Search Results (501)

A single-plunger two-dimensional electro-hydraulic pump is an integrated unit in which a two-dimensional plunger pump is embedded inside the rotor of a permanent magnet synchronous motor, significantly improving the power density and power-to-weight ratio of electro-hydraulic pumps. The pursuit of a higher power-to-weight ratio has made high-speed operation and high-pressure output persistent research priorities. However, during the iterative design process of electro-hydraulic pumps, cavitation has been identified as a common issue, leading to difficulties in oil suction and even severe backflow. Based on the structure and motion characteristics of the single-plunger two-dimensional electro-hydraulic pump, a CFD numerical model was established to analyze the influence of different working conditions on the cavitation characteristics inside the pump. The study shows that cavitation mainly occurs in the plunger chamber, the distribution groove, and the triangular damping groove. The location and intensity of cavitation are directly reflected by the gas volume fraction. The simulation analysis of variable operating conditions has verified that suction pressure and rotational speed have a significant impact on cavitation—an increase in suction pressure can effectively suppress cavitation, while an increase in rotational speed will exacerbate cavitation development. Specifically, the non-cavitation working boundary of this type of pump was determined through theoretical derivation, and the coupling relationship between critical suction pressure and critical speed was clarified. This work provides an important theoretical basis for the optimization design of the new integrated electro-hydraulic pump. Full article

Electro-hydraulic servo fatigue testing machines are susceptible to concurrent degradation and failure of multiple components during high-frequency, high-load, and long-duration cyclic operations, posing significant challenges for online health monitoring. To address this, this paper proposes a multi-state recognition method based on spatiotemporal feature fusion and bidirectional cross-attention. The method employs a Bidirectional Temporal Convolutional Network (BiTCN) to extract multi-scale local features, a Bidirectional Gated Recurrent Unit (BiGRU) to capture forward and backward temporal dependencies, and Bidirectional Cross-Attention (BiCrossAttention) to achieve fine-grained bidirectional interaction and fusion of spatial and temporal features. During training, GradNorm is introduced to dynamically balance task weights and mitigate gradient conflicts. Experimental validation was conducted using a real-world multi-sensor dataset collected from an SDZ0100 electro-hydraulic servo fatigue testing machine. The results show that on the validation set, the cooler and servo valve achieved both accuracy and F1-scores of 100%, the motor-pump unit achieved an accuracy of 98.32% and an F1-score of 97.72%, and the servo actuator achieved an accuracy of 96.39% and an F1-score of 95.83%. Compared to single-task models with the same backbone, multi-task learning improved performance by approximately 3% to 4% for the hydraulic pump and servo actuator tasks, while significantly reducing overall deployment resources. Compared to single-task baselines, multi-task learning improves performance by 3–4% while reducing deployment parameters by 75%. Ablation studies further confirmed the critical contributions of the bidirectional structure and individual components, as well as the effectiveness of GradNorm in multi-task learning for testing machines, achieving an average F1-score of 98.38%. The method also demonstrated strong robustness under varying learning rates and resampling conditions. Compared to various deep learning and fusion baseline methods, the proposed approach achieved optimal performance in most tasks. This study provides an effective technical solution for high-precision, lightweight, and robust online health monitoring of electro-hydraulic servo fatigue testing machines under complex operating conditions. Full article

Vehicle axle load scales are one of the most important devices for vehicle safety testing. To ensure the stability and reliability of test results, regular calibration of axle load scales is necessary. Traditional calibration methods are inefficient and error-prone. In this work, an automatic calibration device for portable axle load scales was presented, which uses a pump-controlled hydraulic cylinder as a loading unit. The loading unit was controlled by a high-precision force sensor and a PLC. A hydraulic unit based on a servo motor and a gear pump was designed, and control software including automatic control, data acquisition, and report generation was developed. The experimental test was carried out. The results showed that the developed portable automatic calibration device could realize the automatic calibration of a 0~150 kN load range, and the accuracy level was up to ±0.3%. Finally, it was verified that the device had the advantages of compactness and lightweight and simple operation. Full article

Rotary rock drilling generates vibration signals that capture the dynamic behavior of the drilling system, the interaction between the tool and the rock, and the progression of tool wear. These signals, traditionally considered undesirable, have become a key source of information for condition monitoring and predictive maintenance. This study experimentally investigates vibration sources and diagnostic indicators using a laboratory horizontal drilling stand equipped with accelerometers and controlled operating regimes. Six regimes were evaluated, ranging from idle operation of individual aggregates (motor, pump, hydrogenerator) to drilling of concrete and granite under defined process parameters. Vibration data were analyzed in the time, frequency, and time–frequency domains using RMS, variance, spectral centroid, spectral entropy, FFT-based spectra, autocorrelation, cross-correlation, and spectrograms. The results confirm the research hypothesis that selected vibration-based indicators correlate with tool degradation. Increased RMS values, higher variance, reduced correlation symmetry, and a shift of spectral energy above 6 kHz reliably reflect wear progression and changes in the dynamic response of the system. Spectrograms further reveal transient phases and redistribution of vibration energy during drilling. The findings demonstrate that vibration measurements enable the identification and separation of vibration sources related to aggregates and processes. The extracted diagnostic features form a basis for intelligent monitoring and predictive algorithms in rotary drilling, supporting advanced condition monitoring strategies within Industry 4.0. Full article

Water pumps consume roughly 20% of global electricity, yet 60–70% of pumps operate below optimal efficiency, leading to substantial energy waste. Improving pump efficiency is therefore critical. A major contributor to these losses is the low efficiency of the driving motor at reduced speeds and the lack of variable-speed capability—especially in single-phase pumps. This paper presents a fuzzy-logic–FOC (field oriented control) permanent magnet synchronous motor (PMSM) pump system that can run on either three-phase or single-phase power. The system maintains high efficiency across a wide speed range and saves energy not only through variable-speed operation but also via an intelligent control strategy termed “constant flow, variable pressure.” To assess performance, we conducted experiments comparing the proposed fuzzy-logic FOC controlled PMSM pump and a conventional AC asynchronous induction motor pump. The results show that the new system overcomes the inherent lack of speed regulation in traditional single-phase pumps and significantly improves efficiency across diverse operating conditions. Moreover, by implementing the “constant flow, variable pressure” strategy, the system achieves average energy savings estimated at 30–50% compared with a conventional AC asynchronous motor-driven pump. Full article

Pipeline transportation of petroleum products remains one of the safest and most efficient methods of bulk energy delivery, yet overpressure events continue to pose serious operational and regulatory challenges. Traditional fixed-gain PI controllers, commonly used with centrifugal pump drives, cannot adapt to varying product densities or transient disturbances such as valve closures that generate water hammer. This paper proposes a self-tuning adaptive controller based on Recursive Least Squares (RLS) parameter estimation to improve safety and efficiency in pipeline pump operations. A nonlinear simulation model of a centrifugal pump driven by an induction motor is developed, incorporating pipeline friction losses via the Darcy–Weisbach relation and pressure transients induced by rapid valve closures. The RLS algorithm continuously estimates effective loop dynamics, enabling online adjustment of proportional and integral gains under changing fluid and operating conditions. Simulation results demonstrate that the proposed RLS-based adaptive controller maintains discharge pressure within ±2% of the target setpoint under density variations from 710 to 900 kg/m³ and during severe transient events. Compared to a fixed-gain PI controller, the adaptive strategy reduced pressure overshoot by approximately 31.9% and settling time by 6%. Model validation using SCADA field data yielded an $R^2$ = 0.957, RMSE = 3.95 m³/h, and normalized NRMSE of 12.6% (by range), confirming strong agreement with measured system behavior. The findings indicate that RLS-based self-tuning provides a practical enhancement to existing pipeline control architectures, offering both improved robustness to abnormal transients and greater efficiency during steady-state operation. This work establishes a foundation for higher-level supervisory and game-theoretic coordination strategies to be explored in subsequent studies. Full article

Reliable detection of faults in centrifugal pump systems is challenging in industrial environments due to harsh operating conditions, limited sensor access, and the need for fast, explainable decisions. We developed PumpSpectra, an industrial Motor Current Signature Analysis (MCSA) platform that processes uploaded stator-current CSV files using FFT/STFT with transparent, rule-based models designed to identify mechanical faults including misalignment, bearing defects, and impeller anomalies; field validation demonstrated misalignment detection. In a case study at the El Oued desalination plant (Algeria; $n=40$ operating points), PumpSpectra achieved 91.2% diagnostic accuracy with a 95% reduction in analysis time compared to manual MCSA post-processing, and a false-positive rate of 3.8% at 0.1 Hz resolution. These results suggest that current-only, explainable analytics can support predictive maintenance programs by accelerating fault triage, improving traceability of decisions, and reducing avoided maintenance costs in pump-driven industrial assets. Full article

This study presents a streamlined and accurate approach for modeling the performance of hermetic reciprocating compressors under variable-speed conditions. Traditional compressor models often neglect the influence of motor frequency, leading to considerable deviations at low-speed operation. To address these limitations, a frequency-dependent numerical framework was developed using one-dimensional (1-D) and two-dimensional (2-D) polynomial regressions to represent volumetric efficiency (${\eta }_v$) and isentropic efficiency (${\eta }_{isentr}$) as functions of compression ratio (r) and motor speed frequency (f). The proposed model integrates manufacturer data and thermodynamic property databases to predict compressor behavior across a wide range of operating conditions. Validation using the Bitzer 4HTE-20K CO₂ compressor demonstrated strong agreement with experimental data, maintaining prediction errors within ±10% for both power input and discharge temperature. Moreover, the model enhanced accuracy by up to 19.4% in the low-frequency range below 40 Hz, where conventional models typically fail. The proposed method provides a practical and computationally efficient tool for accurately simulating the performance of hermetic reciprocating compressors that support improved design, optimization, and control of refrigeration and heat pump systems. Full article

Electric Submersible Pumps (ESPs) are widely deployed in high-flowrate wells but are constrained by frequent failures and the need for rig-based interventions. This study presents the development and field validation of a rigless cable-deployed ESP (CDESP) system designed to enhance operational uptime and reduce intervention costs. The system features a corrosion-resistant metal-jacketed power cable, an inverted ESP configuration that eliminates the motor lead extension (MLE), and a vertical cable hanger spool (VCHS) for surface integration without removing the production tree. A field trial in a high-H₂S well demonstrated successful rigless deployment using coiled tubing (CT), achieving over two years of continuous runtime. Post-retrieval inspection revealed minimal wear, validating the system’s mechanical durability and reusability. Operational performance demonstrated reduced non-productive time (NPT), enhanced safety, and cost savings, with deployment completed in under 24 h, compared to the typical 10–14 days for rig-based methods. The CDESP system’s compatibility with digital monitoring and its potential for redeployment across wells positions it as a transformative solution for offshore and mature field operations. These findings support the broader adoption of CDESP as a scalable, efficient, and safer alternative to conventional ESP systems. Full article

The electrification of non-road mobile machinery is advancing to enhance sustainability and reduce emissions. This study investigates how to maximize the efficiency of the retrofitting of a material handler from an internal combustion engine to a battery-powered electric motor, while keeping the hydraulic system unchanged. Using a previously validated model, this study proposes three control strategies for the electric motor and hydraulic pump to enhance efficiency and performance. The first control strategy optimizes hydraulic pump performance within its most efficient displacement range. The second strategy maximizes powertrain efficiency by considering both efficiencies of the electric motor and hydraulic pump. The third strategy uses a servo-actuated valve to adjust the load-sensing margin and exhibits energy savings up to 14.2% and an 11.5% increase in efficiency. The proposed strategies avoid complex optimization algorithms, ensuring practical applicability for small- and medium-sized enterprises, which often face cost constraints and limited scalability. Full article

This study introduces a sensorless fault diagnosis method for efficiently detecting bearing faults in induction motors. The proposed method eliminates the need for torque sensors, frequency sensors, thermal cameras, or real-time Fast Fourier Transform (FFT) tools. Induction motors are commonly utilized in a variety of industrial applications, including fans, pumps, and home appliances, due to their straightforward construction, affordability, and robust reliability. Traditional bearing fault diagnosis methods often rely on additional hardware such as vibration or thermal sensors. Additionally, approaches employing Artificial Intelligence (AI) and real-time FFT processing require advanced and expensive hardware capabilities. However, many V/f control systems are primarily intended for cost-effective and simple implementations, making resource-intensive approaches undesirable. Therefore, such methods present limitations for these use cases. To address these challenges, this paper presents a sensorless detection technique that estimates torque via a flux observer, removing the dependence on external sensors. The estimated torque is processed using an offline FFT to identify amplitude changes within bearing fault frequency bands. Here, the FFT-based frequency analysis is performed offline and is used to design a targeted band-pass filter (BPF). The torque signal, after passing through the BPF, undergoes a straightforward threshold-based logic to assess the existence of faults. Compared to AI- or data-driven approaches, the proposed method provides a lightweight, interpretable, and sensorless solution without the need for additional training or high-end processors. Despite its straightforward approach, the technique achieves effective detection of bearing faults across various components and speeds, making it ideal for embedded and economically constrained motor applications. Full article

Due to the limitations of traditional micropumps in terms of miniaturization and integration, ferrofluid micropumps, as emerging microfluidic driving devices, exhibit significant application potential due to their unique pumping mechanism. Research on ferrofluid micropumps can advance micro/nano technology, meet biomedical needs, and facilitate micro-electro-mechanical system (MEMS) integration. As traditional structural improvement methods struggle to meet increasingly stringent application conditions, under the action of the motion and mechanism of magnetic fluids, a new method of using neodymium magnetic ball plugs instead of traditional magnetic fluid plungers has been developed, aiming to enhance the pumping performance. In this study, the influence of the magnetic field (MF) generated by permanent magnets (PM) on the magnetic properties inside the micropump cavity was first determined. Furthermore, it was revealed in this research that the neodymium magnetic ball plug enhances the pumping flow rate and maximum pumping height of the ferrofluid plug and the pumping stability of the neodymium magnetic ball plug ferrofluid micropump is significantly improved. Additionally, the rotational speed (Rev) of the dynamic neodymium magnetic ball type magnetic fluid plug driven by the motor and the magnetic strength created by the PM are the main aspects influencing the result in this experiment. Full article

Shaft misalignment is among the most common faults in rotating machinery, and although many diagnostic methods have been proposed, reliably detecting it under varying load conditions remains a major challenge for vibration-based techniques. To address this issue, this study proposes a new vibration-based misalignment detection framework that leverages cointegration analysis. The approach examines both the stationarity of vibration signals and the residuals derived from the cointegration process. Specifically, it combines the Augmented Dickey–Fuller (ADF) test with cointegration analysis in three stages: (1) applying the ADF test to raw vibration data before cointegration, (2) performing cointegration on the vibration time series, and (3) reapplying the ADF test to the post-cointegrated data. The method was validated using experimental measurements collected from a laboratory-scale test rig comprising a motor, gearbox, and hydraulic gear pump, tested under both healthy and misaligned states with varying degrees of severity. Vibration signals were recorded across multiple load conditions. The results demonstrate that the proposed method can successfully detect misalignment despite load variations, while also providing insights into fault severity. In addition, the residuals from the cointegration process proved to be highly sensitive to damage, highlighting their value as features for vibration-based condition monitoring. Full article

Reliable water access in remote and desert-like regions remains a challenge, particularly in areas with limited infrastructure. Solar-powered submersible pumps offer a promising solution; however, optimizing their performance under variable environmental conditions remains a challenging task. This research presents an Artificial Intelligence (AI)-driven digital twin framework for modeling and optimizing the performance of a solar-powered submersible pump system. The proposed system has three core components: (1) an AI model for predicting the inverter motor’s output frequency based on the current generated by the solar panels, (2) a predictive model for estimating the pump’s generated power based on the inverter motor’s output, and (3) a mathematical formulation for determining the volume of water lifted based on the system’s operational parameters. Moreover, a dataset comprising 6 months of environmental and system performance data was collected and utilized to train and evaluate multiple predictive models. Unlike previous works, this research integrates real-world data with a multi-phase AI modeling pipeline for real-time water output estimation. Performance assessments indicate that the Random Forest (RF) model outperformed alternative approaches, achieving the lowest error rates with a Mean Absolute Error (MAE) of 1.00 Hz for output frequency prediction and 1.39 kW for pump output power prediction. The framework successfully estimated annual water delivery of 166,132.77 m³, with peak monthly output of 18,276.96 m³ in July and minimum of 9784.20 m³ in January demonstrating practical applicability for agricultural water management planning in arid regions. Full article

This study investigates the dynamic characteristics of electro-hydrostatic actuators (EHA), which serve as the core actuating element in vehicle active suspension systems, with the aim of enhancing overall system performance. The purpose of this research is to identify and address the factors limiting EHA dynamic response. Through theoretical analysis from the perspectives of natural frequency properties and power demand, the study reveals that the natural frequency of the motor-pump assembly acts as the primary bottleneck, while insufficient motor output torque represents another major constraint. To overcome these limitations, a method is proposed involving increased maximum motor output torque and reduced rotational inertia of the motor-pump assembly. The feasibility of this approach is validated via frequency domain simulation analysis. Comparative simulations demonstrate that the enhanced EHA system exhibits significantly improved dynamic performance under both step and sinusoidal position commands compared to the baseline system. These findings provide important theoretical insights and practical directions for overcoming actuator performance limitations in vehicle active suspension systems. Full article