data-driven predictive approaches using machine learning. Industrial case studies illustrate the practical challenges of managing waviness, while recent proposals such as controlled surface texturing are also discussed. The review identifies gaps in current research: (i) the lack of standardized waviness metrics for consistent comparison across studies; (ii) the limited validation of digital twin approaches against measured data; and (iii) the insufficient integration of machine learning with physics-based models. Addressing these gaps will be essential for linking surface finish specifications with NVH performance, reducing development costs, and improving passenger comfort in EV transmissions. Full article

Thick wall structures involving longitudinal wave are typically utilized in aerospace engineering, nuclear power engineering, precision transmission device design, and pressure vessels design. Consequently, developing sophisticated dynamic models for thick plates is of paramount importance. However, the commonly used longitudinal vibration equation is of the second order, which is regarded as a plane stress problem. Its dispersion curve is a straight line, which cannot describe the actual dispersion in the plate. In this paper, the spectral analysis of Navier equation describing three-dimensional elasto-dynamics is carried out by using the symmetric and asymmetric spectral decomposition theory of differential operators and introducing the concept of virtual differential operators. The infinite product operator series describing longitudinal vibration are truncated into fourth order. The governing equation of longitudinal vibration consists of a fourth-order wave equation and a second-order wave equation. Owing to the fact that no a priori assumptions were introduced during the derivation of its dynamic equations, the proposed plate dynamic model boasts higher precision and is applicable across a broader frequency spectrum and for plates with greater thicknesses. This is a breakthrough in the longitudinal vibration equation of plates. Full article

This study presents a multivariate regression-based analysis aimed at quantifying the influence of key vibration-generating components in two types of grain combines—C110H (with straw walker) and CASE IH (axial flow)—on the operator’s seat (OS). Using triaxial accelerometers, vibrational measurements were performed under both stationary and operational working mode. RMS acceleration values were recorded for major subsystems (engine, threshing unit, chassis, chopper/header) and processed via multiple linear regression. The models generated for each combine and axis (Ox, Oy, Oz) revealed high coefficients of determination (R² > 0.85), confirming the linear model’s validity. Influence maps and standardized coefficients were used to rank the sources of vibration. Results indicate that the straw walker dominates vibration transmission in the C110H, while the header and threshing system are more significant in the CASE IH. The findings support the development of predictive algorithms for real-time vibration monitoring and ergonomic improvements in combine design. Moreover, the proposed methodology provides a cost-effective diagnostic tool for early fault detection, targeted maintenance, and the long-term reduction of operator fatigue and injury risks. Full article

Ultra-high-voltage (UHV) transmission lines are prone to galloping and oscillations under ice and wind loads, posing risks to system reliability and safety. Accurate aerodynamic coefficients are essential for evaluating these effects, but conventional wind tunnel and CFD methods are costly and inefficient for practical applications. To address these challenges, this study develops a surrogate model for rapid and accurate prediction of aerodynamic coefficients for six-bundle conductors. Initially, a CFD model to calculate the aerodynamic coefficients of six-bundle conductors was proposed and validated against wind tunnel experimental results. Subsequently, Latin hypercube sampling (LHS) was employed to generate datasets covering wind speed, icing shape, icing thickness, and wind attack angle. High-throughput numerical simulations established a comprehensive aerodynamic database used to train and validate multiple tree-based surrogate models, including decision tree (DT), random forest (RF), extremely randomized trees (ERTs), gradient boosted decision tree (GBDT), and extreme gradient boosting (XGBoost). Comparative analysis revealed that the XGBoost-based model achieved the highest prediction accuracy, with an R² of 0.855 and superior generalization performance. Feature importance analysis further highlighted wind speed and icing shape as the dominant influencing factors. The results confirmed the XGBoost surrogate as the most effective among the tested models, providing a fast and reliable tool for aerodynamic prediction, vibration risk assessment, and structural optimization in UHV transmission systems. Full article

Functionally graded porous beam (FGPB) structures are widely used in engineering due to their light weight, high strength, and vibration-damping performance. However, their energy localization and vibration suppression characteristics remain largely unexplored. To address this gap, this study proposes an axially functionally graded porous beam (AFGPB) structure capable of achieving energy localization and suppressing vibration transmission. A semi-analytical model is first developed within the Rayleigh–Ritz framework, using Gaussian functions as basis functions to accurately represent the displacement field. The accuracy of the model is validated by comparing its vibration characteristics with those obtained using the finite element method (FEM). Subsequently, the vibration behavior of double-AFGPB with simply supported boundary constraints is investigated. A series of numerical results are presented in this study to analyze the influence of porosity parameters on the energy localization effect and vibration suppression performance. Results reveal that the porosity power-law index N and truncation coefficient δ play key roles in energy localization and vibration suppression performance. When N ≥ 4, the energy localization effect and the vibration attenuation of the double-AFGPB become more pronounced with increasing N and decreasing δ, particularly in the low-frequency range. Full article

This study presents a comprehensive dynamic analysis of mechanical systems incorporating elastic joints and introduces an adaptive vibration actuator with an integrated transmission variator. The system’s behavior is modeled through kinematic and dynamic formulations, utilizing both analytical and numerical methods. The analysis reveals that the inclusion of elastic elements enables a force adaptation effect, allowing the output element to adjust dynamically to variations in external loading. Under conditions of constant input power, the output speed varies inversely with the load, ensuring reliable adaptive performance. Furthermore, the elastic joints facilitate internal force redistribution, enhancing energy efficiency and reducing mechanical losses. These findings hold relevance for applications in industrial automation and robotics, where consistent functionality under variable load conditions is essential. Full article

Lubrication plays a crucial role in reducing gear surface damage and defects such as pitting, wear, and scuffing; therefore, analyzing the influence of lubrication is essential for preventing such failures in gear transmission systems. To this end, the dynamic properties of gear systems were examined, leading to the creation of a thermal elastohydrodynamic lubrication (TEHL) model for the line contact of involute spur gears. This model utilizes a multigrid method to calculate the oil film pressure and thickness. Subsequently, models for meshing stiffness, normal oil film stiffness, and overall normal stiffness were developed using energy methods and lubrication theory. Ultimately, a dynamic model of the spur gear system that incorporated lubrication effects was developed to examine how different operating conditions affect dynamic transmission error, vibration velocity, and dynamic meshing force. The findings revealed that when considering the TEHL effect, the dynamic transmission error along the gear meshing line increases, while both the vibration velocity and dynamic meshing force exhibit a decrease. Furthermore, as speed and load intensify, the amplitudes of dynamic transmission error, vibration velocity, and dynamic meshing force also rise. Notably, an increase in the initial viscosity of the lubricating oil correlates with a decrease in the fluctuation of dynamic transmission error, while the variations in vibration velocity and dynamic meshing force remain relatively insignificant. Full article

Ultrasonic-assisted machining (UAM) has emerged as a transformative technology for increasing material removal efficiency, improving surface quality and extending tool life in precision manufacturing. This review specifically focuses on the application of it to titanium aluminide (TiAl) alloys. These alloys are widely used in aerospace and automotive sectors due to their low density, high strength and poor machinability. This review covers various aspects of UAM, including ultrasonic vibration-assisted turning (UVAT), milling (UVAM) and grinding (UVAG), with emphasis on their influence on the machinability, tool wear behavior and surface integrity. It also highlights the limitations of single-energy field UAM, such as inconsistent energy transmission and tool fatigue, leading to the increasing demand for multi-field techniques. Therefore, the advanced machining strategies, i.e., ultrasonic plasma oxidation-assisted grinding (UPOAG), protective coating-assisted cutting, and dual-field ultrasonic integration (e.g., ultrasonic-magnetic or ultrasonic-laser machining), were discussed in terms of their potential to further improve TiAl alloys processing. In addition, the importance of predictive force models in optimizing UAM processes was also highlighted, emphasizing the role of analytical and AI-driven simulations for better process control. Overall, this review underscores the ongoing evolution of UAM as a cornerstone of high-efficiency and precision manufacturing, while providing a comprehensive outlook on its current applications and future potential in machining TiAl alloys. Full article

The quality change process of table grapes during cold chain logistics is complex and highly susceptible to vibration-induced damage. Traditional monitoring techniques not only consume significant human and material resources but also cause destructive effects on the fruit structure of table grapes, making them difficult to apply in practical scenarios. Based on this, this paper focuses on table grapes in cold chain business processes and designs a flexible wireless vibration sensor for monitoring the quality of table grapes during cold chain transportation. The hardware component of the system fabricates a flexible wireless vibration sensing for monitoring the quality of the table grape cold chain. In contrast, the software component develops corresponding data acquisition and processing functionalities. Using Summer Black table grapes purchased from Tianjin Hongqi Agricultural Market as the research subject, correlation and quality monitoring models for the cold chain process of table grapes were constructed. After Z-score standardization, the prediction results based on the MLR model achieved R² values all greater than 0.87 and RPD values all exceeding 2.7. Comparisons with other regression models demonstrated its optimal fitting performance for monitoring the quality of the cold chain for table grapes. This achieves non-destructive and high-precision data acquisition and processing during the cold chain process of table grapes, wirelessly transmitting results to terminal devices for real-time visual monitoring. Full article

As a key component of aero transmission systems, internal splines suffer from problems of low efficiency and poor precision in traditional lapping processes due to geometric deformation and high hardness after heat treatment. To address this, this study proposes an ultrasonic vibration lapping technology, which combines the synergistic mechanism of high-frequency vibration and free abrasive particles to achieve efficient and precise machining of small-sized hardened internal splines. By establishing an abrasive grain impact trajectory model and a rolling abrasive grain material removal model, the mechanisms of micro-cutting and impact removal of abrasive particles under ultrasonic vibration are revealed. Based on the local resonance theory, a longitudinal ultrasonic vibration system is designed, and its resonant frequency is optimized through finite element modal analysis. An ultrasonic lapping experimental platform is built, and heat-treated 9310 internal spline samples are used for experimental verification. The results show that, compared with traditional manual lapping, ultrasonic vibration lapping significantly improves the tooth profile and tooth lead deviations. After measurement, following ultrasonic vibration lapping, both the total tooth profile deviation and tooth lead deviation of the internal spline meet the Grade 6 accuracy requirements specified in GB/T 3478.1-2008 Cylindrical straight-tooth involute splines (Metric Module, Tooth Side Fit)—Part 1: General. This study confirms that ultrasonic vibration lapping can effectively correct the geometric accuracy of tooth surfaces and suppress thermal damage, and provides an innovative solution for the high-quality repair of aero transmission components. Full article

This paper investigates the nonlinear dynamics of the wheel-side planetary reducer, considering the tooth wear effect. The tooth wear model based on the Archard adhesion wear theory is established, and the impact of tooth wear on meshing stiffness and piecewise-linear backlash of the planetary gear system is discussed. Then, the torsional vibration model and dimensionless differential equations considering tooth wear for the wheel-side planetary reducer are established, in which meshing excitations include time-varying mesh stiffness (TVMS), piecewise-linear backlash, and transmission error. The dynamic responses are numerically solved using the fourth-order Runge–Kutta method. On this basis, the nonlinear dynamics, such as the bifurcation and chaos properties of the wheel-side planetary reducer with tooth wear, are analyzed. Simulation results demonstrate that the existence of tooth wear reduces meshing stiffness and increases backlash. The reduction in the meshing stiffness changes the bifurcation path and chaotic amplitude of the system, inducing chaotic phenomena more easily. The increase in the gear backlash causes a higher amplitude of the relative displacement and more severe vibration. Full article