Search Results (1,593)

The central purpose of this study is to develop and validate an advanced numerical model capable of simulating the vibrational behavior of the operator’s seat in a tractor-type agricultural vehicle designed for operation in protected horticultural environments, such as vegetable greenhouses. The three-dimensional (3D) model of the seat was created using SolidWorks 2023, while its dynamic response was investigated through Finite Element Analysis (FEA) in Altair SimSolid, enabling a detailed evaluation of the natural vibration modes within the 0–80 Hz frequency range. Within this interval, eight significant natural frequencies were identified and correlated with the real structural behavior of the seat assembly. For experimental validation, direct time-domain measurements were performed at a constant speed of 5 km/h on an uneven, grass-covered dirt track within the research infrastructure of INMA Bucharest, using the TE-0 self-propelled electric tractor prototype. At the operator’s seat level, vibration data were collected considering the average anthropometric characteristics of a homogeneous group of subjects representative of typical tractor operators. The sample of participating operators, consisting exclusively of males aged between 27 and 50 years, was selected to ensure representative anthropometric characteristics and ergonomic consistency for typical agricultural tractor operators. Triaxial accelerometer sensors (NexGen Ergonomics, Pointe-Claire, Canada, and Biometrics Ltd., Gwent, UK) were strategically positioned on the seat cushion and backrest to record accelerations along the X, Y, and Z spatial axes. The recorded acceleration data were processed and converted into the frequency domain using Fast Fourier Transform (FFT), allowing the assessment of vibration transmissibility and resonance amplification between the floor and seat. The combined numerical–experimental approach provided high-fidelity validation of the seat’s dynamic model, confirming the structural modes most responsible for vibration transmission in the 4–8 Hz range—a critical sensitivity band for human comfort and health as established in previous studies on whole-body vibration exposure. Beyond validating the model, this integrated methodology offers a predictive framework for assessing different seat suspension configurations under controlled conditions, reducing experimental costs and enabling optimization of ergonomic design before physical prototyping. The correlation between FEA-based modal results and field measurements allows a deeper understanding of vibration propagation mechanisms within the operator–seat system, supporting efforts to mitigate whole-body vibration exposure and improve long-term operator safety in horticultural mechanization. Full article

This paper presents a Vibration-based Track Anomaly Detection (VTAD) system designed for real-time monitoring of urban tram infrastructure. The novelty of VTAD is that it converts existing public transport vehicles into distributed mobile sensor platforms, eliminating the need for specialized diagnostic trains. The system integrates low-cost micro-electro-mechanical system (MEMS) accelerometers, Global Positioning System (GPS) modules, and Espressif 32-bit microcontrollers (ESP32) with wireless data transmission via Message Queuing Telemetry Transport (MQTT), enabling scalable and continuous condition monitoring. A stringent ±6σ statistical threshold was applied to vertical vibration signals, minimizing false alarms while preserving sensitivity to critical faults. Field tests conducted on multiple tram routes in Zagreb, Croatia, confirmed that the VTAD system can reliably detect and locate anomalies with meter-level accuracy, validated by repeated measurements. These results show that VTAD provides a cost-effective, scalable, and operationally validated predictive maintenance solution that supports integration into intelligent transportation systems and smart city infrastructure. Full article

Mitigating ground vibrations from sources like vehicles and construction operations poses significant challenges, often relying on computationally intensive numerical methods such as Finite Element Methods (FEM) or Boundary Element Methods (BEM) for analysis. This study addresses these limitations by developing and evaluating Machine Learning (ML) methodologies for the rapid and accurate prediction of Insertion Loss (IL), a critical parameter for assessing the effectiveness of open trenches as vibration barriers. A comprehensive database was systematically generated through high-fidelity numerical simulations, capturing a wide range of geometric, elastic, and physical configurations of a stratified geotechnical system. Three distinct ML strategies—Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Random Forests (RF)—were initially assessed for their predictive capabilities. Subsequently, a Meta-RF stacking ensemble model was developed, integrating the predictions of these base methods. Model performance was rigorously evaluated using complementary statistical metrics (RMSE, MAE, NMAE, R), substantiated by in-depth statistical analyses (normality tests, Bootstrap confidence intervals, Wilcoxon tests) and an analysis of input parameter sensitivity. The results clearly demonstrate the high efficacy of Machine Learning (ML) in accurately predicting IL across diverse, realistic scenarios. While all models performed strongly, the RF and the Meta-RF stacking ensemble models consistently emerged as the most robust and accurate predictors. They exhibited superior generalization capabilities and effectively mitigated the inherent biases found in the ANN and SVM models. This work is intended to function as a proof-of-concept and offers promising avenues for overcoming the significant computational costs associated with traditional simulation methods, thereby enabling rapid design optimization and real-time assessment of vibration mitigation measures in geotechnical engineering. 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

Vibrations induced by marine environmental loads can compromise the operational performance of offshore platforms and, in severe cases, result in structural instability or overturning. This study proposes a biomimetic nonlinear X-shaped vibration isolation system (NXVIS) to suppress earthquake-induced vibration response in offshore platforms. Compared with traditional passive vibration isolators, the key innovations of the NXVIS include: (1) the proposed NXVIS can be tailored to different load requirements and resonant frequencies to accommodate diverse offshore platforms and environmental loads; (2) By adjusting isolator parameters (e.g., link length and spring stiffness, etc.), the anti-vibration system can achieve different types of nonlinear stiffness and a large-stroke quasi-zero stiffness (QZS) range, enabling ultra-low frequency (ULF) vibration control without compromising load capacity. To evaluate the effectiveness of the designed NXVIS for vibration suppression of jacket offshore platforms under seismic loads, numerical analysis was performed on a real offshore platform subjected to seismic loads. The results show that the proposed nonlinear vibration isolation solution significantly reduces the dynamic response of deck displacement and acceleration under seismic loads, demonstrating effective low-frequency vibration control. This proposed NXVIS provides a novel and effective method for manipulating beneficial nonlinearities to achieve improved anti-vibration performance. Full article

As a large-scale flexible structure, the free-vibration characteristics of a horizontal single-axis solar tracking bracket (HSSTB) hold significance for its dynamic optimization design. However, due to material fabrication, construction processes, and harsh field service environments, structural parameters such as the elastic modulus inevitably exhibit uncertainty, leading to discrepancies between actual free-vibration characteristics and design values. This study considers the randomness of the steel elastic modulus and conducts a global sensitivity analysis of a real-life five-column HSSTB. First, the Kriging method is employed to build a surrogate model to describe the natural frequencies of the HSSTB and its stochastic parameters, which enables efficient evaluation of the statistical characteristics of the HSSTB’s natural frequencies. Further, the Sobol indices are utilized to quantify the influence of parameter randomness on the natural frequencies. The results indicate that the mean values of the first five natural frequencies are slightly lower than the design values. The first, fourth, and fifth natural frequencies of the five-column HSSTB are predominantly influenced by the middle three columns, while the second and third natural frequencies are more susceptible to the two edge columns. Full article

We present a photoacoustic spectroscopy (PAS)-based method using a laser Doppler vibrometer (LDV) for real-time detection of laser-induced damage (LID) in optical components. By measuring audible frequency surface vibrations, the method enables remote, non-contact, and sensitive detection. Experiments with various dielectric optics (slide glass and single-layer coatings) and pulse durations (7 ns and 360 ps) of an Nd:YAG laser (wavelength of 1064 nm) showed detection accuracy comparable to microscopy. Vibration spectra correlated with natural modes calculated by finite element modeling, and vibrations according to the detecting location were observed. The method remained effective under typical mounting conditions, demonstrating its practical applicability. This PAS-LDV approach offers a promising tool for in situ monitoring of LID in high-power laser systems. Full article

This article focuses on the problem of building a real-world predictive maintenance system for hydraulic piston pumps. Particular attention is given to the issue of limited data availability regarding the failure state of systems with a damaged valve plate. The main objective of this work was to analyze the impact of imbalanced data on the quality of the failure prediction system. Several data balancing techniques, including oversampling, undersampling, and combined methods, were evaluated to overcome the limitations. The dataset used for evaluation includes recordings from eleven sensors, such as pressure, flow, and temperature, registered at various points in the hydraulic system. It also includes data from three additional vibration sensors. The experiments were conducted with imbalance ratios ranging from 0.5% to a fully balanced dataset. The results indicate that two methods, Borderline SMOTE and SMOTE+Tomek Links, dominate. These methods allowed the system to achieve the highest performance on a completely new dataset with different levels of damaged valve plates, for the balance rate larger than three percent. Furthermore, for balance rates below one percent, the use of data balancing methods may adversely affect the model. Finally, our results indicate the limitations of the use of cross-validation procedures when assessing data balancing methods. Full article

The proliferation of IoT devices has created vast sensor networks that generate continuous time-series data. Efficient and real-time processing of these signals is crucial for applications such as predictive maintenance, healthcare monitoring, and environmental sensing. This paper proposes a lightweight framework that combines classical signal processing techniques (Fourier and Wavelet-based feature extraction) with edge-deployed machine learning models for anomaly detection. By performing feature extraction and classification locally, the approach reduces communication overhead, minimizes latency, and improves energy efficiency in IoT nodes. Experiments with synthetic vibration, acoustic, and environmental datasets showed that the proposed Shallow Neural Network achieved the highest detection performance (F1-score ≈ 0.94), while a Quantized TinyML model offered a favorable trade-off (F1-score ≈ 0.92) with a 3× reduction in memory footprint and 60% lower energy consumption. Decision Trees remained competitive for ultra-constrained devices, providing sub-millisecond latency with limited recall. Additional analyses confirmed robustness against noise, missing data, and variations in anomaly characteristics, while ablation studies highlighted the contributions of each pipeline component. These results demonstrate the feasibility of accurate, resource-efficient anomaly detection at the edge, paving the way for practical deployment in large-scale IoT sensor networks. Full article

Low-voltage electrical appliances, represented by circuit breakers, contactors, and proximity switches, are widely used in various electrical control systems in transportation vehicles. During vehicle operation, engine vibrations, poor workplace balance, constantly changing operating directions, unbalanced transmission systems, emergency braking, and other factors can all cause variable loads. These variable loads may decrease the effectiveness of low-voltage electrical contacts; the failure rate of the low-voltage electrical appliances used in transportation vehicles is three times that of normal indoor low-voltage electrical appliances. This study analyzes the failure mechanism of low-voltage electrical appliances used in transportation vehicles and establishes a model for their reliability evaluation and prediction based on a variable-load data-driven approach. This variable-load data comes from the constructed simulated vehicle operation test platform. Nonlinear variable-load test data generated by simulation is collected through the test platform and is then processed. An evaluation feature dataset is constructed and input into the reliability evaluation and prediction model to obtain the remaining life of low-voltage electrical appliances. The analysis and verification of the predicted evaluation values and the real values detected through platform equipment showed that the accuracy of this model for these appliances based on the variable-load data-driven approach reached 94%, meeting the requirements of practical applications. This method used in this study to derive the model can provide a theoretical basis for online evaluation and prediction of low-voltage electrical appliance reliability for transportation vehicles. This can not only prevent vehicle failures and avoid sudden accidents, but also fully utilize the remaining life of low-voltage electrical appliances and reduce the cost of replacing them. Full article

The inversion of shallow underground vibration fields primarily relies on signals collected by numerous sensors deployed on the surface. However, the accuracy of inversion is affected by the spatial distribution of these sensors. Therefore, under limited measurement points, signal reconstruction at unknown locations remains a critical challenge. To address this problem, we developed an SC-Net-based self-supervised interpolation method for missing wavefield data in shallow subsurface applications. This study utilizes incomplete seismic data acquired in real-world scenarios to train a neural network for seismic data interpolation, thereby expanding the sampled signals required for inversion. Since available seismic data samples are often scarce in practice, we adopt a hybrid training strategy combining simulated and real data. Specifically, a large number of numerically simulated samples are jointly trained with a limited set of real-world measurements. Furthermore, to enhance the robustness of network outputs, we integrate the Mean Teacher model framework and propose a self-supervised learning approach for missing data. Additionally, to enable the network to effectively capture long-range dependencies in both frequency and spatial domains of seismic data, we introduce a dual-branch feature fusion network that jointly models channel-wise and spatial relationships. Finally, in our actual field explosion experiments conducted at the test site, we demonstrated improved accuracy of our method through comparative analysis with several typical interpolation neural networks. Three ablation studies are also designed to demonstrate the effectiveness of the proposed approach. Full article

As global Marine resource development continues to expand into deep-sea and ultra-deep-sea domains, the intelligent and green transformation of deep-sea aquaculture equipment has become a key direction for high-quality development of the Marine economy. Large deep-sea cages are considered essential equipment for deep-sea aquaculture. However, there are significant challenges associated with ensuring their structural integrity and long-term monitoring capabilities in the complex Marine environments characteristic of deep-sea aquaculture. The present study focuses on large deep-sea cages, addressing their dynamic response challenges and long-term monitoring power supply needs in complex Marine environments. The present study investigates the nonlinear vibration characteristics of flexible net structures under complex fluid loads. To this end, a multi-field coupled dynamic model is constructed to reveal vibration response patterns and instability mechanisms. A self-powered sensing system based on triboelectric nanogenerator (TENG) technology has been developed, featuring a curved surface adaptive TENG array for the real-time monitoring of net vibration states. This review aims to focus on the research of optimizing the design of curved surface adaptive TENG arrays and deep-sea cage monitoring. The present study will investigate the mechanisms of energy transfer and cooperative capture within multi-body coupled cage systems. In addition, the biomechanics of fish–cage flow field interactions and micro-energy capture technologies will be examined. By integrating different disciplinary perspectives and adopting innovative approaches, this work aims to break through key technical bottlenecks, thereby laying the necessary theoretical and technical foundations for optimizing the design and safe operation of large deep-sea cages. Full article

To address the difficulty in obtaining analytical solutions for the torsional vibration response of pile foundations in orthotropic layered soil foundations subjected to torsional excitation at the pile top, this study investigates a layered recursive algorithm based on the Hankel transform. An integral transformation method is employed to reduce the dimensionality of the coupled pile–soil torsional vibration equations, converting the three-dimensional system of partial differential equations into a set of ordinary differential equations. Combining the constitutive properties of transversely anisotropic strata with interlayer contact conditions, a transfer matrix model is established. Employing inverse transformation coupled with the Gauss–Kronrod integration method, an explicit frequency-domain solution for the torsional dynamic impedance at the pile top is derived. The research findings indicate that the anisotropy coefficient of the foundation significantly influences both the real and imaginary parts of the impedance magnitude. The sequence of soil layer distribution and the bonding state at interfaces jointly affect the nonlinear transmission characteristics of torque along the pile shaft. Full article

A modular, multi-functional (encompassing data acquisition, management, preprocessing, and transmission) sensing (MMFS) system based upon the Internet of Things (IoT) paradigm is discussed in this paper with the goal of continuous real-time, multi-sensor and multi-location monitoring of aircraft (including drones) structural performances during flight. According to industrial and system requirements, a microcontroller and four sensors (strain, acceleration, vibration, and temperature) were selected and integrated into the system. To enable the determination of potential in-flight failures and estimates of the remaining useful service life of the aircraft, resistance strain gauge networks, piezoelectric sensors for capturing structural vibrations and impact, accelerometers, and thermistors have been integrated into the MMFS system. Real flight tests with Evektor’s Cobra VUT100i and SportStar RTC aircraft have been undertaken to demonstrate the features of recorded data and provide requirements for the MMFS functional design. Real flight test data were analysed, indicating that a sampling rate of 1000 Hz is necessary to balance representation of relevant features within the data and potential loss of quality in fatigue life estimation. The design and evaluation of the performance of a prototype (evaluated via representative stress/strain experiments using an Instron Hydraulic 250 kN machine within laboratories) are detailed in this paper. Full article

This paper outlines the inception, conceptualization and primary morphological selection of a robotic system that employs raw lunar regolith for constructing protective berms and shelters on the Moon’s surface. The lunar regolith is considered the most readily available material for in situ resource utilization on the Moon. The lunar environment is characterized, and the operational task is defined, informing the development of high-level system requirements and a functional analysis through the glass-box method. The key morphological areas are identified, and candidate concepts are evaluated using the Analytic Hierarchy Process (AHP). The evaluation process employs a new approach to aggregating expert data through the ZM_II method to establish priorities of the design criteria, which eliminates the need for pairwise comparisons in data collection. Each criterion is associated with a specific and quantifiable metric, which is then used to evaluate the morphologies during the AHP. The selected morphologies are determined as: a vibrating hopper for intake (normalized decision value of 27.5% out of 5 candidate solutions), a roller system for container deployment and filling (26.2% out of 7), a magnetic RCU interface (22.6% out of 7), and a 4-DoF manipulator to place the RCUs in the environment (23.6% out of 5). The final morphology is selected by combining the decision values across the primary morphological areas into a unified decision metric. This is followed by the preliminary selection of the system’s surrounding architecture. The design conceptualization is performed within a real-life operational scenario, namely, to create a blast berm for the landing pad using the lunar regolith provided by an existing excavator. The next phase of the work will include the system’s detailed design, as well as investigations on the requirements for a variety of construction tasks on the lunar surface. Full article