Search Results (500)

This research develops a new low-cost method for continuous flow monitoring in open channels. Flow is calculated using a standard 1D hydraulic model that integrates surveyed cross-sections and water level measurements at the boundaries of a studied reach, from which the name Boundary Water Level Method (BWLM) is derived. By implementing low-cost ultrasonic sensors for water level measurement, the method gains advantage for application on smaller channels, which are often not included in national hydrological monitoring networks due to limited budgets. New and innovative monitoring methods in hydrology are a necessary alternative to increasing the monitoring budgets, especially for continuous, real-time flow monitoring. Like any novel method, it requires validation under the intended environmental conditions, especially when designed primarily for ungauged channels. Validation was conducted on two test-sites by comparing the BWLM discharge and the discharge from official hydrological stations, with an error of up to 15%. BWLM provides reliable discharges using estimated hydraulic roughness values based on the literature and experience. Sensitivity analysis of the estimated hydraulic roughness coefficient demonstrated a substantial influence on the resulting discharge values. This has to be considered when implementing the method in unstudied basins. Full article

Urban water distribution systems (WDSs) face increasing threats from accidental or intentional contaminant intrusion events. While contamination warning systems using water quality sensors enable early detection and rapid response to contamination events, traditional sensor placement approaches often rely on a single or limited performance metric, overlooking the multidimensional nature of system resilience. This study presents a multidimensional resilience-based framework for the optimal placement of water quality sensors in urban WDSs, integrating hydraulic and water quality simulations using the EPANET-MATLAB toolkit with a genetic algorithm (GA) optimization process. For Anytown Water Distribution Network, four distinct functionalities were formulated to capture different aspects of system performance during contamination events, and an integrated-multidimensional resilience metric was proposed as a collective measure. Results demonstrated that the optimal sensor configurations varied significantly depending on the selected functionality. However, the integrated multidimensional resilience-based approach yielded more balanced and effective sensor placements, simultaneously enhancing resilience levels for all individual functionalities. Furthermore, the findings indicated that adding more sensors beyond a certain number offers marginal improvements in system resilience, suggesting that sensor deployment should be guided by monitoring objectives (e.g., resilience) rather than simply increasing sensor numbers. The findings and discussion suggest practical insights for utilities to enhance water supply services with safe quality and system security against contamination threats in urban WDSs. Full article

To address the problems of uneven stubble height and high missed-cutting rate caused by the insufficient profiling capability of traditional forage harvesters in complex hilly terrain, this paper designs a three-degrees-of-freedom (DOF) profiling header primarily for typical hilly terrain with gentle slopes of 8–15°. Through pitch, roll, and height adjustments, it stably maintains stubble height at 150 mm. Subsequently, geometric analysis and structural optimization achieved kinematic decoupling among all degrees of freedom, thereby overcoming the inherent limitations of the two-DOF header, such as poor adaptability to longitudinal slope and strong adjustment coupling. Three-dimensional modeling was completed in SolidWorks, multibody dynamics simulation was performed in ADAMS, and a profiling control system incorporating a hydraulic system, multi-source sensor fusion, and a fuzzy PID controller was built. The dynamics simulation results show that under the working conditions of 15° longitudinal and 10° transverse slopes, the stubble height error of the header is controlled within 10%, the attitude angle adjustment error is less than 0.5°, and the dynamic response is excellent. Prototype field tests showed that, compared with the two-DOF header, the three-DOF profiling header improved the stubble height stability by about 35%, reduced the missed-cutting rate by about 5%, and increased the operating efficiency by about 15%. No cutting blade contact with the soil occurred, verifying the rationality of the mechanism design and its adaptability to terrain. This study provides an effective technical solution for improving the mechanization level of forage harvesting in hilly and mountainous areas. Full article

Accurately quantifying vertical sediment transport rates in large seaward rivers is vital for estimating basin-scale water and sediment fluxes and assessing riverbed evolution. Traditional multi-point velocity and suspended sediment concentration (SSC) measurements are costly and slow, hindering long-term online monitoring. Bidirectional flows in tidal reaches further exacerbate this challenge. We propose a physics-constrained support vector machine (SVM) inversion method to estimate vertical sediment transport rates from single-point measurements. Constrained by modified logarithmic velocity and Rouse suspended sediment concentration profiles, it quantitatively relates single-point hydraulic variables to key parameters governing vertical distributions. Lower Yangtze River tidal reach field data validate the hybrid model’s successful reconstruction of vertical distributions. It accurately captures transient sediment responses across maximum flood and ebb. Inverted transport rates match measurements closely (RMSE = 0.085, NSE = 0.969, PBIAS = 2.50%) and exhibit strong cross-site generalization. Sensitivity analysis identifies 0.4 times the water depth above the riverbed as the optimal single-point sensor position. Although currently validated only in the lower Yangtze River, this low-cost, reliable method supports local basin management, flood control, and disaster mitigation by enabling continuous sediment flux monitoring. However, applying it to other river or estuarine systems may require recalibration or retraining to adapt to different local conditions. Full article

Cavitation in rotating hydraulic machinery—such as industrial pumps and hydropower turbines—can cause blade and casing erosion, excessive vibration, noise and efficiency loss, posing significant operational and economic risks across industrial sectors. Reliable and scalable monitoring strategies are therefore essential, particularly under variable operating conditions in real-world environments. Recent advances in machine learning (ML) and deep learning (DL) have enabled data-driven approaches for cavitation detection based on operational sensor signals, yet a structured synthesis of these developments is lacking. This scoping review systematically analyzes measurement-based ML and DL approaches for cavitation monitoring, with the aim of identifying key trends, challenges and future research directions. Following PRISMA-ScR and JBI guidelines, 52 peer-reviewed studies published between 1996 and 2025 were evaluated, covering laboratory and field investigations across pumps and turbines and a wide range of model architectures. The analysis reveals that most studies are laboratory-based (∼80%), focus on pumps (∼70%) and rely on single-machine datasets (>80%), limiting generalization across machines and operating conditions. Classical ML approaches remain relevant due to interpretability and robustness with limited data, while DL enables end-to-end learning from raw or time–frequency transformed signals, frequently achieving diagnostic accuracy above 95%. Hybrid frameworks combining DL-based feature extraction with classical classifiers are increasingly adopted. Key limitations across the literature include domain shifts between laboratory and field data, scarce or inconsistent labeling and a predominant focus on categorical cavitation severity levels. Full article

Maize cultivation in tropical Oxisols during the second growing season faces significant climatic risks, where spatial heterogeneity in soil water retention often dictates economic viability. This study integrated a trimodal sensing approach, combining multispectral, thermal, and LiDAR data, with proximal physiological measurements to map isohydric responses and yield variability. Conducted in the Brazilian Cerrado, the research monitored a one-hectare maize field using UAV-based sensors alongside ground truth evaluations of gas exchange, leaf water potential, and soil moisture. Results revealed high yield variability (6.6 to 13.4 Mg ha⁻¹) primarily governed by clay content-mediated water availability. Maize exhibited strict isohydric behavior, maintaining homeostatic leaf water potential through preventive stomatal closure, which limited CO₂ assimilation in zones with lower water retention. A significant statistical decoupling was observed between plant height and final grain yield, as water stress impacted reproductive stages more severely than vegetative growth. Furthermore, the Temperature Vegetation Dryness Index (TVDI) served as a robust proxy for biomass vigor rather than mere water deficit. These results confirm that yield variability in tropical Oxisols was not a product of hydraulic failure, but rather a consequence of carbon limitation necessitated by the crop’s conservative hydraulic management to maintain leaf water potential within safe thresholds. Full article

Micro-electro-mechanical systems (MEMS) sensors offer the benefits of compact size, lightweight design, and low cost, which has led to widespread use in consumer electronics, vehicles, healthcare, defense, and communications. As their performance has improved, MEMS sensors have also found applications in oil exploration and geophysical studies. Pressure and temperature measurements during hydraulic fracturing have long been employed to improve downhole conductivity during oil and gas extraction. Nevertheless, the development of high-precision MEMS sensors for oil exploration remains an active area of research. This paper presents the design, fabrication, packaging, and characterization of a silicon-on-insulator (SOI) MEMS piezoresistive pressure sensor integrated with a temperature sensor. It also describes the design of a chamber intended to emulate conditions at the bottom of oil exploration wells. The sensors were successfully designed and fabricated on the basis of physics-based simulations, deep reactive ion etching and anodic bonding. The pressure sensors, together with the signal-conditioning system, exhibited a linear response with a sensitivity of 0.0268 mV/V/MPa and maximum hysteresis of 5.3%. Full article

Heavy-duty forklifts possess substantial kinetic energy during braking, which is currently wasted due to a lack of recovery in conventional systems. To ensure braking safety, an electro-hydraulic–mechanical compound braking system is necessary. However, the uncoordinated distribution between regenerative and mechanical braking torque leads to braking torque fluctuations, compromising safety, comfort, and recovery efficiency. This paper constructs a parallel hydraulic hybrid power system for heavy-duty forklifts based on a four-quadrant pump/motor, enabling braking energy recovery and reuse via the pump/motor and an accumulator. A compound braking strategy based on the ideal braking force distribution and multi-sensor information fusion is proposed. The system incorporates various sensors, including pressure, speed, flow, and pedal displacement sensors, to monitor system status and driver intention in real time, providing precise data for coordinated control. Feasibility is verified through AMESim simulation and real vehicle tests. The control system based on sensor feedback maximizes braking energy recovery while ensuring braking safety and comfort, achieving a 12.2% energy-saving rate and significantly improving the vehicle’s economy and range. Full article

Delayed switching faults in hydraulic directional control valves can significantly degrade system performance and reliability, yet their diagnosis remains challenging due to complex fault mechanisms and coupled sensor responses and limited fault samples in industrial applications. While data-driven approaches, including deep learning-based methods, have shown promising performance in fault diagnosis, their practical deployment in industrial quality inspection and condition monitoring is often constrained by limited fault data availability and insufficient physical interpretability of the diagnostic results. In this study, an interpretable fault diagnosis framework for delayed switching faults in hydraulic directional control valves is proposed based on a simulation-guided feature construction method and multi-pressure signal analysis. Instead of using simulation to generate synthetic training data, a physical simulation model is employed to analyze fault mechanisms and to guide the design of valve-level diagnostic features derived from inter-sensor pressure differences. These features are further evaluated using several classical machine learning classifiers, including RF, SVM, KNN, and LR under conditions of limited fault samples. Experimental results demonstrate that the proposed method effectively captures the structural imbalance caused by internal valve faults and achieves high diagnostic accuracy and robustness compared with conventional single-sensor approaches and purely data-driven black-box models. The proposed framework provides a practical and physically interpretable solution for hydraulic valve fault diagnosis under small-sample conditions and offers potential value for industrial quality inspection and maintenance applications. Full article

Sediment yield remains a major challenge in erosion-prone watersheds because it affects reservoir capacity, water quality, hydraulic infrastructure, and ecological stability. Although numerous studies have examined sediment yield estimation and sediment control, these topics are often treated separately, limiting the development of integrated watershed management strategies. Unlike many existing sediment yield review papers that focus primarily on predictive models, erosion processes, or management measures in isolation, this study provides an integrated synthesis of sediment yield estimation methods and sediment control strategies within a single watershed management framework for erosion-prone environments. The review covers empirical models, traditional sampling, physically based models, and emerging data-driven tools such as artificial intelligence, machine learning, remote sensing, and sensor-based monitoring, alongside structural, vegetative, and adaptive sediment control measures. The reviewed literature indicates three major trends: increasing integration of GIS and remote sensing with conventional models, wider use of process-based models for scenario analysis, and rapid growth of AI-based methods for real-time and nonlinear prediction. The findings further show that no single estimation or control strategy is universally applicable; performance depends strongly on watershed scale, sediment connectivity, land use, climatic regime, and data availability. Overall, the review highlights the need for integrated, adaptive, and site-specific sediment management frameworks that combine predictive modeling, monitoring technologies, and practical control interventions to improve long-term watershed resilience. Full article

Advanced high-temperature fluid reactors (ARs), such as sodium fast reactors (SFRs) and molten salt cooled reactors (MSCRs) utilize high-temperature fluids at ambient pressure. To melt the fluid during reactor startup and prevent fluid freezing during cooldown, the thermal–hydraulic systems of such ARs include heater zones consisting of specific heaters with controllers, temperature sensors, and thermal insulation. The failure of heater zones due to insulation material degradation or improper installation, resulting in parasitic heat losses, can lead to fluid freezing. The detection of faults using a heat-transfer model is difficult because of a lack of knowledge of the experimental details. Data-driven machine learning of heater zone temperature time series offers a viable alternative. In this study, we benchmarked the performance of recurrent neural networks (RNNs) in an analysis of heat-up transient temperature time series of heater zones installed on a liquid sodium vessel. The RNN models include long short-term memory (LSTM) and gated recurrent unit (GRU) networks, as well as their bi-directional variants, BiLSTM and BiGRU. Anomalous temperature points were designated using a percentile-based threshold applied to residual fluctuations in the detrended temperature time series. Additionally, the impact of the exponentially weighted moving average (EWMA) method on detection accuracy was examined. The RNN models’ performance was assessed using precision, recall, and F₁ score metrics. Results demonstrated that RNN models effectively detect anomalies in temperature time series with the best models for each heater zone achieving F₁ scores of over 93%. To explain the variations in RNN model performance across different heater zones, we used Kullback–Leibler (KL) divergence to quantify the relative entropy between training and testing data, and the Detrended Fluctuation Analysis (DFA) to assess long-range temporal correlations. For datasets with strong long-range correlations and minimal relative entropy between training and testing data, GRU is the best-performing model. When the data exhibits weaker long-term correlations and a significant relative entropy between training and testing distributions, BiGRU shows the best performance. For the data sets with intermediate values of both KL divergence and DFA, the best performance is obtained with LSTM and BiLSTM, respectively. Full article

Anomaly detection has been a challenging subject in many industrial fields. In industrial machinery such as hydraulic excavators, sensor data distributions are inherently multimodal because different operating conditions produce distinct sensor signatures, and conventional algorithms struggle to establish clear normal–abnormal boundaries when these conditions are mixed. We propose an action-state decomposition framework that partitions multimodal sensor data into homogeneous subsets based on discretized control inputs, thereby reducing the ambiguity of normal–abnormal boundaries by learning state-conditional distributions. The approach comprises a reactive method that evaluates each sample within its action state, and a history-based method that incorporates temporal context from previous action states. This decomposition is algorithm-agnostic and can improve detection performance across diverse anomaly detection algorithms. The framework is further extended to Bayesian fault diagnosis that identifies the root cause of failures using action-state-conditional detection probabilities. Experiments on simulated excavator data and two real-world benchmark datasets (UCI Hydraulic Systems and SKAB) demonstrate the generalizability of the proposed mode decomposition and provide insights into factors that may influence its effectiveness. The history-based method achieves a mean AUC of 0.89 across sensor fault types, outperforming all baseline algorithms, and the Bayesian fault diagnosis achieves 86.7% accuracy in identifying the root cause among six action fault types. For the proposed GMM-based methods, the decomposition also substantially reduces per-sample inference time by approximately 10× (from 8.68 μs to 0.75 μs), enabling real-time deployment in industrial settings. Full article

Leakage detection in water distribution networks plays an instrumental role in effectively addressing water loss, yet the scarcity of annotated leak events limits the applicability of supervised classification methods. While hydraulic simulation-generated datasets are often considered as an alternative, their generation is hindered by incomplete network topology and sparse sensor coverage in real-world settings. Consequently, many real-world solutions rely on unsupervised anomaly detection approaches but frequently struggle to balance sensitivity and accuracy. This study proposes a regression-ensemble framework that learns the district metered area (DMA)-specific demand–supply dynamics to detect emerging leaks using smart meter data, without requiring real or simulated labeled leak datasets for training. Regression models—Random Forest, Support Vector Regression, XGBoost, and Multi-Layer Perceptron—are trained on DMA-level consumption and supply data that are preprocessed to preserve background leakage while correcting emerging leaks. Deviations between predicted and observed supply are quantified through Pearson correlation, Kendall’s tau, and Z-score, whose anomaly indications are combined at metric and model levels using weights derived from model prediction accuracy. A leak is identified once the ensemble anomaly score crosses a threshold. The system detects leaks within 8–12 h of onset, achieving 90% and 98% accuracy on simulated and real leak scenarios, respectively, at an anomaly-score threshold of 0.5. Recall rates of 85% and 95% are observed for simulated and real leaks, respectively, whereas 95% and 100% recall are observed for no-leak events in both leak scenarios, respectively. Our proposed framework demonstrates the potential of smart meter-driven ensemble analytics for rapid and robust leak detection. Full article

Understanding hydrological, agricultural, and environmental processes in soils relies on accurately measuring volumetric water content (θ), matric potential (h), and hydraulic conductivity (K). These parameters are fundamental for quantifying plant-available water, optimizing irrigation scheduling in precision agriculture, modeling watershed responses, and studying the impacts of climate change in complex ecosystems. Among these parameters, θ is truly indispensable, as it represents the primary indicator of the water status of soils and a prerequisite for interpreting the other hydraulic variables. In recent years, capacitive sensors have become one of the most widely adopted technologies for θ estimation, owing to their favorable balance between accuracy, robustness, and affordability. These sensors infer soil moisture by measuring dielectric permittivity of soils, which is strongly governed by water content, making them particularly suitable for distributed monitoring and IoT-based environmental applications. The present study aimed to develop a low-cost capacitive sensor for θ estimation. This sensor can be made using 3D printing technology combined with conductive, nickel-based paint, which (once applied on the 3D-printed guides) forms the capacitive electrode. The capacitive component operates at an operational frequency of 60 MHz. The system was subjected to a rigorous testing protocol, including calibration and validation phases under laboratory conditions using three soils of different textures. Its performance was specifically compared with the time-domain reflectometry (TDR) technique, which is widely recognized in Soil Physics and Soil Hydrology as the reference method for θ estimation due to its reliability and accuracy. These tests confirmed the effective performance of the proposed sensor, which overall exhibited good reliability within the selected validation range, corresponding to a θ range of 0 to 0.40 cm³/cm³. Full article

Hydraulic systems are pivotal components in safety-critical aerospace and industrial applications, making reliable health monitoring essential. However, traditional data-driven diagnosis methods typically rely on static graph structures that fail to capture evolving sensor correlations during different fault modes. Furthermore, existing grid-based models often struggle to extract multi-resolution features and maintain performance under data-limited conditions. To address these challenges, this paper proposes a novel Dynamic Multi-Scale Graph Neural Network (DMS-GNN) for hydraulic system fault diagnosis. The framework integrates a hierarchical multi-scale feature extraction module to capture diverse fault signatures across different frequency bands. Crucially, a self-attention-based dynamic graph learner is introduced to adaptively infer latent sensor topologies end-to-end, eliminating the reliance on predefined physical connections. Experimental validation on a dedicated electro-hydraulic test bench demonstrates that the proposed DMS-GNN achieves a superior diagnostic accuracy of 98.47%, outperforming state-of-the-art baselines such as GraphSAGE, Static GCN, and GAT. The result confirms the efficacy of combining multi-scale temporal learning with dynamic spatial reasoning for robust multi-sensor fusion diagnosis. Full article