Positive displacement compressors are essential in many engineering systems, from domestic to industrial applications. Many studies have been devoted to providing more insights into the workings and ...proposing solutions for performance improvements of these machines. This study aims to present a systematic review of published research on positive displacement compressors of various geometrical structures. This paper discusses the literature on compressor topics, including leakage, heat transfer, friction and lubrication, valve dynamics, port characteristics, and capacity control strategies. Moreover, the current status of the application of machine learning methods in positive displacement compressors is also discussed. The challenges and opportunities for future work are presented at the end of the paper.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
— Ball valve is a key fluid control equipment used extensively in oil and gas pipelines. The online detection and failure diagnosis of the internal leakage of the ball valve is of great significance ...to ensure the safety operation of natural gas transmission pipelines. This paper proposes a prediction method of the internal leakage rate and a diagnosis method of the failure mode of the buried pipeline ball valve based on valve cavity pressure detection. Firstly, the valve cavity pressure signal generated by the internal leakage of the ball valve is detected by the pressure sensor, and the valve cavity pressure signal is denoised by wavelet threshold denoising. Then, the back propagation (BP) neural network has the disadvantage of unstable learning ability, so the BP neural network is optimized by chaos sparrow search optimization algorithm (CSSOA-BP). Finally, the prediction model of the ball valve internal leakage rate and the diagnosis model of the ball valve failure mode are established by using CSSOA-BP neural network and the characteristic parameters of the valve cavity pressure signal. To verify the performance of the prediction model and the diagnosis model of CSSOA-BP neural network, the predictive results and diagnostic results are compared with those of the sparrow search algorithm optimization BP (SSA-BP) neural network and BP neural network. The experimental results show that the maximum prediction error of CSSOA-BP neural network is the smallest, which is 13.6%. The accuracy of the diagnostic results of CSSOA-BP neural network is the highest, which is 83.3%. It indicates that the proposed method can achieve better predictive results of the ball valve internal leakage rate and more accurate diagnostic results of the ball valve failure mode.
•A valve internal leakage detection device is developed to predict the internal leakage rate of the ball valve and diagnose the failure mode.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Valves on natural gas transmission pipelines are prone to internal leakage due to the influence of environment, transportation medium, misoperation and other factors. Therefore, timely and accurate ...detection of valve internal leakage is of great significance to the safe operation of the pipelines. As a non-destructive testing method, acoustic emission (AE) technology can be used for online detection of internal leakage of valve. In order to improve the accuracy of AE technology for detecting valve internal leakage, a ball valve internal leakage detection method based on acoustic emission technology is proposed. Firstly, the AE signal generated by the internal leakage of the ball valve is detected by AE sensor, and the AE signal is denoised by wavelet packet threshold denoising. Secondly, the characteristic parameters with high correlation with the internal leakage rate of the ball valve are selected by using the correlation coefficient method to form the sample feature set. Finally, the mathematical model between the internal leakage rate of the ball valve and the characteristic parameters is established by using the sparrow search algorithm to optimize the backpropagation neural network, and to predict the internal leakage rate of the ball valve. An experimental platform is built to verify that the proposed method can predict the internal leakage rate of the ball valve more accurately. It provides strong technical support for the safe operation of natural gas transmission pipelines.
•Wavelet packet threshold denoising is used to denoise the AE signal generated by the ball valve internal leakage.•The characteristic parameters of acoustic emission signal of the ball valve internal leakage is selected.•The experiments of the ball valve internal leakage detection are performed.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Abstract
Wear caused by contaminated oil or frequent reciprocating of the spool is the chief source of internal leakage in electro-hydraulic directional valves. It is difficult to detect the location ...and extent of the internal wear of directional valves because the hydraulic transmission works in a closed system. Therefore, this paper focuses on the internal leakage fault diagnosis caused by the wear based on intrinsic mode functions (IMFs) and weighted densely connected convolutional networks (WDenseNets), especially for electro-hydraulic directional valves. First, the empirical mode decomposition is performed in three subsections, respectively, and a series of IMF components are obtained correspondingly. Combined with the ReliefF feature selection algorithm, the optimal IMF component is selected. The weights assigned to the optimal IMF mode are normalized and then weighted as the subsequent diagnostic feature vector. Then, a WDenseNet inspired by squeeze-and-excitation networks is designed. Finally, the weighted optimal IMF component is used as the input for the WDenseNet for fault identification and classification. In addition, an experimental method to investigate the internal leakage faults associated with wear is developed. Theoretical and experimental results indicate that the fault diagnosis method can be used to identify the type of internal leakage and wear location in electro-hydraulic directional valves. Compared with other intelligent diagnosis methods, this method has higher diagnostic accuracy and better training stability. The effectiveness of the proposed method is also verified through an experimental study of the bearing fault in an axial piston pump.
Three-dimensional graph of standard deviation, leakage rate and pressure.
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•The experimental setup has been improved.•The experiments of the valve leakage detection are performed.•A ...multivariate mathematical model is established.•The probability distribution of leakage rate is obtained.•The modeling results of various methods are compared.
As a non-destructive testing method, acoustic emission (AE) technology can be used for internal leakage detection of valve on-line. In order to enhance the practicability of valve leakage detection by AE technology and realize valve leakage detection under various pressure conditions, the Gaussian process regression (GPR) is used to establish a multivariate mathematical model for describing the relationship between the characteristic of AE signal and the pressure, leakage rate. Based on the mathematical model built by GPR, we can not only determine leakage rate, but also obtain the probability distribution of leakage rate according to the predicted mean and standard deviation. In order to verify the modeling effect of GPR, the analysis results are compared with those of least squares linear regression, polynomial regression and support vector machine regression. For the HTS50-17 valve, the root mean square error of GPR is the smallest, which is 22.6948 ml/min. Moreover, the fitting degree of the GPR is greatest, which is 0.9653. On this basis, in order to improve the universality of valve leakage AE detection, the mixed experimental data of different flow coefficient of the same type valve are analyzed, and the root mean square error of GPR is the smallest, which is 23.0646 ml/min. It is indicated that GPR can achieve better results for the AE signal of valve leakage.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•The theoretical relationship between standard deviation and leakage rate is established.•The experiments of the valve leakage detection are performed.•The characteristic of valve leakage signal is ...extracted.•The linear relationship between the standard deviations and the leakage rate is obtained.•The influence of inlet pressure on the AE signal is analyzed.
The on-line detection technology of valves based on acoustic emission (AE) can effectively prevent production accidents and reduce the cost of equipment maintenance. However, the existing method for inner leakage detection of the valve cannot effectively detect the small leakage of the valve. Considering the fact that the critical leakage rate of the valve is less than 200 ml/min, the quantitative relationship between the characteristic quantity of the AE signal and the valve leakage rate is studied. The AE mechanism and basic characteristics of valve leakage are analyzed, and the theoretical relationship between standard deviation of AE signal of valve leakage and the leakage rate is determined. The experimental setup of valve leakage detection is built to carry out leakage detection experiments for several valves. The AE signal of valve leakage is processed to obtain its standard deviation. The relationship between the standard deviation and the leakage rate is fitted by least-squares method to establish their mathematical model. On this basis, the influence of inlet pressure on the AE signals of valve leakage is analyzed to lay a foundation for on-line detection of valve leakage under different working conditions.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
This brief presents design and experimental evaluation of a fixed-gain position controller for the class of electrohydrostatic actuator that is tolerant to actuator internal leakage fault and robust ...to various environmental stiffnesses and system parametric uncertainties. Built upon quantitative feedback theory, the proposed controller is designed according to stability and tracking specifications, considering actuator internal leakage as one of the uncertain phenomena. The controller is also augmented with a friction compensator to alleviate the parasitic effect of dry friction in the hydraulic cylinder and further improve the tracking performance. The ability of the augmented controller in maintaining the actuator position within acceptable response envelopes is demonstrated in both simulations and experiments. More specifically, the experimental results show that the steady-state error of various step input responses remains within 0.1 mm despite an internal leakage flow rate as high as 8.5 L/min.
•CFD technology is innovatively adopted to study the mechanism of the internal leakage area of hydraulic cylinder.•It was found that the energy features of the pressure signal can effectively detect ...the leakage fault by CFD technology.•The wavelet packet transform is adopted to extract energy features of pressure signal.•Combining with energy features and multivariate statistical theory, energy features fusion based hydraulic cylinder seal wear and internal leakage fault diagnosis method is proposed.•The proposed method has obvious advantage in terms of accuracy, small samples etc.
Internal leakage is one of themost common faultsinhydraulic cylinders, and seal wear is the main factor in internal leakage. However, it is difficult to detect seal wear and internal leakage in hydraulic cylinder using present approaches due to the complex hydraulic system. Therefore, an intelligent fault diagnosis method based energy features fusion is proposed to detect seal wear and internal leakage. First, computational fluid dynamics(CFD) technology was adopted to analyze the flow field in the internal leakage area of hydraulic cylinder, and it was found that energy features of pressure signal are related to internal leakage. Then, wavelet packet transform is appliedto extract energy features of pressure signal. Finally,energy featuresis decomposed into statistics by multivariate statisticstheory. Statistics are used to detect piston seal wear and internal leakage.The proposed method creativelystudiesseal wear and internal leakage from the perspective of flow field analysis, which doesnot requirealarge number of faultsamplesand complicated parametersoptimization. Experimental investigations are performed to validate the performance of the proposed approach. It is shown that the proposed method has much more robustness and accuracy than several classical fault diagnosis methods. The study does provide an effective way to detect seal wear and internal leakage in hydraulic cylinder.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Valve is a key control component in the oil and gas long-distance pipelines, and the internal leakage detection of the valve has important research significance and application value. In this paper, ...a new method for detecting internal leakage of the buried pipeline ball valve based on the pressure change of the valve cavity was proposed. Firstly, a simplified internal leakage model of ball valve was established, and the change law of valve cavity pressure under different openings was obtained by finite element simulation. Secondly, a set of detection device was designed based on the internal leakage principle of buried ball valve and an experimental platform of ball valve internal leakage was built. Then, the pressure signal of the ball valve cavity under different openings and different pressures was collected by the pressure sensor, and the relationship between the power source pressure, the opening and the time required for the valve cavity pressure to reach equilibrium was established. Finally, the internal leakage degree of ball valve was simulated by ball valve opening and the Back Propagation (BP) neural network was selected to invert the ball valve opening. The inversion results showed that the maximum relative error of the liquid and gas experiment were 13.4% and 19.1%, respectively. The inversion results also verified the feasibility of BP neural network in the inversion calculation of the internal leakage degree of the ball valve, which provides guidance for the study of the classification of the internal leakage degree. The experiment and inversion results show that the proposed method can realize the detection of the internal leakage of the buried ball valve, which provides application reference in practice.
•The detection principle of internal leakage of the buried ball valve is proposed.•The internal leakage detection device is designed and its applicability is validated by experiment.•The prediction of the internal leakage degree of the ball valve is realized by BP neural network.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•CNN is proposed to diagnose the valve internal leakage.•The collected multiple working condition datasets ensures the sample diversity.•CNN shows stronger learning ability than ANN, RBF-SVM and ...K-Medoids.•The maximum prediction error of CNN is less than 3%.•The paper has a guiding significance for diagnosing other fluid leakage.
The internal leakage signal of a valve is generally weak, and it is vulnerable to complex background noise. Due to the limitations of traditional internal leakage diagnosis methods and models, it is difficult to effectively evaluate a valve state under complex working conditions. Recognising this challenge, convolutional neural networks (CNN) is proposed to recognise valve internal leakage, which uses the power spectral density images of internal leakage and non-leakage signals under multiple working conditions as input. The experimental results show that the proposed models effectively recognise a internal leakage or non-leakage signal, and the maximum prediction error is less than 3%, which can be used a new method for valve leakage diagnosis.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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