Search Results (310)

The detection of proteins plays a key role in disease diagnosis and drug development. For this, we numerically investigated a novel microfluidic motor actuated by an induced-charge electro-osmotic (ICEO) whirling flow. An alternating current–flow field effect transistor is engineered to modulate the profiles of ICEO streaming to stimulate and adjust the whirling flow in the circle microfluidic chamber. Based on this, we studied the distribution of an ICEO whirling flow in the detection chamber by tuning the fixed potential on the gate electrodes by the simulations. Then, we established a fluid–structure interaction model to explore the influence of blade structure parameters on the rotation performance of microfluidic motors. In addition, we investigated the rotation dependence of microfluidic motors on the potential drop between two driving electrodes and fixed potential on the gate electrodes. Next, we numerically explored the capability of these microfluidic motors for the detection of low-abundance proteins. Finally, we studied the regulating effect of potential drops between the driving electrodes on the detection performance of microfluidic motors by numerical simulations. Microfluidic motors actuated by an ICEO whirling flow hold good potential in environmental monitoring and disease diagnosis for the outstanding advantages of flexible controllability, a simple structure, and gentle work condition. Full article

Amid the global shift toward clean energy, wind power has emerged as a critical pillar of the modern energy matrix. To improve the reliability and maintainability of wind farms, this work proposes a novel hybrid fault detection approach that combines expert-driven diagnostic knowledge with data-driven modeling. The framework integrates autoencoder-based neural networks with Failure Mode and Symptoms Analysis, leveraging the strengths of both methodologies to enhance anomaly detection, feature selection, and fault localization. The methodology comprises five main stages: (i) the identification of failure modes and their observable symptoms using FMSA, (ii) the acquisition and preprocessing of SCADA monitoring data, (iii) the development of dedicated autoencoder models trained exclusively on healthy operational data, (iv) the implementation of an anomaly detection strategy based on the reconstruction error and a persistence-based rule to reduce false positives, and (v) evaluation using performance metrics. The approach adopts a fault-specific modeling strategy, in which each turbine and failure mode is associated with a customized autoencoder. The methodology was first validated using OpenFAST 3.5 simulated data with induced faults comprising normal conditions and a 1% mass imbalance fault on a blade, enabling the verification of its effectiveness under controlled conditions. Subsequently, the methodology was applied to a real-world SCADA data case study from wind turbines operated by EDP, employing historical operational data from turbines, including thermal measurements and operational variables such as wind speed and generated power. The proposed system achieved 99% classification accuracy on simulated data detect anomalies up to 60 days before reported failures in real operational conditions, successfully identifying degradations in components such as the transformer, gearbox, generator, and hydraulic group. The integration of FMSA improves feature selection and fault localization, enhancing both the interpretability and precision of the detection system. This hybrid approach demonstrates the potential to support predictive maintenance in complex industrial environments. Full article

The wind turbine blade is subject to multi-source impacts, such as bird strikes, lightning strikes, and hail, throughout its extended service. Accurate localization of those impact sources is a key technical link in structural health monitoring of the wind turbine blade. In this paper, a single-sensor impact source localization method is proposed. Capitalizing on deep learning frameworks, this method innovatively transforms the impact source localization problem into a classification task, thereby eliminating the need for anisotropy compensation and correction required by conventional localization algorithms. Furthermore, it leverages the inherent coding effects of the blade’s material and geometric anisotropy on impact sources originating from different positions, enabling localization using only a single sensor. Experimental results show that the method has a high localization accuracy of 96.9% under single-sensor conditions, which significantly reduces the cost compared to the traditional multi-sensor array scheme. This study provides a cost-effective solution for real-time detection of wind turbine blade impact events. Full article

Ice formation on critical infrastructure such as wind turbine blades can lead to severe performance degradation and safety hazards. This study investigates the use of hyperspectral imaging (HSI) combined with machine learning to detect and classify ice on various coated and uncoated surfaces. Hyperspectral reflectance data were acquired using a push-broom HSI system under controlled laboratory conditions, with ice and rime ice generated using a thermoelectric cooling setup. Support Vector Machine (SVM) and Random Forest (RF) classifiers were trained on uncoated aluminum samples and evaluated on surfaces with different coatings to assess model generalization. Both models achieved high classification accuracy, though performance declined on black-coated surfaces due to increased absorbance by the coating. The study further examined the impact of spectral band reduction to simulate different sensor types (e.g., NIR vs. SWIR), revealing that model performance is sensitive to wavelength range, with SVM performing optimally in a reduced band set and RF benefiting from the full spectral range. A multiclass classification approach using RF successfully distinguished between glaze and rime ice, offering insights into more targeted mitigation strategies. The results confirm the potential of HSI and machine learning as robust tools for surface ice monitoring in safety-critical environments. Full article

The quality of machined marine propellers plays a critical role in underwater propulsion performance. Precision casting is the predominant manufacturing technique; however, deformation of wax models and rough blanks during manufacturing frequently cause deviations in the dimensions of final products and, thus, affect propellers’ performance and service life. Current inspection methods primarily involve using coordinate measuring machines and sampling. This approach is time-consuming, has high labor costs, and cannot monitor manufacturing quality in real-time. This study developed a real-time online automated measurement system containing a high-resolution CITIZEN displacement sensor, a four-degree-of-freedom measurement platform, and programmable logic controller-based motion control technology to enable rapid, automated measurement of blade deformation across the wax model, rough blank, and final product processing stages. The measurement data are transmitted in real time to a cloud database. Tests conducted on a standardized platform and real propeller blades confirmed that the system consistently achieved measurement accuracy to the second decimal place under the continual measurement mode. The system also demonstrated excellent repeatability and stability. Furthermore, the continuous measurement mode outperformed the single-point measurement mode. Overall, the developed system effectively reduces labor requirements, shortens measurement times, and enables real-time monitoring of process variation. These capabilities underscore its strong potential for application in the smart manufacturing and quality control of marine propellers. Full article

Given the exceptional capabilities of femtosecond laser processing in achieving high-precision ablation for air-film cooling hole fabrication on turbine blades, it is imperative to develop an advanced monitoring methodology that enables real-time feedback control to automatically terminate the laser upon complete penetration detection, thereby effectively preventing backside damage. To tackle this issue, a spectrum-domain coherent imaging technique has been developed. This innovative approach adapts the fundamental principle of fiber-based Michelson interferometry by integrating the air-film hole into a sample arm configuration. A broadband super-luminescent diode with a 830 nm central wavelength and a 26 nm spectral bandwidth serves as the coherence-optimized illumination source. An optimal normalized reflectivity of 0.2 is established to maintain stable interference fringe visibility throughout the drilling process. The system achieves a depth resolution of 11.7 μm through Fourier transform analysis of dynamic interference patterns. With customized optical path design specifically engineered for through-hole-drilling applications, the technique demonstrates exceptional sensitivity, maintaining detection capability even under ultralow reflectivity conditions (0.001%) at the hole bottom. Plasma generation during laser processing is investigated, with plasma density measurements providing optical thickness data for real-time compensation of depth measurement deviations. The demonstrated system represents an advancement in non-destructive in-process monitoring for high-precision laser machining applications. Full article

This study presents a comparative analysis of the water droplet erosion resistance of three compressor wheels coated with Ni-P and Si-P layers. The tests were conducted using a custom-developed experimental apparatus in accordance with the ASTM G73-10 standard. The degree of erosion was monitored through continuous precision mass measurements, and structural changes on the surfaces of both the base materials and the coatings were examined using a Zeiss Crossbeam 350 scanning electron microscope (SEM). Hardness values were determined using a Vickers KB 30 hardness tester, while the chemical composition was analysed using a WAS Foundry Master optical emission spectrometer. Significant differences in erosion resistance were observed among the various compressor wheels, which can be attributed to differences in coating hardness values, as well as to the detachment of the Ni-P layer from the base material under continuous erosion. In all cases, water droplet erosion led to a reduction in the isentropic efficiency of the compressor—measured using a hot gas turbocharger testbench—with the extent of efficiency loss depending upon the type of coating applied. Although blade protection technologies for turbocharger compressor impellers used in the automotive industry have been the subject of only a limited number of studies, modern technologies, such as the application of certain alternative fuels and exhaust gas recirculation, have increased water droplet formation, thereby accelerating the erosion rate of the impeller. The aim of this study is to evaluate the resistance of three different coating layers to water droplet erosion through standardized tests conducted using a custom-designed experimental apparatus. Full article

The attenuation of bolt preload is a critical factor leading to bolt fatigue failure, whereas the study of the nonlinear attenuation behavior of preload and its mechanism during installation is an inevitable challenge in engineering practice. The attenuation of the preload of a bolt is mainly related to the stiffness of the bolt body as well as the stiffness of the connected parts. This study aimed to develop an experimental system to analyze the nonlinear attenuation behavior of preload during bolt tightening. First, a simulation system replicating the bolt installation process was constructed in a laboratory setting, incorporating blade and pitch bearing specimens identical to those used in a 10 MW wind turbine, restoring the stiffness coupling characteristics of the “composite-metal bearing” heterogeneous interface at the blade root through a 1:1 full-scale simulation system for the first time. Second, ultrasonic preload measurement equipment was employed to monitor preload variations during the bolt tightening process. Finally, the instantaneous preload decay rate of the wind turbine blade-root bolts and the over-draw coefficient were quantified. Experiments have shown that the preload decay rate of commonly used M36 leaf root bolts is 11–16%. If a more precise value is required, each bolt needs to be calibrated. These findings provide valuable insights for optimizing bolt installation procedures, enabling precise preload control to mitigate fatigue failures caused by abnormal preload attenuation. Full article

Background/Objectives: Advancements in neuronavigation and intraoperative imaging have made gross-total resection of deep-seated lesions more feasible. However, in eloquently located regions, brain shift can lead to unintentional damage of functionally critical tissue during the approach. This study analyzes the feasibility and outcomes of a stereotactically guided microsurgical approach supported by intraoperative CT (iCT) for such lesions. Methods: Patients with deep-seated, eloquently located lesions treated between 03/2017 and 04/2023 at the Department of Neurosurgery, Ludwig-Maximilians-University (LMU) Munich, Germany, were included. Frame-based, image-guided stereotaxy was used for trajectory planning and catheter placement, verified by iCT. Microsurgical resection was conducted along the catheter trajectory using 2 mm conical blade retractors and continuous neurophysiological monitoring. Postoperative MRI assessed the extent of resection. Neurological outcomes were evaluated postoperatively, at 6 weeks, and at long-term follow-up in 12/2023. Results: A total of 12 patients were treated using the stereotactically guided microsurgical approach described in this study. In all cases, the implanted catheter precisely matched the preoperative trajectory, as confirmed by fused iCT data. Median durations were 23 min for stereotaxy and 3 h 7 min for microsurgery. Complete resection was achieved in all cases. One patient experienced transient hemiparesis and aphasia, both of which were fully resolved. All other patients showed neurological improvement or remained seizure-free at long-term follow-up. Conclusions: In selected cases, a stereotactically guided microsurgical approach with iCT enabled intraoperative localization of the target with high spatial accuracy and without immediate procedure-related complications in this limited cohort. Our findings support the feasibility of the technique; however, conclusions regarding clinical efficacy or broader applicability are limited by the small sample size and non-comparative study design. Full article

With the widespread application of FRP (Fiber Reinforced Plastics) materials in fields such as wind turbine blades and ships, the safety performance of these materials during their service life has garnered signification attention. This study employs the fiber Bragg grating (FBG) sensor to monitor damage of the FRP materials. An FRP plate embedded with six FBGs was fabricated, and different degrees of damage were induced in the FRP plate. The six FBGs measured the damage information of the FRP plate under impulse and continuous sinusoidal vibration loads. The results demonstrate that both the strain information and the frequency shift information measured by the FBG sensors can effectively and sensitively identify damage in the FRP plate. Full article

Blades are widely used in the engines of aerospace vehicles, fans of near-space aerostat, and other equipment, and they are the key to completing energy conversion and pressure adjustment of the capsule. Blade tip timing (BTT) is the most cost-efficient approach for the monitoring of blades. The reliability and validity of BTT is mainly investigated through numerical simulation and experimental verification. However, not all researchers are able to carry out the expensive and time-consuming task of rotating the blade test bench and its monitoring systems. Therefore, a good and easily understood simulator is necessary. In this paper, an effective BTT simulation model that is capable of considering various uncertainties such as installation errors, probe accuracy, sampling clock frequency, speed fluctuations, and mistuning is presented. A blade multi-harmonic vibration model is also presented, which is not only easy to implement but also simplifies the solution of dynamic equations. Also, the simulation results show that the proposed model is accurate and consistent with the experimental results. This will help researchers to achieve an improved understanding of BTT and form the basis for conducting research in related areas in a short period of time. Full article

This paper reports a convolutional neural network (CNN)-based damage detection approach for radar-based structural health monitoring of wind turbine blades. Subsequent radar measurements are transformed into an image-type representation for use as CNN input. In contrast to conventional approaches that require compensation for temperature and loading effects, the proposed framework inherently learns all required information during the training phase. Its damage detection performance (i.e., detecting intact vs. damaged condition) is demonstrated using measurements from multiple embedded radar sensors during fatigue testing of a wind turbine blade with a length of 31 m. The achieved F1-score for correct damage classification is between 91% and 100% for both the unloaded and the loaded blade. Full article

Real-time non-intrusive monitoring of wind turbines, blades, and defect surfaces poses a set of complex challenges related to accuracy, safety, cost, and computational efficiency. This work introduces an enhanced deep learning-based framework for real-time detection of wind turbine blade defects. The WindDefNet is introduced, which features the Inception-ResNet modules, Visual Transformer (ViT), and multi-scale attention mechanisms. WindDefNet utilizes modified cross-convolutional blocks, including the powerful Inception-ResNet hybrid, to capture both fine-grained and high-level features from input images. A multi-scale attention module is added to focus on important regions in the image, improving detection accuracy, especially in challenging areas of the wind turbine blades. We employ pertaining to Inception-ResNet and ViT patch embedding architectures to achieve superior performance in defect classification. WindDefNet’s capability to capture and integrate multi-scale feature representations enhances its effectiveness for robust wind turbine condition monitoring, thereby reducing operational downtime and minimizing maintenance costs. Our model WindDefNet integrates a novel advanced attention mechanism, with custom-pretrained Inception-ResNet combining self-attention with a Visual Transformer encoder, to enhance feature extraction and improve model accuracy. The proposed method demonstrates significant improvements in classification performance, as evidenced by the evaluation metrics attain precision, recall, and F1-scores of 0.88, 1.00, and 0.93 for the damage, 1.00, 0.71, and 0.83 for the edge, and 1.00, 1.00, and 1.00 for both the erosion and normal surfaces. The macro-average and weighted-average F1 scores stand at 0.94, highlighting the robustness of our approach. These results underscore the potential of the proposed model for defect detection in industrial applications. Full article

An inductive clearance measurement sensor has advantages of good anti-interference, fast response speed, and high sensitivity, and it has obvious technical potential in aeroengine turbine tip clearance measurement. In this paper, a rotor dynamic tip clearance measurement experiment system was designed based on a high-resolution inductive measurement system. The high temperature calibration experiment, performance verification experiment, and dynamic clearance measurement experiment under varying operating conditions were used to verify the high-temperature dynamic measurement performance of the measurement system. The resolution was used as the evaluation parameter of measurement performance. The experimental result shows the system has good resolution and dynamic response at 1000 °C, and the dynamic resolution reaches 10 μm in the 3 mm measuring range. The varying condition experiment results show that the blade deformation caused by the speed change of 1000–3000 r/min and the temperature change of 600–1000 °C can be resolved, and the resolution reaches about 10 μm. The research results verify that the inductance clearance measurement system has the characteristics of high temperature resistance and high resolution, and the technical specifications of clearance detection meet the basic requirements of dynamic clearance measurement of turbine tips, which provides an effective detection method for aeroengine health monitoring. Full article

Aiming to solve the problems of low precision and poor efficiency caused by relying on manual experience during the manual polishing of blades, a multi-view spark image detection method based on YOLOv8 transfer learning is proposed. A multi-pose spark image dataset including front, side, and 45° angle views is constructed, and the cross-view detection task is achieved for the first time. The generalization ability of the model is enhanced through the following innovative strategies: (1) a cross-view transfer learning framework based on dynamic anchor box optimization is designed, and the parameters of the front spark detection model YOLOv8 are transferred to the side and 45°-angle detection tasks; (2) an attention-guided feature alignment module is introduced to alleviate the feature distribution shift caused by view differences; and (3) a curriculum learning strategy is adopted, where the datasets of different views are trained separately first and then sampled to reconstruct the dataset for further training, gradually increasing the weight of samples from complex views. The experimental results show that on the self-built multi-view dataset (containing 3000 annotated images), this method achieves an average detection accuracy of 98.7%, which is 14.2% higher than that of the original YOLOv8 model. The inference speed reaches 55 FPS on an NVIDIA RTX 4090, meeting the requirements of industrial online monitoring. The research results provide key technical support for the intelligent prediction of the material removal rate in the precision machining of blades and have the potential for rapid deployment in industrial scenarios. Full article