Search Results (44)

Sand production in an offshore hydrocarbon wells poses significant operational and integrity challenges, particularly in deviated wells, where complex flow geometries intensify particle transport and erosion risks. The traditional sand-monitoring method utilizes stationary acoustic sensors attached to the production flowline at the surface. However, these sensors provide limited spatial coverage and intermittent measurements, restricting their ability to detect early sanding onset or precisely localize sanding intervals. By combining with vertical seismic profiling (VSP), Distributed Acoustic Sensing (DAS) delivers continuous, high-density data along the entire length of the wellbore and is increasingly recognized as a powerful diagnostic tool for real-time downhole monitoring. This study presents a field application of DAS-VSP for detecting and characterizing sand transport in a deviated offshore production well equipped with 350 distributed fiber-optic channels spanning 0–1983 m true vertical depth (TVD) at 8 m spacing. A multistage workflow was developed, including SEGY ingestion and shot merging, channel and time window selection, trace normalization, and low-pass filtering below 20 Hz. Multi-domain signal analysis, such as RMS energy, spike-based time-domain attributes, FFT, PSD spectral characterization, and time–frequency decomposition, were used to isolate the characteristic im-pulsive low-frequency (<20 Hz) signatures associated with sand impact. An adaptive thresholding and event-clustering scheme was then applied to discriminate sanding bursts from background noise and integrate their acoustic energy over depth. The processed DAS section revealed distinct, depth-localized sand ingress zones within the production interval (1136–1909 m TVD). The derived sand log provided a quantitative measure of sand intensity variations along the deviated wellbore, with normalized RMS amplitudes ranging from 0.039 to 1.000 a.u., a mean value of 0.235 a.u., and 137 analyzed channels within the production interval. These results indicate that sand production is highly clustered within discrete depth intervals, offering new insights into sand–fluid interactions during steady-state flow. Overall, the findings confirm that DAS-VSP enables continuous real-time monitoring of the sanding behavior with a far greater depth resolution than conventional tools. This approach supports proactive sand management strategies, enhances well-integrity decision-making, and underscores the potential of DAS to evolve into a standard surveillance technology for hydrocarbon production wells. Full article

The combustion diagnostics of rotating detonation engines (RDE) based on excited-state hydroxyl radical (OH*) chemiluminescence imaging is an important method used to characterize combustion flow fields. Overcoming the limitations of imaging devices to achieve nanosecond-scale temporal resolution is crucial for observing the propagation of high-frequency detonation waves. In this work, a nanosecond-scale imaging system with an ultra-high spatiotemporal resolution was designed and constructed. The system employs four near ultraviolet (NUV)-visible ICMOS, equipped with a high-gain, dual-microchannel plate (MCP) architecture fabricated using a new atomic layer deposition (ALD) process. The system has a maximum electronic gain of 10⁷, a minimum integration time of 3 ns, a minimum interval time 4 ns, and an imaging resolution of 1608 × 1104 pixels. Using this system, high-spatiotemporal-resolution visualization experiments were conducted on RDE, fueled by H₂–oxygen-enriched air and NH₃–H₂–oxygen-enriched air. The results enable the observation of the detonation wave structure, the cellular structure, and the propagation velocity. In combination with optical flow analysis, the images reveal vortex structures embedded within the cellular structure. For NH₃-H₂ mixed fuel, the results indicate that detonation wave propagation is more unstable than in H₂ combustion, with a larger bright gray area covering both the detonation wave and the product region. The experimental results demonstrate that high spatiotemporal OH* imaging enables non-contact, full-field measurements, providing valuable data for elucidating RDE combustion mechanisms, guiding model design, and supporting NH₃ combustion applications. Full article

To meet the demand for online monitoring of acetylene (C₂H₂) in transformer oil, a high-sensitivity detection system based on photoacoustic spectroscopy (PAS) is presented. The system integrates custom-designed modules for signal acquisition, phase-sensitive detection, and data processing, centered around a high-performance microcontroller. A full-wave lock-in amplification-based phase-sensitive detection circuit enables precise extraction of nV-level photoacoustic signals. Finite element simulations of the resonant photoacoustic cell in COMSOL 6.2 were conducted to optimize the structural configuration and sensor placement, achieving maximum acoustic response. Calibration experiments confirmed excellent system performance, demonstrating a linear response (R² > 0.99) over the 0.5–20 ppm range and a practical detection limit of 0.1 ppm. Comparative evaluations against conventional dissolved gas analysis (DGA) equipment verify the system’s sensitivity, stability, and temporal resolution, demonstrating its potential as a high-sensitivity and reliable solution for transformer fault gas diagnostics. Full article

An operational test and degradation analysis of a hydraulic fluid based on synthetic esters was performed in three types of work machines. To enhance its performance, ZDDP anti-wear agents were added. Hydraulic fluids are susceptible to degradation by oxidation; therefore, to ensure the long service life of the equipment, it is essential to monitor their current condition through laboratory analyses during machine operation. Emission spectrometry was used to determine the presence of contaminants and the concentration of additive substances in the oil. Pollution was assessed by cleanliness code analysis according to ISO 4406-2021, alongside Total Acid Number (TAN) analysis and LNF analysis of wear and contamination in lubricants. The combination of cleanliness code analysis and LNF analysis of particle type and origin allows for monitoring not only the count but also the origin of contaminating metallic particles, which increases the probability of correct diagnostics and successful detection and resolution of wear problems. All three machines were still operational at the end of the test interval, meaning the tested hydraulic fluid is a suitable alternative to mineral variants. However, in all three pieces of equipment, it is necessary to replace the hydraulic fluid and flush the system before further operation. Furthermore, we recommend replacing the filter elements and inspecting the internal spaces of rotating parts with an increased potential for wear. From the oil’s perspective, it is advisable to add more anti-wear additives (ZDDP), which are depleted the fastest. Full article

This paper focuses on the vibration analysis of a prototype helicopter rotor test stand, with particular attention to the dynamic response of its electric propulsion system. The stand is driven by an induction motor and equipped with composite rotor blades of various geometries, including blades with shape memory alloy (SMA)-based torsion actuators for angle of attack (AoA) adjustment. These variable geometries significantly influence the system’s dynamic behavior, where resonance phenomena may pose risks to structural integrity. The objective was to investigate how selected operational parameters specifically motor speed and AoA affect the vibration response of the propulsion system. Structural vibrations were measured using a tri-axial piezoelectric accelerometer system integrated with calibrated signal conditioning and high-resolution data acquisition modules. This setup enabled precise, time-synchronized recording of dynamic responses along all three axes. Fast Fourier Transform (FFT) and Power Spectral Density (PSD) methods were applied to identify dominant frequency components, including those associated with rotor harmonics and SMA activation. The highest vibration amplitudes were observed at an AoA of 16°, but all results remained within the vibration limits defined by MIL-STD-810H for rotorcraft drive systems. The study confirms the importance of sensor-based diagnostics in evaluating electromechanical propulsion systems operating under dynamic loading conditions. Full article

The study investigates the electrical energy parameters in the distribution system of a mining facility located in Murmansk Oblast, Russia, focusing on power quality (PQ) issues arising substantially from mine hoist operation conditions. Despite compliance with Russian standards related to PQ, discrepancies were observed between PQ measurement results and problems inherent in the system, such as transformer failures. The research employed two instruments, Resurs-UF2M and Metrel MI2892, to conduct a PQ survey, comparing their data aggregation methods and measurement accuracy. Various data aggregation intervals were also used to evaluate the impact of resolution on PQ assessment. Results revealed significant discrepancies between the instruments, with Metrel MI2892 providing a more reliable and detailed dataset, while Resurs-UF2M failed to capture rapid transients and enable profound PQ analysis to be performed. The research identified eight PQ indices exceeding permissible levels, attributed to the electromagnetic influence of high-power mining equipment. The findings underscore the limitations of current regulatory frameworks and measurement methods, emphasizing the need for revised standards to improve diagnostic accuracy. The research highlights the importance of proper instrument selection and configuration to mitigate PQ disturbances, prevent equipment failures, and enhance power system reliability in mining facilities. Full article

The integration of unmanned aerial vehicles (UAVs) and deep learning (DL) has significantly advanced crop disease detection by enabling scalable, high-resolution, and near real-time monitoring within precision agriculture. This systematic review analyzes peer-reviewed literature indexed in the Web of Science Core Collection as articles or proceeding papers through 2024. The main selection criterion was combining “unmanned aerial vehicle*” OR “UAV” OR “drone” with “deep learning”, “agriculture” and “leaf disease” OR “crop disease”. Results show a marked surge in publications after 2019, with China, the United States, and India leading research contributions. Multirotor UAVs equipped with RGB sensors are predominantly used due to their affordability and spatial resolution, while hyperspectral imaging is gaining traction for its enhanced spectral diagnostic capability. Convolutional neural networks (CNNs), along with emerging transformer-based and hybrid models, demonstrate high detection performance, often achieving F1-scores above 95%. However, critical challenges persist, including limited annotated datasets for rare diseases, high computational costs of hyperspectral data processing, and the absence of standardized evaluation frameworks. Addressing these issues will require the development of lightweight DL architectures optimized for edge computing, improved multimodal data fusion techniques, and the creation of publicly available, annotated benchmark datasets. Advancements in these areas are vital for translating current research into practical, scalable solutions that support sustainable and data-driven agricultural practices worldwide. Full article

In pharmaceutical manufacturing, the reliability of machines and utility assets is critical to ensuring product quality, regulatory compliance, and uninterrupted operations. Traditional Risk-Based Maintenance (RBM) models quantify asset criticality using the Risk Priority Number (RPN), calculated from the probability and impact of failure alongside detectability. However, these models often neglect the practical challenges involved in diagnosing and resolving equipment issues, particularly in GMP-regulated environments. This study proposes an enhanced RPN framework that replaces the conventional detectability component with Maintainability Complexity (MC), quantified through two practical indicators: Ease of Diagnosis (ED) and Ease of Resolution (ER). Thirteen Key Performance Indicators (KPIs) were developed to assess Probability, Impact, and MC across 185 pharmaceutical utility assets. To enable objective risk stratification, Jenks Natural Breaks Optimization was applied to group assets into Low, Medium, and High risk tiers. Both multiplicative and normalized averaging methods were tested for score aggregation, allowing comparative analysis of their impact on prioritization outcomes. The enhanced model produced stronger alignment with operational realities, enabling more accurate asset classification and maintenance scheduling. A 3D risk matrix was introduced to translate scores into proactive strategies, offering traceability and digital compatibility with Computerized Maintenance Management Systems (CMMS). This framework provides a practical, auditable, and scalable approach to maintenance planning, supporting Industry 4.0 readiness in pharmaceutical operations. Full article

New technologies and equipment for medicine analysis and diagnostics have always been critical in clinical medication and pharmaceutical production. Especially in the field of traditional Chinese medicine (TCM) where the chemical composition is not fully clear, cross-device analysis and identification using the same technology can sometimes even lead to misjudgments. Akebia species, capable of inducing heat clearing, diuresis, and anti-inflammatory effects, show great potential in clinical applications. However, the three commonly used species differ in pharmacological effects and therefore should not be used interchangeably. We proposed a method combining LIBS with random forest for species identification and established a modeling and verification scheme across device platforms. Spectra of three Akebia species were collected using two LIBS systems equipped with spectrometers of different resolutions. The data acquired from the low-resolution spectrometer were used for model training, while the data from the high-resolution spectrometers were used for testing. A spectral correction and feature selection (SCFS) method was proposed, in which spectral data were first corrected using a standard lamp, followed by feature selection via analysis of variance (ANOVA) to determine the optimal number of discriminative features. The highest classification accuracy of 80.61% was achieved when 28 features were used. Finally, a post-processing (PP) strategy was applied, where abnormal spectra in the test set were removed using density-based spatial clustering of applications with noise (DBSCAN), resulting in a final classification accuracy of 85.50%. These results demonstrate that the proposed “SCFS-PP” framework effectively enhances the reliability of cross-instrument data utilization and expands the applicability of LIBS in the field of TCM. Full article

Modernizing irrigation systems (ISs) from traditional gravity methods to sprinkler and drip technologies has significantly improved water use efficiency. However, it has simultaneously increased electricity demand and operational costs. Integrating photovoltaic generators into ISs represents a promising solution, as solar energy availability typically aligns with peak irrigation periods. Despite this potential, photovoltaic pumping systems (PVPSs) often face reliability issues due to fluctuations in solar irradiance, resulting in frequent start/stop cycles and premature equipment wear. The IEC 62253 standard establishes procedures for evaluating PVPS performance but primarily addresses steady-state conditions, neglecting transient regimes. As the main contribution, the current paper proposes a non-intrusive, high-resolution monitoring system and a methodology to assess the performance of an isolated, high-power PVPS, considering also transient regimes. The system records critical electrical, hydraulic and environmental parameters every second, enabling in-depth analysis under various weather conditions. Two performance indicators, pumped volume efficiency and equivalent operating time, were used to evaluate the system’s performance. The results indicate that near-optimal performance is only achievable under clear sky conditions. Under the appearance of clouds, control strategies designed to protect the system reduce overall efficiency. The proposed methodology enables detailed performance diagnostics and supports the development of more robust PVPSs. Full article

Vitamin A deficiency (VAD) remains a significant cause of preventable blindness worldwide, with ocular surface changes representing early manifestations that require prompt recognition and treatment. Conventional examination methods are capable of detecting advanced changes; however, subtle conjunctival abnormalities may be overlooked, potentially delaying the administration of appropriate interventions. We herein present the case of a 5-year-old Japanese boy with severe VAD due to selective eating patterns. This case demonstrates the utility of infrared photography as a novel diagnostic approach for detecting and monitoring conjunctival surface abnormalities. The patient exhibited symptoms including corneal ulcers, night blindness, and reduced visual acuity. Furthermore, blood tests revealed undetectable levels of vitamin A (5 IU/dL), despite relatively normal physical growth parameters. Conventional slit-lamp examination revealed characteristic sandpaper-like conjunctival changes. However, infrared photography (700–900 nm wavelength) revealed distinct abnormal patterns of conjunctival surface folds and keratinization that were not fully appreciated on a routine examination. Following high-dose vitamin A supplementation (4000 IU/day), complete resolution of ocular abnormalities was achieved within 2 months, with infrared imaging objectively documenting treatment response and normalization of conjunctival surface patterns. This case underscores the potential for severe VAD in developed countries, particularly in the context of dietary restrictions, thereby underscoring the significance of a comprehensive dietary history and a meticulous ocular examination. Infrared photography provides a number of advantages, including the capacity for non-invasive assessment, enhanced visualization of subtle changes, objective monitoring of treatment response, and cost-effectiveness due to the use of readily available equipment. This technique represents an underutilized diagnostic modality with particular promise for screening programs and clinical monitoring of VAD-related ocular manifestations, potentially preventing irreversible visual loss through early detection and intervention. Full article

Rolling bearings are widely used in rotating machinery, and their health status is crucial for the safe operation of the equipment. The research on relevant fault diagnosis algorithms is a hot topic in the field. As a leading deep learning paradigm, Convolutional Neural Networks (CNNs) have demonstrated remarkable effectiveness in bearing fault diagnosis. However, conventional CNNs encounter significant limitations in accurately identifying and classifying early-stage bearing faults, primarily due to two challenges: (1) the diagnostic accuracy is highly susceptible to variations in the input signal length and segmentation strategies and (2) incipient faults are characterized by extremely low signal-to-noise ratios (SNRs), which obscure fault signatures. To address these challenges, we propose a Waveform Intersection-CNN (WI-CNN)-based intelligent diagnosis method for early faults. This approach integrates Gramian Angular Field theory to construct high-resolution fault signatures, enabling the CNN-based diagnosis of incipient bearing faults. Validation using the Case Western Reserve University dataset demonstrates an average diagnostic accuracy exceeding 98%. Furthermore, we established a custom test platform to develop a hybrid diagnosis strategy for 10 distinct fault types. Comparative studies against two conventional CNN diagnostic methods confirm that our approach delivers superior diagnostic precision, a faster iteration speed, and enhanced algorithmic robustness. The empirical findings demonstrate that the model achieves an accuracy of 99.67% during training and 98.167% in the testing phase. Crucially, the proposed method offers exceptional simplicity, computational efficiency, and practical applicability, facilitating its widespread implementation. Full article

Background/Objectives: Cerebral palsy (CP) is a neurodevelopmental disorder associated with sleep disturbances, particularly sleep-disordered breathing (SDB), and is often linked to an increased risk of obstructive sleep apnea (OSA). OSA is underdiagnosed in this population due to the lack of standardized methods and limited access to appropriate diagnostic technologies and appropriate equipment. Thus, this study aimed to investigate the presence and severity of sleep disorders, with a particular focus on OSA, in children and adolescents with CP compared to their typically developing peers. Methods: This observational, clinical, and prospective study included 28 children and adolescents with CP and 32 age- and sex-matched typically developing individuals. Sleep disturbances were assessed using the Sleep Disturbance Scale for Children (SDSC) and a high-resolution oximeter plus actigraphy combined with a cloud-based algorithm for the detection of obstructive sleep apnea (Biologix^® system), which provided data on oxygen saturation, snoring, movement during sleep, and total sleep time. Results: According to the SDSC, 92% of children and adolescents with CP presented scores indicative of sleep disturbances, compared to 31% of typically developing individuals. SDB was the most prevalent subtype (64%) and overnight oximetry revealed that 100% of the CP group presented oxygen desaturation index (ODI) values consistent with a diagnosis of OSA. The CP group also exhibited significantly lower mean SpO₂, longer snoring duration, shorter total sleep time, and prolonged sleep latency compared to the typically developing group. Conclusions: Children and adolescents with cerebral palsy (CP) exhibit a high prevalence of sleep disturbances, with increasing evidence indicating a significant occurrence of sleep-disordered breathing (SDB), particularly obstructive sleep apnea (OSA). Full article

The increasing global demand for resilient, sustainable agricultural systems has intensified the need for advanced monitoring strategies, particularly for climate-adaptive crops such as Moringa oleifera Lam. This study presents an integrated approach using Unmanned Aerial Vehicles (UAVs) equipped with multispectral and thermal cameras to monitor the vegetative performance and determine the optimal harvest period of four M. oleifera genotypes in a Mediterranean environment. High-resolution data were collected and processed to generate the NDVI, canopy temperature, and height maps, enabling the assessment of plant vigor, stress conditions, and spatial canopy structure. NDVI analysis revealed robust vegetative growth (0.7–0.9), with optimal harvest timing identified on 30 October 2024, when the mean NDVI exceeded 0.85. Thermal imaging effectively discriminated plant crowns from surrounding weeds by capturing cooler canopy zones due to active transpiration. A clear inverse correlation between NDVI and Land Surface Temperature (LST) was observed, reinforcing its relevance for stress diagnostics and environmental monitoring. The results underscore the value of UAV-based multi-sensor systems for precision agriculture, offering scalable tools for phenotyping, harvest optimization, and sustainable management of medicinal and aromatic crops in semiarid regions. Moreover, in this study, to produce M. oleifera leaf powder intended for use as a food ingredient, the leaves of four M. oleifera genotypes were dried, milled, and evaluated for their hygiene and safety characteristics. Plate count analyses confirmed the absence of pathogenic bacterial colonies in the M. oleifera leaf powders, highlighting their potential application as natural and functional additives in food production. Full article

This study aims to mitigate slope-collapse hazards that threaten life and property at the Lujiawan resettlement site in Wanbi Town, Dayao County, Yunnan Province, within the Guanyinyan hydropower reservoir. It integrates centimeter-level point-cloud data collected by a DJI Matrice 350 RTK equipped with a Zenmuse L2 airborne LiDAR (Light Detection And Ranging) sensor with detailed structural-joint survey data. First, qualitative structural interpretation is conducted with stereographic projection. Next, safety factors are quantified using the limit-equilibrium method, establishing a dual qualitative–quantitative diagnostic framework. This framework delineates six hazardous rock zones (WY1–WY6), dominated by toppling and free-fall failure modes, and evaluates their stability under combined rainfall infiltration, seismic loading, and ambient conditions. Subsequently, six-degree-of-freedom Monte Carlo simulations incorporating realistic three-dimensional terrain and block geometry are performed in RAMMS::ROCKFALL (Rapid Mass Movements Simulation—Rockfall). The resulting spatial patterns of rockfall velocity, kinetic energy, and rebound height elucidate their evolution coupled with slope height, surface morphology, and block shape. Results show peak velocities ranging from 20 to 42 m s⁻¹ and maximum kinetic energies between 0.16 and 1.4 MJ. Most rockfall trajectories terminate within 0–80 m of the cliff base. All six identified hazardous rock masses pose varying levels of threat to residential structures at the slope foot, highlighting substantial spatial variability in hazard distribution. Drawing on the preceding diagnostic results and dynamic simulations, we recommend a three-tier “zonal defense with in situ energy dissipation” scheme: (i) install 500–2000 kJ flexible barriers along the crest and upper slope to rapidly attenuate rockfall energy; (ii) place guiding or deflection structures at mid-slope to steer blocks and dissipate momentum; and (iii) deploy high-capacity flexible nets combined with a catchment basin at the slope foot to intercept residual blocks. This staged arrangement maximizes energy attenuation and overall risk reduction. This study shows that integrating high-resolution 3D point clouds with rigid-body contact dynamics overcomes the spatial discontinuities of conventional surveys. The approach substantially improves the accuracy and efficiency of hazardous rock stability assessments and rockfall trajectory predictions, offering a quantifiable, reproducible mitigation framework for long slopes, large rock volumes, and densely fractured cliff faces. Full article