Pump Leakage Research Articles

Domain correction for hydraulic internal pump leakage detection considering multiclass aberrant flow data

Harsh working environment not only threatens the health of the hydraulic system but also the condition monitoring system. The latter problem will make data aberrant and disable lots of data-based fault detection methods. Inspired by the Fail-Safe principle, the multiclass aberrant data problem is investigated in this study from the perspective of transfer learning. Firstly, the Domain Correction, a variant of Domain Adaptation, is defined theoretically. Then, an indirect Domain Correction framework is proposed and applied to internal pump leakage detection with aberrant flow data. The Teacher-Student structure is the basis. Extra Correction Module is designed to better correct aberrant representation into normal. Layer-wise training and the Noisy Tune are performed to mitigate overfitting. The Self Correction Attention mechanism is presented to help the model focus on the well-measured parts of samples. The proposed method can improve the model's accuracy on the aberrant dataset from 47.1% to 95.0%, meanwhile, the accuracy on the well-measured dataset is guaranteed at 99.2%.

An intelligent diagnosis method for oil-well pump leakage fault in oilfield production Internet of Things system based on convolutional attention residual learning

In the Internet of Things (IoT) system for oil field production, oil-well pump leakage is one of the most challenging faults to detect. Once the pump leakage fault occurs, it will severely impact crude oil production. Therefore, accurate diagnosis of oil-well pump leakage faults is highly necessary. However, achieving accurate diagnosis through existing artificial intelligence methods remains a challenge due to the prolonged and progressive nature of oil-well pump leakage faults. This paper proposes a novel methodology for diagnosing oil-well pump leakage. Specifically, we propose a time-series data transformation method that ingeniously transforms difficult-to-identify pump leakage long time-series data variation features into image pixel-level texture features, and based on the characteristics of differences in image texture features under different states, we propose the structure of convolutional residual blocks with attributes and spatial attention for oil-well pump leakage fault diagnosis, which enables more accurate fault diagnosis by assigning more attention weights to extract valuable fault information. Through extensive experimentation, we found that the diagnosis process proposed in this paper outperforms sequence models such as LSTM and GRU, achieving a diagnostic accuracy of 98.36% for oil-well pump leakage faults. Moreover, it leads to a significant improvement of 19.40% and 10.36% in accuracy compared to conventional CNN and ResNet models, respectively. Therefore, in the actual production of oilfields, this method can effectively meet the detection requirements for oil-well pump leakage.

Fault Diagnosis of Hydraulic System based on SOM Neural Network

The working principle of hydraulic system is to use the flow and pressure of liquid in the system for energy transfer and conversion. Hydraulic system realizes various work tasks such as pushing, grasping, lifting, rotating, etc. by controlling the action of hydraulic actuators. Its advantages are high power density, good stability, fast response time, smooth power output, etc. The disadvantage is that the hydraulic system is easy to lose control of more points. In this paper, taking the horizontal outrigger hydraulic circuit as an example, the hydraulic pump leakage, etc. is used as a fault sample, and the SOM neural network is used for fault diagnosis to make timely and accurate diagnosis of the abnormal or fault state of the hydraulic system, give guidance on the operation of the hydraulic system, improve the reliability and safety of operation, and reduce the fault loss to a minimum.

Effect of blade top clearance on semi-open fuel pump performance and leakage vortex

Effect of blade top clearance on semi-open fuel pump performance and leakage vortex

Internal pump leakage detection of the hydraulic systems with highly incomplete flow data

Poor working condition makes internal pump leakage become one of the most frequent faults of hydraulic systems in industrial and agricultural production. The faults detection of the internal pump leakage not only suffers from the feature engineering but also the data acquisition and transmission. The latter may lead to the highly (over 50%) incomplete data problem which is deadly for fault diagnosis. Hence, the highly incomplete data problem is defined in this study. Then a two-stage fault diagnosis method based on the flow data is proposed. In the first stage, a Denoising Auto-Encoder with the conditional mask is used to complement the incomplete flow data. The conditional mask helps the model get extra information at a high missing ratio. In the second stage, with the help of masked noise and the Mask Attention mechanism, a classifier orients to the completed data is trained. These modifications help the classifier pay more attention to the remaining parts of flow data. The proposed method achieves the accuracy of 97% and 96% on the flow data which is missing by 60% and 70%. All the improvement measures are justified by the ablation and comparison experiments.

Multi-step prediction of main pump leakage in nuclear power plants with an additive model

With the global demand of clean and low-carbon emission energy sources, safety in the nuclear power industry has gained widespread attention. The accurate and timely long-term fault prognostics of critical equipment in nuclear power plants (NPPs) helps to effectively schedule maintenance, thus reducing operation and maintenance costs while ensuring safety. Unpredictable future load and operations make long-term prediction of time series data in NPPs quite challenging. In this study, we proposed a framework for multi-step prediction based on an empirical mode decomposition (EMD) algorithm, which decomposed complex time series data into several components with simple trends. The useful signal and noise in the components were then divided by a relative ratio method. Candidate prediction models from the model library were trained and tuned on the signal-dominated components. The appropriate model was selected for prediction according to the characteristics of each component signal, and the prediction results were summed and smoothed with an additive model. The proposed method, noted as the denoised EMD method using an additive method (EMD-AM), was applied to the leakage prediction at shaft seals in the reactor coolant pump (RCP) of five different NPPs. Root mean square error (RMSE), mean absolute percentage error (MAPE) and mean absolute scaled error (MASE) were selected to determine the prediction accuracy and uncertainty. Compared with EMD-AM and EMD method using long short-term memory (EMD-LSTM), the experimental results show that the denoised EMD-AM has the overall advantage of simplicity and accuracy.

Fault Diagnosis Algorithm of Beam Pumping Unit Based on Transfer Learning and DenseNet Model

The difficulty of collecting fault data samples is one of the application problems of the deep learning method in fault diagnosis of mechanical production; the second is that when the depth of the learning network increases, the network accuracy is saturated or even decreased. Therefore, based on the deep learning algorithm and the DenseNet model, this paper establishes a fault diagnosis model for the beam pumping unit through the transfer learning method. The model uses the global pooling layer as the classifier. The model is used to classify and test various working conditions such as wax deposition, pump leakage, insufficient liquid supply, and pump leakage in oil wells. The results show that the model can obtain a classification model with high accuracy by learning a limited number of sample data; in the case of uneven data samples, the model can also basically complete the task classification task accurately. Through the evaluation of the test set, the model has an average accuracy of more than 95% in identifying various working conditions.

Predictive Maintenance Model Based on Multisensor Data Fusion of Hybrid Fuzzy Rough Set Theory Feature Selection and Stacked Ensemble for Fault Classification

With the rising demand for integrated and autonomous systems in the field of engineering, efficient frameworks for instant detection of performance anomalies are imperative for improved productivity and cost-effectiveness. This study proposes a systematic predictive maintenance framework based on the hybrid multisensor fusion technique of fuzzy rough set feature selection and stacked ensemble for the efficient classification of fault conditions characterised by uncertainties. First, a feature vector of time-domain features was extracted from 17 multiple sensor signals. Then, a comparative study of six different Fuzzy Rough Set Feature Selection (FRFS) methods was employed to select the various combinations of optimal feature subsets for various faults classification tasks. The determined optimal feature subsets then served as inputs for training the stacked ensemble (ESB(STK)). In the ESB(STK), Support Vector Machine (SVM), Multilayer Perceptron (MLP), k -Nearest Neighbour ( k -NN), C4.5 Decision Tree (C4.5 DT), Logistic Regression (LR), and Linear Discriminant Analysis (LDA) served as the base classifiers while the LR was selected to be the metaclassifier. The proposed hybrid framework (FRFS-ESB(STK)) improved the classification accuracy with the selected combinations of optimal feature subset size whiles reducing the computational cost, overfitting, training runtime, and uncertainty in modelling. Overall analyses showed that the FRFS-ESB(STK) proved to be generalisable and versatile in the classification of all conditions of four monitored hydraulic components (i.e., cooler, valve, accumulator, and internal pump leakage) when compared with the six base classifiers (standalone) and three existing ensemble classifiers (Stochastic Gradient Boosting (SGB), Ada Boost (ADB), and Bagging (BAG)). The proposed FRFS-ESB(STK) showed an average improvement of 11.28% and 0.88% test accuracies when classifying accumulator and pump conditions, respectively, whiles 100% classification rates were obtained for both cooler and valve.

Anomaly Detection with Feature Extraction Based on Machine Learning Using Hydraulic System IoT Sensor Data.

Hydraulic systems are advanced in function and level as they are used in various industrial fields. Furthermore, condition monitoring using internet of things (IoT) sensors is applied for system maintenance and management. In this study, meaningful features were identified through extraction and selection of various features, and classification evaluation metrics were presented through machine learning and deep learning to expand the diagnosis of abnormalities and defects in each component of the hydraulic system. Data collected from IoT sensor data in the time domain were divided into clusters in predefined sections. The shape and density characteristics were extracted by cluster. Among 2335 newly extracted features, related features were selected using correlation coefficients and the Boruta algorithm for each hydraulic component and used for model learning. Linear discriminant analysis (LDA), logistic regression, support vector classifier (SVC), decision tree, random forest, XGBoost, LightGBM, and multi-layer perceptron were used to calculate the true positive rate (TPR) and true negative rate (TNR) for each hydraulic component to detect normal and abnormal conditions. Valve condition, internal pump leakage, and hydraulic accumulator data showed TPR performance of 0.94 or more and a TNR performance of 0.84 or more. This study’s findings can help to determine the stable and unstable states of each component of the hydraulic system and form the basis for engineers’ judgment.

Effect of pump leakage on slip and pressure loss in a hydraulic cylinder drive

Pump leakage plays a critical role in the safety as well as the functioning of hydraulic drives. Studying the effect of pump leakage on the dynamics of hydraulic drive can improve the drive safety by giving insight into dynamic symptoms of leakage fault and can improve the functioning range of drive by giving insight into the slip and pressure losses of the drive during steady and transient states. This article aims at dynamic characteristic analysis of pump leakage in a hydrostatic drive with linear actuator and lever mechanism. Such hydraulic drives are used in several earth moving machines e.g. crane, dumper truck, front loader etc. To carry out this analysis, the system model is developed using the Bond graph technique and validated using real measurements. Finally, the developed model is utilized to generate and analyse the system dynamics under different intensities of pump leakage. The steady state portion of the results indicate that the pump leakage results in significant slip or flow loss as compared to pressure loss. However, the transient portion of the result indicates that effect of pump leakage is almost same on both slip loss as well as pressure loss of the drive.

Effect of various functional parameters on the dynamic trend of bent-axis motor

This paper examines the effect of load inertia, fluid stiffness, pump leakage resistance and the load pressure on the dynamic trend of bent-axis motor. For this analysis a test set-up has been made. The modelling and simulation of the test set-up have been done through bond graph simulation technique. The value of loss coefficients has been calculated in steady-state conditions. The work shown in this article may be useful to practical engineers in the early stages of a similar design process that uses this type of motor.

Position Tracking Performance with Fine Tune Ziegler-Nichols PID Controller for Electro-Hydraulic Actuator in Aerospace Vehicle Model

Electro-Hydraulic Actuator (EHA) system is a third order non-linear system which is highly suffer from system uncertainties such as Coulomb friction, viscous friction and pump leakage coefficient which makes this system more complicated for the designing of the controller. The Proportional-Integral-Derivative (PID) controller has proposed in this paper to control EHA system and main problem in its application is to tune the parameter to its optimum value. Two different methods are used to tune the PID controller which are trial and error and Ziegler-Nichols method. MATLAB Simulink is used to simulate the system. In order to determine the performance of EHA system for the position tracking. 3 different of external disturbance such as 0N, 5000N and 10000N has been injected into the system. Simulation results show that the Ziegler-Nichols fine tuning method provides the better tracking performance when compared to the trial and error method for every specific disturbance setting. The Ziegler Nichols method provides better disturbance rejection as the performances indexes such as percentage overshoot, settling time and steady state error are not affected by the varying of disturbance.

Dynamic thermal coupling modeling and analysis of wet electro-hydrostatic actuator

The electro-hydrostatic actuator (EHA) used in more electric aircraft (MEA) has been extensively studied due to its advantages of high reliability and high integration. However, this high integration results in a small heat dissipation area, leading to high-temperature problems. Generally, to reduce the temperature, a wet cooling method of using the pump leakage oil to cool the motor is adopted, which can also increase the difficulty of accurately predicting the system temperature in the early design stage. To solve this problem, a dynamic coupling thermal model of a wet EHA is proposed in this paper. In particular, the leakage oil of the pump is used as a coupling item between the electrical system and the hydraulic system. Then, an improved T-equivalent block model is proposed to address the uneven distribution of axial oil temperature inside the motor, and the control node method is applied to hydraulic system thermal modeling. Meanwhile, a dynamic coupling thermal model is developed that enables a dynamic evaluation of the wet EHA temperature. Then, experimental prototypes of wet motor and wet EHA are developed, while the temperature response of the wet motor at different rotation speeds and different loads and the temperature response of the wet EHA at no-load condition were verified experimentally at room temperature, respectively. The maximum temperature difference between the experimental and theoretical results of the wet motor as well as the experimental and theoretical results of the wet EHA is less than 8 °C. These test results indicate that the dynamic coupling thermal model is valid and demonstrate that the thermal coupling modeling method proposed in this paper can provide a basis for the detailed thermal design of EHA.

Performance degradation and wearing of Electrical Submersible Pump (ESP) with gas-liquid-solid flow: Experiments and mechanistic modeling

Electrical Submersible Pump (ESP), one of the most profitable artificial lift methods in the petroleum industry, is sensitive to flow conditions, especially under solid-particle flow. Compared to the sand produced from the unconsolidated sandstone, the proppant backflow in hydraulic fractured wells is more erosive to ESPs due to its higher hardness and larger diameter. As a result, the pump geometry is jeopardized, and the boosting pressure is affected. Therefore, the sand-water-air tests were conducted in this study to investigate the performance degradation and wearing damage in ESPs. For the first time, a semi-mechanistic model based on Achard law was proposed to predict the abrasion damage in the second flow passage of an ESP. A new mechanistic model was established to predict pump boosting pressure and describe the pump leakage effect under gas-liquid flow conditions. Considering the rotational effect, the leakage flow rate was calculated in three stage seal clearances, skirt ring, balance ring, and inter-stage. The change of the clearance calculated by the proposed abrasion equation agrees well with the experimental measurement. With the original and eroded pump geometries, the predicted ESP two-phase boosting pressure matches the measured pump curves, while the flow pattern transition from bubbly flow to intermittent flow is also captured.

Retracted] Steady‐State Flow Characteristics and End Clearance Optimization of Internal Gear Grease Pump

The internal gear grease pump is a mechanical device used for transfer of high viscosity fluid. The existing clearance between the end faces of the internal or external gear and the floating side plate might cause pump leakage during operation. In order to obtain the optimal end clearance of the internal gear grease pump, the rheological features of the lubricating lithium‐based grease for various temperatures are explored via rotating rheometer. Shear force and apparent viscosity are chosen as monitored experimental parameters. The experimental data is fitted to obtain grease rheological features at various temperatures. The end clearance flow field model and the leakage model are established. Fluent software is employed for solving the flow field model and exploring the effect of temperature, end clearance, and speed on grease leakage. Superior grease flow performance is observed with an increase in temperature, which makes it to easier for the grease to leak from the end clearance. With an increase in the end clearance and the working pressure of the pump, an increase in leakage is also observed. Furthermore, it is found that rotational speed also affects the pump leakage. The leakage mechanism is obtained by combining the rheological features of the grease at the end clearance. The mathematical model method is utilized to solve for the optimal value of the end clearance.

Condition monitoring of hydraulic transmission system with variable displacement axial piston pump and fixed displacement motor

Electrohydraulic power transmission systems are used in industries, automobiles, mining, aircraft, wind turbine system, machine too etc. To increase safety and reliability, it has become essential to know the health of the system components. So, fault detection of transmission system components has become necessary to reduce maintenance costs and prevent accidents. The leakage based health monitoring of a transmission system with variable displacement axial piston pump and fixed displacement hydraulic motor has been studied. In the present investigation, an axial piston pump of the transmission system has been analyzed for health monitoring. Mathematical modeling of the transmission system has been carried out in details. The wear between piston and cylinder bore has been considered as the primary cause of fault. The deviation of flow characteristic from normal flow due to increased leakage gap has been considered as a parameter for pump health monitoring. The performance of the transmission system has been studied due to variation pump leakage flow due to increase leakage gap. The present investigation has been carried out through a simulation study in Matlab Simulink environment.

Study on Speed and Clearance Optimization of All-Metal Progressing Cavity Pumps: Experiment and Simulation.

The stator and rotor of an all-metal screw pump are made of metal; so the matching method of the all-metal screw pump cannot be like that of the traditional rubber screw pump, and a certain gap must be reserved. If the gap is too small, it will affect the normal operation of the pump. If the gap is too large, the pump leakage will also become larger, affecting the efficiency of the pump. In this paper, the method of numerical simulation is used to optimize the gap of the pump. At the same time, it is found that the optimal gap of the pump is closely related to the viscosity of the fluid transported by the pump, and different viscosity fluids have different optimal speeds. The speed and clearance of the pump are optimized through numerical simulation to ensure high pump efficiency while taking into account energy saving and avoid the waste caused by excessively high speed. Finally, the relevant research results of numerical simulation are verified through experiments, and it is found that the experimental results are in good agreement with the simulation results, which also proves the accuracy of the numerical simulation results.

Dynamic hybrid scheduling of crude oil using structure adapted genetic algorithm for uncertainty of tank unavailability

The marine access refineries which supply nearly 15% of the global energy requirement constantly encounter uncertainty such as unit unavailability. This uncertainty often arises due to various factors such as pump failure or leakage in tanks. Crude oil scheduling under such uncertain conditions not only compromises the profitability but also compromises the operability of the plant. The study comprehensively analyses the advantages and disadvantages of using preventive and reactive scheduling approaches to handle a commonly encountered tank unavailability uncertainty. Based on these, a new hybrid approach which combines both the features from the preventive and reactive approach is developed. This study is important for solving scheduling optimization problem when uncertainty, non-linearity and multiple objectives are involved. For this purpose, three large-scale marine access refinery scheduling problems having a time horizon of 7, 7 and 14 days were studied.

Rational Matching Injection and Production Pump for Rod Pump Injection-Production Systems

Single-well injection-production technology is an effective measure for stabilizing oil and water control of the high water cut oilfield. The rod pump injection-production system uses gravity to separate oil and water under the well; throught two sets of rod pumps, the water is injected back into the formation and the concentrate is lifted to the ground. Because of the particularity of pump structure and the complexity of its working environment, the calculation method of 1st pump efficiency is quite different from that of conventional pumps. Reasonable matching of the production pump and the infection pump is the key factor to reduce the water cut of the produced liquid. Based on the gas PVT theory and the principle of mass conservation, the gas volume equation of the down well was established in this paper, and the calculation method of pump efficiency of single well injection-production system was established, which was verified and corrected by 17 field test wells, with considering the influence of pump stroke loss and leakage. Combining the established mechanical model of the single-well injection-production system, aimed at the lowest water cut of the produced fluid, we use the enumeration method to design a reasonable matching of the production-injection pump and suction parameters, and formulated the selection template of production-injection pump type and the selection of the working system for the rod pump single-well injection-production system. It provides a basis for the process design of the single-well injection-production system and provides technical support for the field application of the fourth generation oil recovery technology.

Thermal failure of 2/3 PCP in high temperature environment and optimization analysis of stator and rotor meshing parameters

Progressive cavity pump (PCP), which has problems such as invalid and leakage of rubber bushings, is employed in heavy oil field exploitation. In this paper, based on the thermal aging mechanical test of rubber and the fitting precision evaluation result of the constitutive model, the rubber constitutive model is optimized; the finite element model of PCP is established; the deformation law of stator bushing inner wall is quantitatively analyzed by numerical simulation method; and the phenomenon that temperature field distribution and thermal aging failure of stator rubber bushings is revealed. As results: The increase in well depth, which leads to the increase in the overall temperature of the stator bushing and the decrease of the thermal hysteresis, have greatest influence on the temperature of the stator bushing; the interference, which leads the maximum temperature increases first and then decreases, has a great impact on the thermal hysteresis; and the eccentricity have little effect on the thermal hysteresis of the stator bushing. The research results provide theoretical support to optimize stator and rotor parameter matching, recommending applicable working conditions, improving invalid of stator bushing, pump leakage and other issues.