Search Results (2,619)

Predictive maintenance based on vibration monitoring can significantly improve gearbox reliability in heavy-industry environments. Although it is well established in vibration engineering that operating regimes influence vibration levels, the contribution of this work lies in providing an integrated, data-driven diagnostic linkage between continuously acquired IoT vibration indicators and key process/operational variables to identify and quantify the dominant drivers of vibration escalation. This study deployed wireless IoT sensors for continuous acquisition of RMS vibration and lubrication temperature in gearboxes operating in cement and mining plants and applied multivariate machine learning models to detect anomalies and identify root causes. We compared boosted multilayer feedforward neural networks, boosted trees, and k-nearest neighbors using RMS vibration and process variables including mill feed, lubrication pressures, and temperature. The boosted neural network delivered superior validation performance and isolated low or near-zero mill feed during operation as the primary driver of elevated RMS vibration, with lubrication instability acting as a secondary interacting factor. This shifts the diagnosis from a generic “high vibration during transients” statement to actionable operational mitigations—minimum feed set-points, controlled ramping logic, and lubrication pressure governance—supported by multivariate evidence. Our results motivate further validation with k-fold and out-of-time tests. Full article

Phase-sensitive optical time domain reflectometry (Φ-OTDR) is a highly sensitive distributed vibration sensing technology crucial for pipeline safety monitoring. However, its sensitivity makes it susceptible to environmental interference, leading to frequent false alarms by misclassifying routine activities as threats. To enable accurate threat identification and rapid response, this study proposes a lightweight LightPatch Vision Transformer (LP-ViT) model suitable for edge deployment. We establish a mapping between excavator-pipeline distance and threat levels: “direct intrusion” (within 5 m), “high-risk operation” (within 10 m), and “background construction” (beyond 15 m). The LP-ViT model is developed through structural optimization and parameter compression of the standard Vision Transformer, achieving a 96.6% reduction in parameter count while maintaining a high classification accuracy of 89.9%. Furthermore, via knowledge distillation, we derive an ultra-lightweight student model with merely 0.37 M parameters, which achieves an inference latency of 5.5 ms per sample, enabling millisecond-level threat detection and response. The proposed solution effectively enhances both the classification accuracy and real-time performance of Φ-OTDR systems in complex environments, providing a practical pathway for implementing edge intelligence in pipeline safety monitoring. Full article

A vibration energy harvester (VEH) based on the principle of variable magnetic reluctance has been developed to enable wireless and maintenance-free power supply for condition monitoring sensors in vibrating machinery. Conventional battery or wired solutions are often impractical due to limited lifetime and high installation costs, motivating the use of vibration-based energy harvesting. The proposed VEH converts mechanical vibrations into electrical energy through the relative motion of a movable ferromagnetic core within a magnetic circuit. Unlike conventional VEH designs, where the magnet is the moving element, this concept utilizes a movable ferromagnetic core in combination with a stationary pole piece for voltage induction. This configuration enables a compact and easily adjustable proof mass, as neither the coil nor the magnet needs to be moved. The VEH is designed to operate effectively under excitation frequencies between $16 \mathrm{H}\mathrm{z}$ and $50 \mathrm{H}\mathrm{z}$ and acceleration levels from $9.81 {\displaystyle \frac{\mathrm{m}}{{\mathrm{s}}^2}}$ (equivalent to $1 \mathrm{g}$) up to $98.1 {\displaystyle \frac{\mathrm{m}}{{\mathrm{s}}^2}}$ (equivalent to $10 \mathrm{g}$). To ensure a reliable power supply, the VEH must deliver a minimum electrical output of $0.1 \mathrm{m}\mathrm{W}$ at the lowest excitation ($1 \mathrm{g}$) while maintaining structural integrity. Additionally, the maximum permissible displacement amplitude of the movable core is limited to $1.15 \mathrm{m}\mathrm{m}$ to avoid mechanical damage and ensure durability over long-term operation. Coupled magnetic-transient and mechanical finite element method (FEM) simulations were conducted to analyze the system’s dynamic behavior and electrical power output across varying excitation frequencies and accelerations. A laboratory prototype was developed and tested under controlled vibration conditions to validate the simulation results. The experimental measurements confirm that the VEH achieves an electrical output of $0.166 \mathrm{m}\mathrm{W}$ at $9.81 {\displaystyle \frac{\mathrm{m}}{{\mathrm{s}}^2}}$ and $16 \mathrm{H}\mathrm{z}$, while maintaining the maximum allowable displacement amplitude of $1.15 \mathrm{m}\mathrm{m}$, even at $98.1 {\displaystyle \frac{\mathrm{m}}{{\mathrm{s}}^2}}$ ($10 \mathrm{g}$) and $50 \mathrm{H}\mathrm{z}$. The strong agreement between simulation and experimental data demonstrates the reliability of the coupled FEM approach. Overall, the proposed VEH design meets the defined performance targets and provides a robust solution for powering wireless sensor systems under a wide range of vibration conditions. Full article

Broadband piezoelectric energy harvesters (PEHs) are attractive for powering self-sustained sensing nodes in industrial monitoring, structural health monitoring, and distributed IoT systems, where ambient vibration spectra are often uncertain, drifting, and broadband. However, tuning multiple resonant peaks in PEH arrays usually relies on time-consuming finite element (FE) parameter sweeps or iterative optimizations, which becomes a practical bottleneck when rapid, site-specific customization is required. This study presents a data-driven inverse-design framework for a five-beam PEH array based on a tandem neural network (TNN). A forward multilayer perceptron (MLP) surrogate is first trained using 10,000 COMSOL-generated samples to predict the array’s characteristic frequencies from the design variables (end masses $M_1$–$M_5$ and tilt angle $\alpha$), achieving >98% prediction accuracy with a prediction time <1 s, thereby enabling efficient replacement of repeated FE evaluations during design. The trained MLP is then coupled with an inverse-design network to form the TNN, which maps target characteristic-frequency sets directly to physically feasible parameters through the learned surrogate. Multiple representative target frequency sets are demonstrated, and the TNN-generated designs are independently verified by COMSOL frequency–response simulations. The resulting arrays achieve broadband operation, with bandwidths exceeding 10 Hz. By shifting most computational cost to offline dataset generation and training, the proposed spectrum-to-parameter pathway enables near-instant parameter design and reduces reliance on exhaustive FE tuning, supporting rapid, application-specific deployment of broadband PEH arrays. Full article

Early gearbox defect detection is imperative for reducing unplanned downtime, ensuring reliability and efficiency, and minimizing maintenance expenses. In recent years, with the rise of Artificial Intelligence (AI) and digital transformation, gearbox defect detection using AI has gained popularity. Machine learning (ML) classifiers are very popular and transform gearbox condition monitoring from manual to automatic monitoring systems. This work proposes a moving window-based method for extracting statistical features from recorded vibration signals from the gearbox. The extracted features were used to train traditional ML classifiers. Moving window sizes of 300, 400, 500, 600, 700, and 800 were applied to extract statistical features from the publicly available benchmark dataset. The six different moving window sizes caused six types of datasets, each one corresponding to the moving window size. The generated datasets were partitioned using the K-fold cross-validation method to train and test ML models. This study explored and evaluated seven prominent ML classifiers: Decision Tree, Random Forest, Support Vector Machine (SVM), Naïve Bayes, K-Nearest Neighbor (KNN), Gradient Boosting Classifier (GBC), and Logistic Regression. The experimental results demonstrated that SVM, Logistic Regression, and GBC can outperform other ML classifiers. The experimental results in terms of accuracy, precision, and recall revealed that the ML classifier’s performance improves as the size of the moving window used for feature extraction increases. Full article

This paper presents the development of a robot-oriented Distributed Acoustic Sensing (DAS) system designed for environmental monitoring and hazard detection on ground robotic platforms. Unlike conventional DAS solutions primarily intended for stationary or quasi-stationary infrastructures, the proposed approach explicitly accounts for robot-induced mechanical vibrations, mobility constraints, and limited onboard resources. A dedicated anti-jitter signal processing pipeline combined with edge-based data processing is introduced to suppress motion-induced strain components while preserving weak external acoustic signals. The system integrates optical fiber deployment along the robot structure using flexible guides and vibration-isolated clamps, ensuring stable mechanical coupling under continuous motion. Experimental validation, including laboratory tests and preliminary outdoor field trials, demonstrates reliable detection of acoustic events in the 10–200 Hz frequency range, with reduced processing latency of 80–100 ms and a detection reliability of up to 95%. Comparative analysis with conventional sensors confirms the advantages of the proposed DAS-based approach in terms of sensitivity, spatial coverage, and robustness. The results demonstrate the feasibility and effectiveness of DAS technology for real-time sensing applications on mobile robotic platforms. Full article

The lack of reliable sensory input from prosthetic limbs limits transfemoral amputees’ ability to perceive limb movement without visual monitoring. This study evaluated design parameters of a proposed forearm-based vibrotactile system in a pre-clinical, design-level perceptual evaluation, conveying prosthetic joint positions through patterned vibrations to provide non-invasive proprioceptive feedback. Healthy participants completed two experiments assessing detection of tactile cues from dual-actuator bands on the wrist and elbow representing assumed ankle and knee positions. The effects of temporal structuring (sequential vs. simultaneous stimulation), actuator configuration, amplitude and frequency settings, and signal duration on response accuracy were examined. Sequential vibrations produced significantly higher recognition accuracy than simultaneous presentation (72.4% vs. 42.7%, p < 0.001) in a variety of vibration signal parameter values. Actuator placement also influenced performance: simultaneous stimulation on opposite forearm sides yielded significantly lower accuracy (p < 0.001) than same-side configurations, whereas this directional effect was not significant for sequential presentation. Accuracy did not differ significantly between equal and unequal amplitude or frequency levels across actuators. Longer stimulus durations improved accuracy, increasing from 82.3% at 60 ms to 92.5% at 240 ms, though the results indicated a saturation point, suggesting an optimal temporal window. These findings inform the design of forearm-based sensory feedback systems for improved prosthetic limb control. Full article

The liner temperature distribution of an aero-engine combustor is a critical parameter for evaluating its cooling effectiveness. It provides essential guidance for designing the combustor cooling flow field, assessing combustion performance, identifying critical regions, and predicting service life. However, current research on surface temperature field measurements in real or model aero-engine combustors remains limited. Existing studies focus primarily on the liner temperature measurement under near-steady-state conditions, with less attention to its dynamic changes. This study employs Laser-Induced Phosphorescence (LIP) thermometry to measure the effusion-cooled liner temperature field of an aero-engine model combustor under various CH₄/Air swirling premixed flame conditions and varying blowing ratios. Based on the geometric characteristics of the effusion-cooled liner, an optimization method for matching phosphorescence images of different wavelengths is proposed. This enhances the applicability of phosphorescence intensity ratio-based LIP thermometry in high-vibration environments. The study specifically focuses on the dynamic response of LIP thermometry for monitoring combustor liner temperature. The instantaneous effects of blowing ratio variations on liner temperature rise rates were investigated. Additionally, the influence mechanisms of a broad range of combustion conditions and the blowing ratios on the combustor liner temperature distribution and cooling effectiveness were examined. These findings provide theoretical and technical support for cooling design and dynamic liner temperature field measurement in real aero-engine combustors. Full article

Digital transformation in water conservancy and urban water utilities demands perception systems that are accurate, fast, and energy-efficient and maintainable over long service lifecycles at the edge. We present HydroSNN, a neuromorphic computer-vision framework that couples an event-driven sensing pipeline with a spiking-transformer backbone to support monitoring of canals, reservoirs, treatment plants, and buried pipeline networks. By reducing always-on compute and unnecessary data movement, HydroSNN targets sustainability goals in smart water infrastructure: lower operational energy use, fewer site visits, and improved resilience under harsh illumination and weather. HydroSNN introduces three novel components: (i) spiking temporal tokenization (STT), which converts asynchronous events and optional frames into latency-aware spike tokens while preserving motion cues relevant to hydraulics; (ii) physics-guided spiking attention (PGSA), which injects lightweight mass-conservation/continuity constraints into attention weights via a differentiable regularizer to suppress physically implausible interactions; and (iii) cross-modal self-supervision (CM-SSL), which aligns RGB frames, event streams, and low-cost acoustic/vibration traces using masked prediction to reduce annotation requirements. We evaluate HydroSNN on public water-surface and event-vision benchmarks (MaSTr1325, SeaDronesSee, DSEC, MVSEC, DAVIS, and DDD20) and report accuracy, latency, and an operation-based energy proxy. HydroSNN improves mIoU/F1 over strong CNN/ViT baselines while reducing end-to-end latency and the estimated energy proxy in event-driven settings. These efficiency gains are practically relevant for off-grid or power-constrained deployments and support sustainable development by enabling continuous, low-power monitoring and timely anomaly response. These results demonstrate that event-driven spiking vision, augmented with simple physics guidance, offers a practical and efficient solution for resilient perception in smart water infrastructure. Full article

With the increasingly significant impact of high-wind-load environments on power equipment, the wind stability of the corona ring has become a key issue to ensure the safe operation of power grids. The wind-induced vibration response and fatigue characteristics of the corona ring in power equipment under different wind speeds, wind direction angles and wind attack angles are systematically studied via wind tunnel tests and numerical simulation. The results show that the peak acceleration and displacement of the corona ring are positively correlated with the increase in wind speed, and the wind-induced response is the most significant under the condition of 0° wind direction angle and 5° wind attack angle. In the wind speed range of 5 m/s to 8 m/s, the corona ring is prone to vortex-induced vibration. Through fatigue analysis, it is determined that the vertical support rod and the welding position and the bolt connection of the support frame are the stress concentration areas. The research results reveal the key weak points of the corona ring and provide an important basis for optimization design and safety monitoring, and they are of great significance for improving the wind resistance of power equipment. Full article

The present study extends the previous work which was concerned with the identification of damage in GFRP composite plates by damage detection algorithms such as the Normalized Curvature Damage Factor (NCDF), Strain Energy Difference (SED), and Damage Index (DI), using a novel damage (crack) modeling technique called the ‘Node-Releasing Technique’ (NRT) in Finite Element Analysis (FEA) for modeling and detecting perpendicular and slant partial-depth cracks in GFRP composite beams. This study explores the sensitivity of the damage modeling technique NRT in damage detection for composite beams using the NCDF algorithm, since it was concluded in the previous work that the NCDF performs better compared to the other methods when detecting both perpendicular and slant partial-depth cracks. This study also examines the variations in the Frequency Response Function (FRF) as another novel tool for identifying even small-scale damage. Most prior research in this domain has focused on variations in natural frequency, displacement mode shape, and damping as indicators for detecting and localizing structural damage through various experimental, theoretical, and computational approaches. However, these conventional parameters often lack the sensitivity required to detect small-scale damage and, still, there exists a gap in the use of the node-releasing technique in FEA to model the partial-depth perpendicular and slant crack damage in laminated composite structures, such as beam-like structures. To fill this gap, the present study attempts to use Curvature Mode Shapes (CMS)-based NCDF, obtained from numerical modal analysis, and variations in the Frequency Response Function (FRF), obtained through harmonic analysis, as more sensitive indicators for damage detection in laminated composite beams. FEA simulations are performed using the commercial FEA software package ANSYS 2021 R1 to obtain the first five flexural natural frequencies and the corresponding displacement mode shapes of both the intact and damaged composite beams. The curvature mode shapes are obtained from the displacement mode shapes data using the central difference approximation method to compute the NCDF. Simultaneously, GFRP composite beams were fabricated by the hand lay-up method, and Experimental Modal Analysis (EMA) was employed to substantiate the FE model and the validity of the numerical results. By combining both numerical and experimental methods, we proved that NCDF and FRF are reliable tools to determine and locate structural damage, even at a comparatively small scale. In general, the results indicate that NCDF is a stable and practically applicable parameter to locate cracks in laminated composite beams and provide meaningful information to be used as guidelines in applications of vibration-based structural health monitoring. Full article

Accurate identification of road roughness is pivotal for optimizing vehicle suspension control and enhancing passenger comfort. However, existing data-driven methods often struggle to balance classification accuracy with the strict computational constraints of real-time onboard monitoring. To address this challenge, this paper proposes a lightweight and robust road roughness classification framework utilizing a single sprung mass accelerometer. First, to overcome the scarcity of labeled real-world data and the limitations of linear models, a high-fidelity co-simulation platform combining CarSim and Simulink is established. This platform generates physically consistent vibration datasets covering ISO A–F roughness levels, effectively capturing nonlinear suspension dynamics. Second, we introduce DB-MLP, a novel Dual-Branch Multi-Layer Perceptron architecture. In contrast to computationally intensive Transformer or RNN-based models, DB-MLP employs a dual-branch strategy with multi-resolution temporal projection to efficiently capture multi-scale dependencies, and integrates dual-domain (time and position-wise) feature transformation blocks for robust feature extraction. Experimental results demonstrate that DB-MLP achieves a superior accuracy of 98.5% with only 0.58 million parameters. Compared to leading baselines such as TimeMixer and InceptionTime, our model reduces inference latency by approximately 20 times (0.007 ms/sample) while maintaining competitive performance on the specific road classification task. This study provides a cost-effective, high-precision solution suitable for real-time deployment on embedded vehicle systems. Full article

The H2IOSC project aims to establish a federated cluster of European distributed research infrastructures involved in the humanities and cultural heritage sectors, with operating nodes across Italy. Through four key RIs—DARIAH-IT, CLARIN, OPERAS, and E-RIHS—the project promotes collaboration among researchers with interdisciplinary expertise. Within this framework, DIGILAB functions as the digital access platform for the Italian node of E-RIHS. Conceived as a socio-technical infrastructure for the Heritage Science community, DIGILAB is designed to manage heterogeneous data and metadata through advanced knowledge graph representations. The platform adheres to the FAIR principles and supports the complete data lifecycle, enabling the development and maintenance of Heritage Digital Twins. DIGILAB integrates diverse categories of information related to cultural sites and objects, encompassing historical and artistic datasets, diagnostic analyses, 3D models, and real-time monitoring data. This monitoring capability is achieved through the deployment of cutting-edge Internet of Things (IoT) technologies and large-scale Wireless Sensor Networks (WSNs). As part of DIGILAB, we developed SENNSE (v1.0), a fully open hardware/software platform dedicated to environmental and structural monitoring. SENNSE allows the remote, real-time observation and control of cultural heritage sites (collecting microclimatic parameters such as temperature, humidity, noise levels) and of cultural objects (collecting object-specific data including vibrations, light intensity, and ultraviolet radiation). The visualization and analytical tools integrated within SENNSE transform these datasets into actionable insights, thereby supporting advanced research and conservation strategies within the Cultural Heritage domain. In the following sections, we provide a detailed description of the SENNSE platform, outlining its hardware components and software modules, and discussing its benefits. Furthermore, we illustrate its application through two representative use cases: one conducted in a controlled laboratory environment and another implemented in a real-world heritage context, exemplified by the “Biblioteca Bernardini” in Lecce, Italy. Full article

During on-orbit service, spacecraft are subjected to hypervelocity impacts (HVIs) from micrometeoroids and space debris, causing diverse damage types that challenge structural health assessment. Unimodal approaches often struggle with similar damage patterns due to mechanical noise and imaging distance variations. To overcome these physical limitations, this study proposes a physics-informed multimodal fusion framework. Innovatively, we integrate a distance-aware infrared enhancement strategy with vibration spectral subtraction to align heterogeneous data qualities while employing a dual-stream ResNet coupled with Dempster–Shafer (D-S) evidence theory to rigorously resolve inter-modal conflicts at the decision level. Experimental results demonstrate that the proposed strategy achieves a mean accuracy of 99.01%, significantly outperforming unimodal baselines (92.96% and 97.11%). Notably, the fusion mechanism corrects specific misclassifications in micro-cracks and perforation, ensuring a precision exceeding 96.9% across all categories with high stability (standard deviation 0.74%). These findings validate the efficacy of multimodal fusion for precise on-orbit damage assessment, offering a robust solution for spacecraft structural health monitoring. Full article

Viticulture is facing growing economic and environmental pressures that demand a transition toward intelligent and autonomous crop management systems. Phytopathologies remain one of the most critical threats, causing substantial yield losses and reducing grape quality, while regulatory restrictions on agrochemicals and sustainability goals are driving the development of precision agriculture solutions. In this context, early disease detection is crucial; however, current visual inspection methods are hindered by subjectivity, cost, and delayed symptom recognition. This study presents a fully autonomous robotic platform developed within the Agrimet project, enabling continuous, high-frequency monitoring in vineyard environments. The system integrates a tracked mobility base, multimodal sensing using RGB-D and thermal cameras, an AI-based perception framework for leaf localisation, and a compliant six-axis manipulator for biological sampling. A custom control architecture bridges standard autopilot PWM signals with industrial CANopen motor drivers, achieving seamless coordination among all subsystems. Field validation in a Sicilian vineyard demonstrated the platform’s capability to navigate autonomously, acquire multimodal data, and perform precise georeferenced sampling under unstructured conditions. The results confirm the feasibility of holistic robotic systems as a key enabler for sustainable, data-driven viticulture and early disease management. The YOLOv10s detection model achieved good precision and F1-score for leaf detection, while the integrated Kalman filtering visual servoing system demonstrated low spatial tolerance under field conditions despite foliage sway and vibrations. Full article