Search Results (1,355)

LiDAR sensors are becoming more common and are going to be widely adopted in vehicles in the future by reducing the production cost of the time-of-flight units. Manufacturers are uncertain about the placement, cover material, and shape of the assembly to achieve the optimal performance of the LiDAR, especially in rainy conditions. Although there are existing methodologies for evaluating the visibility and signal intensity of point clouds, there are no indexing approaches available since they would require a broad and comprehensive dataset and realistic and repeatable conditions to perform parametric studies. A matrix of rain conditions with quantified raindrop distribution characteristics is simulated using a wind tunnel via the wind-driven rain concept to produce the realistic impact of raindrops onto the sensor assembly surface at various wind speeds. This paper presents a performance prediction model method for LiDAR sensors and showcases the capability of such a model to provide insights quantitatively when comparing variations. The model is 3-dimensional, including rain conditions perceived by a moving vehicle at different speeds, material properties of surface wettability, and LiDAR visibility in rain compared to dry conditions. The observed LiDAR signal degradation follows an exponential manner, for which this study provides experimentally derived coefficients, enabling quantitative prediction across materials, topologies, rain, and driving speed conditions. Full article

This paper presents a robust sensorless control strategy for a dual-inverter doubly fed induction motor (DFIM) designed for high-performance electric vehicle (EV) traction systems. The proposed scheme eliminates the mechanical speed sensor by employing a sliding-mode observer (SMO) for real-time estimation of rotor speed and flux, ensuring accurate feedback under load disturbances and thereby enhancing reliability while reducing implementation cost. The DFIM is powered by two voltage-source inverters that independently control the stator and rotor windings through space vector pulse-width modulation (SVPWM). A power-sharing strategy optimally distributes the electromagnetic power between the two converters, ensuring smooth transitions between sub-synchronous and super-synchronous operating modes. Furthermore, a stator-flux-oriented vector control (SFOC) scheme incorporating a graphical torque optimization algorithm is developed to maximize torque while satisfying inverter and machine constraints across both base-speed and flux-weakening regions. The stability of the SMO-based estimation and closed-loop control is rigorously validated using Lyapunov theory. Comprehensive MATLAB R2024b/Simulink simulations conducted under the WLTC-Class 3 driving cycle confirm high accuracy and robustness, showing fast dynamic response, precise speed estimation, and smooth torque behavior across the full speed range. The results demonstrate that the SMO-based DFIM drive offers a cost-effective and reliable solution for next-generation EV traction applications. Full article

In response to the key issues of complex internal structure, significant attenuation of partial discharge (PD) ultrasound signal propagation, and low sensor sensitivity in large oil–immersed power transformers, this paper analyzes the multi–order resonant mode vibration characteristics of the shaft–type fiber optic ultrasound sensor core structure. The displacement distribution patterns of the core structure in both transverse and longitudinal resonant modes are clarified. A strategy using oblique fiber winding rings is proposed to eliminate the problems of strain cancellation and non–accumulation of displacement in transverse and longitudinal resonant modes, which are common in traditional fiber optic ultrasound sensors with parallel fiber windings. Furthermore, design principles are provided to enhance the coverage of the free end and the high–strain regions with semi–high symmetry, as well as the vector–integrated response suitable for multi–order modes. Experimental results show that, in typical PD model detection, the oblique winding sensor exhibits a more prominent response near the high–order resonances of the core, with a detection sensitivity approximately 2.5 times higher than that of the parallel winding structure, and an overall sensitivity at least 7.4 times greater than that of traditional Piezoelectric (PZT) sensors. This demonstrates that the fiber winding method is a key design parameter determining the acoustic–solid coupling efficiency and high sensitivity performance of shaft–type fiber optic interferometric PD sensors, providing a feasible path for high–reliability fiber optic sensing solutions for online monitoring of transformer partial discharges. Full article

In low-amplitude and low-frequency vibration environments, the energy harvesting efficiency of self-powered wireless sensor nodes is insufficient, limiting their long-term autonomous operation. To address this issue, a micro piezoelectric–electromagnetic hybrid energy harvester is designed, aiming to enhance energy capture efficiency through structural integration and parameter optimization. The study is conducted entirely through numerical simulations. A coaxial integrated architecture is adopted, combining a piezoelectric cantilever beam array with an electromagnetic induction module. The piezoelectric layer uses lead magnesium niobate–lead titanate (PMN-PT) solid solution material with a thickness of 0.2 mm. The electromagnetic module employs copper wire coils with a diameter of 0.08 mm, winding 1500–3000 turns, paired with N52-type neodymium–iron–boron (NdFeB) permanent magnets. To improve energy conversion efficiency, the optimization parameters include the length-to-thickness ratio of the cantilever beam, the mass of the tip mass, the number of coil turns, and the spacing of the permanent magnets. Each parameter is set at four levels for orthogonal experiments. A multi-physics coupling model is established using ANSYS Workbench 2023, covering structural dynamics, piezoelectric effects, and the electromagnetic induction module. The mesh size is set to 0.1 mm. The energy output characteristics are analyzed under vibration frequencies of 0.3–12 Hz and amplitudes of 0.2–1.0 mm. Simulation results show that the optimized hybrid harvester achieves 45% higher energy conversion efficiency than a single piezoelectric structure and 31% higher than a traditional separated hybrid structure within the 0.3–12 Hz low-frequency range. Under a 6 Hz frequency and 0.6 mm amplitude, the output power density reaches 3.5 mW/cm³, the peak open-circuit voltage is 4.1 V, and the peak short-circuit current is 1.3 mA. Under environmental conditions of 20–88% humidity and −15–65 °C temperature, the device maintains over 94% stability in energy output. After 1.2 million vibration cycles, structural integrity remains above 96%, and energy conversion efficiency decreases by no more than 5%. The proposed coaxial hybrid structure and multi-parameter orthogonal optimization method effectively enhance energy harvesting performance in low-amplitude, low-frequency environments. The simulation design parameters and analysis procedures provide a reference for the development of similar micro hybrid energy harvesters and support the performance optimization of self-powered wireless sensor nodes. Full article

One of the major challenges in autonomous path following for unmanned surface vehicles (USVs) is the impact of stochastic environmental forces—primarily wind, waves and currents—which introduce nonlinearities that affect control models. Conventional strategies often rely on minimizing cross-track error, resulting in a reactive system that corrects heading only after a disturbance has displaced the vessel, potentially leading to oscillatory behavior and reduced precision. Deep Reinforcement Learning (DRL) is successfully used for a wide range of nonlinear control tasks. It has already been shown that robust solutions that can handle disturbances such as sensor noise or changes in system dynamics can be obtained. This study investigates whether an agent, provided it can explicitly observe disturbances, can go beyond simply correcting deviations and autonomously learn the correlation between environmental conditions and necessary counter-forces. We show that integrating the wind vector directly into the agent’s observation space allows a Proximal Policy Optimization (PPO) policy to decouple the environmental cause from the kinematic effect, facilitating drift compensation before significant errors accumulate. By systematically comparing agents trained with randomized wind scenarios, we found that agents that can observe the wind can achieve goal reaching rates of up to 99.0% and reduce the spread of path deviation and velocity in our tested scenarios. Furthermore, our results quantify a distinct Pareto frontier between navigational velocity and tracking precision, demonstrating that explicit disturbance perception improves consistency, although robust implicit training already provides substantial resilience. These findings indicate that augmenting state observations with environmental data enhances the stability of learning-based controllers. Full article

This paper evaluates a data-driven classification approach of operational wind turbine blades based on consecutive tower-radar measurements that are each compressed in a two-dimensional slow-time to range representation (radargram). Like many real-world machine learning systems, installed tower-radar systems face some key challenges: (i) transferability to new operational contexts, (ii) impediments due to evolving environmental and operational conditions (EOCs), and (iii) limited explainability of their deep neural decisions. These challenges are addressed here with a set of structured machine learning studies. The unique field data comes from a sensor box equipped with a frequency-modulated continuous wave (FMCW) radar (33.4–36 GHz frequency range). Relevant parts of the radargram that contribute to a decision of the used convolutional neural networks were identified by a class-sensitive visualization technique named GuidedGradCAM (Guided Gradient-weighted Class Activation Mapping). The following main contributions are provided to the field of tower-radar monitoring (TRM) in the context of wind energy applications: (i) every individual rotor blade holds a number of characteristic structural features revealed by the radar sensor, which can be used to discriminate rotor blades from the same turbine via neural networks; (ii) those unique features are not agnostic to changing EOCs; and (iii) pixel-level distortions reveal the necessity of low-level information for a precise rotor blade classification. Full article

To investigate the hypersonic boundary layer transition over complex three-dimensional configurations, this study conducted an experiment using infrared thermography, Rayleigh scattering visualization, and high-frequency pressure sensors in a Mach 6 Ludwieg wind tunnel. The infrared results indicate that increasing the Reynolds number promotes boundary layer transition on the model surface. Spectral analysis reveals a high-frequency peak centered at 250 kHz on the finless side of the windward surface. Comprehensive analysis indicates this represents high-frequency secondary instability triggered by the traveling crossflow mode in its nonlinear phase. On the finless side of the leeward surface, a typical Mack second-mode high-frequency instability amplification process is observed within the 140–280 kHz frequency band. Additionally, the spectrum results for the fin–cone junction became more complex. On the windward side, the primary energy concentration in the junction zone is observed between 80 and 200 kHz, with calculated wave packet velocities higher than those on the finless side. Wavelet analysis reveals that low-frequency modes are amplified first and gradually excite high-frequency components, with significant modal coupling appearing in the high-frequency region of the bicoherence. The leeward fin–cone junction exhibits dual-band characteristics at 60–120 kHz and 180–260 kHz, demonstrating stronger intermodal interactions. Both the windward and leeward surfaces of the fin show low-frequency transverse flow-like modes around 70–180 kHz. The spectral results for the windward and leeward sides are largely consistent, with only slight differences in amplitude levels and saturation positions. Full article

Reliable environmental monitoring in remote or sparsely instrumented regions is hindered by the cost, maintenance demands, and inaccessibility of dense physical sensor deployments. To address these challenges, this study introduces VSG-SGL, a unified virtual sensor generation framework that integrates Sparse Gaussian Process Regression (SGPR) and Bayesian Ridge Regression (BRR) with deep generative learning via Variational Autoencoders (VAE) and Conditional Tabular GANs (CTGAN). Real meteorological datasets from multiple South Korean cities were preprocessed using thresholding and Isolation Forest anomaly detection and evaluated using distributional alignment (KDE) and sequence-learning validation with BiLSTM and BiGRU models. Experimental findings demonstrate that VAE-augmented virtual sensors provide the most stable and reliable performance. For temperature, VAE maintains predictive errors close to those of BRR and SGPR, reflecting the already well-modeled dynamics of this variable. In contrast, humidity and wind-related variables exhibit measurable gains with VAE; for example, SGPR-based wind speed MAE improves from 0.1848 to 0.1604, while BRR-based wind direction RMSE decreases from 0.1842 to 0.1726. CTGAN augmentation, however, frequently increases error, particularly for humidity and wind speed. Overall, the results establish VAE-enhanced VSG-SGL virtual sensors as a cost-effective and accurate alternative in scenarios where physical sensing is limited or impractical. Full article

Intensive pig production generates significant emissions of ammonia (NH₃) and methane (CH₄), gases with both environmental and health impacts, primarily originating from slurry storage lagoons and their management. This study monitored a maternity pig farm over a 360 day period, using sensors located next to the slurry storage lagoon (Sensor 4) and in the immediate external surroundings of the facility, while simultaneously recording environmental variables (temperature, relative humidity, wind, and precipitation). The results showed that concentrations at the lagoon were thousands to tens of thousands of times higher than those measured in the surrounding area, with temperature and relative humidity emerging as key factors that increase volatilization and microbial generation, especially in summer under medium humidity conditions. Precipitation and wind modulate concentrations through resuspension and dispersion processes. Overall, the slurry storage lagoon constitutes the primary hotspot of emissions, and proper sensor placement is essential to accurately estimate its real impact, while integrating climatic and spatial conditions is crucial for designing and implementing effective mitigation strategies in intensive pig production systems. Full article

Accurate measurement of sea-surface winds is critical for climate science, physical oceanography, and the rapidly expanding offshore wind energy sector. Buoy-based platforms—moored meteorological buoys, drifters, and floating LiDAR systems (FLS)—provide practical alternatives to fixed offshore structures, especially in deep water where bottom-founded installations are economically prohibitive. Yet these floating platforms are subject to continuous pitch, roll, heave, and yaw motions forced by wind, waves, and currents. Such six-degree-of-freedom dynamics introduce multiple error pathways into the measured wind signal. This paper synthesizes the current understanding of motion-induced measurement errors and the techniques developed to compensate for them. We identify four principal error mechanisms: (1) geometric biases caused by sensor tilt, which can underestimate horizontal wind speed by 0.4–3.4% depending on inclination angle; (2) contamination of the measured signal by platform translational and rotational velocities; (3) artificial inflation of turbulence intensity by 15–50% due to spectral overlap between wave-frequency buoy motions and atmospheric turbulence; and (4) beam misalignment and range-gate distortion specific to scanning LiDAR systems. Compensation strategies have progressed through four recognizable stages: fundamental coordinate-transformation and velocity-subtraction algorithms developed in the 1990s; Kalman-filter-based multi-sensor fusion emerging in the 2000s; Response Amplitude Operator modeling tailored to FLS platforms in the 2010s; and data-driven machine-learning approaches under active development today. Despite this progress, key challenges persist. Sensor reliability degrades under extreme sea states precisely when accurate data are most needed. The coupling between high-frequency platform vibrations and turbulence remains poorly characterized. No unified validation framework or benchmark dataset yet exists to compare methods across platforms and environments. We conclude by outlining research priorities: end-to-end deep-learning architectures for nonlinear error correction, adaptive algorithms capable of all-sea-state operation, standardized evaluation protocols with open datasets, and tighter integration of intelligent software with next-generation low-power sensors and actively stabilized platforms. Full article

This paper presents the design, manufacturing, instrumentation and validation by tests (ground and icing wind tunnel) of a full-scale Hybrid Laminar Flow Control (HLFC) leading-edge demonstrator based on Airbus A330 outer wing plan-form. The Ground-Based Demonstrator (GBD) was developed to reproduce a full-scale, realistic wing section integrating into the leading-edge three key systems: micro-perforated skin for the hybrid laminar flow control suction system (HLFC), the hot-air Wing Ice Protection System (WIPS) and a folding “bull nose” Krueger high-lift device. The demonstrator combines a superplastic-formed and diffusion-bonded (SPF/DB) perforated titanium skin mounted on aluminum ribs jointed with a carbon-fiber-reinforced polymer (CFRP) wing box. Titanium internal ducts were designed to ensure uniform hot-air distribution and structural compatibility with composite components. Manufacturing employed advanced aeronautical processes and precision assembly under INCAS coordination. Ground tests were performed using a dedicated hot-air and vacuum rig delivering up to 200 °C and 1.6 bar, thermocouples and pressure sensors. The results confirmed uniform heating (±2 °C deviation) and stable operation of the WIPS without structural distortion. Relevant tests were performed in the CIRA Icing Wind Tunnel facility, verifying the anti-ice protection system and Krueger device. The successful design, fabrication, testing and validation of this multifunctional leading edge—featuring integrated HLFC, WIPS and Krueger systems—demonstrates the readiness of the concept for subsequent aerodynamic testing. Full article

This paper presents a systematic review conducted under the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework, analyzing 286 scientific articles focused on vibration-based predictive maintenance strategies for wind turbines within the context of advanced Prognostics and Health Management (PHM). The review combines international standards (ISO 10816, ISO 13373, and IEC 61400) with recent developments in sensing technologies, including piezoelectric accelerometers, microelectromechanical systems (MEMS), and fiber Bragg grating (FBG) sensors. Classical signal processing techniques, such as the Fast Fourier Transform (FFT) and wavelet-based methods, are identified as key preprocessing tools for feature extraction prior to the application of machine-learning-based diagnostic algorithms. Special emphasis is placed on machine learning and deep learning techniques, including Support Vector Machines (SVM), Random Forest (RF), Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and autoencoders, as well as on hybrid digital twin architectures that enable accurate Remaining Useful Life (RUL) estimation and support autonomous decision-making processes. The bibliometric and case study analysis covering the period 2020–2025 reveals a strong shift toward multisource data fusion—integrating vibration, acoustic, temperature, and Supervisory Control and Data Acquisition (SCADA) data—and the adoption of cloud-based platforms for real-time monitoring, particularly in offshore wind farms where physical accessibility is constrained. The results indicate that vibration-based predictive maintenance strategies can reduce operation and maintenance costs by more than 20%, extend component service life by up to threefold, and achieve turbine availability levels between 95% and 98%. These outcomes confirm that vibration-driven PHM frameworks represent a fundamental pillar for the development of smart, sustainable, and resilient next-generation wind energy systems. Full article

Operational expenditure in wind farms is heavily influenced by unplanned maintenance, much of which stems from undetected rotor system faults. Although many fault-detection methods have been proposed, most remain confined to laboratory test. Blade-root bending-moment measurements are among the few techniques applied in the field, yet their reliability is limited by strong sensitivity to varying operational and environmental conditions. This study presents a data-driven rotor health-monitoring framework that enhances the diagnostic value of blade bending-moments. Assuming that the wind speed profile remains approximately stationary over short intervals (e.g., 20 s), a machine-learning model is trained on bending-moment data from healthy blades to predict the incident wind-speed profile under a wide range of conditions. During operation, real-time bending-moment signals from each blade are independently processed by the trained model. A healthy rotor yields consistent wind-speed profile predictions across all three blades, whereas deviations for an individual blade indicate rotor asymmetry. In this study, the methodology is verified using high-fidelity OpenFAST simulations with controlled blade pitch misalignment as a representative fault case, providing simulation-based verification of the proposed framework. Results demonstrate that the proposed inverse-modeling and cross-blade consistency framework enables sensitive and robust detection and localization of pitch-related rotor faults. While only pitch misalignment is explicitly investigated here, the approach is inherently applicable to other rotor asymmetry mechanisms such as mass imbalance or aerodynamic degradation, supporting reliable condition monitoring and earlier maintenance interventions. Using OpenFAST simulations, the proposed framework reconstructs height-resolved wind profiles with RMSE below 0.15 m/s (R² > 0.997) under healthy conditions, and achieves up to 100% detection accuracy for moderate-to-severe pitch misalignment faults. Full article

Orchard wind-assisted spraying technology relies on auxiliary airflow to disturb the canopy and improve droplet deposition uniformity. However, there are few effective means of quantitatively assessing the dynamic response of fruit tree leaves to airflow or the changes in airflow patterns within the canopy in real time. To address this, this study proposed an online monitoring method for the aerodynamic characteristics of fruit tree leaves using strain gauge sensors. The flexible strain gauge was affixed to the midribs of leaves from peach, pear and apple trees. Leaf deformations were captured with high-speed video recording (100 fps) alongside electrical signals in controlled wind fields. Bartlett low-pass filtering and Fourier transform were used to extract frequency-domain features spanning between 0 and 50 Hz. The AdaBoost decision tree model was used to evaluate classification performance across frequency bands. The results demonstrated high accuracy in identifying wind exposure (98%) for pear leaf and classifying the three leaf types (κ = 0.98) within the 4–6 Hz band. A comparison with the frame analysis of high-speed video recordings revealed a time error of 2 s in model predictions. This study confirms that strain gauge sensors combined with machine learning could efficiently monitor fruit tree leaf responses to external airflow in real time. It provides novel insights for optimizing wind-assisted spray parameters, reconstructing internal canopy wind field distributions and achieving precise pesticide application. Full article

Sea fog is a major meteorological hazard that severely disrupts maritime transportation and economic activities in the South China Sea. As China’s next-generation geostationary meteorological satellite, Fengyun-4B (FY-4B) supplies continuous observations that are well suited for sea fog monitoring, yet a satellite-specific recognition method has been lacking. A key obstacle is the radiometric inconsistency between the Advanced Geostationary Radiation Imager (AGRI) sensors on FY-4A and FY-4B, compounded by the cessation of Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) observations, which prevents direct transfer of fog labels. To address these challenges and fill this research gap, we propose a machine learning framework that integrates cross-satellite radiometric recalibration and physical mechanism constraints for robust daytime sea fog detection. First, we innovatively apply a radiation recalibration transfer technique based on the radiative transfer model to normalize FY-4A/B radiances and, together with Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) cloud/fog classification products and ERA5 reanalysis, construct a highly consistent joint training set of FY-4A/B for the winter-spring seasons since 2019. Secondly, to enhance the model’s physical performance, we incorporate key physical parameters related to the sea fog formation process (such as temperature inversion, near-surface humidity, and wind field characteristics) as physical constraints, and combine them with multispectral channel sensitivity and the brightness temperature (BT) standard deviation that characterizes texture smoothness, resulting in an optimized 13-dimensional feature matrix. Using this, we optimize the sea fog recognition model parameters of decision tree (DT), random forest (RF), and support vector machine (SVM) with grid search and particle swarm optimization (PSO) algorithms. The validation results show that the RF model outperforms others with the highest overall classification accuracy (0.91) and probability of detection (POD, 0.81) that surpasses prior FY-4A-based work for the South China Sea (POD 0.71–0.76). More importantly, this study demonstrates that the proposed FY-4B framework provides reliable technical support for operational, continuous sea fog monitoring over the South China Sea. Full article