Accurate detection of pipeline leakage is essential to maintain the safety of pipeline transportation.Recently,deep learning(DL)has emerged as a promising tool for pipeline leakage detection(PLD).However,most existing...Accurate detection of pipeline leakage is essential to maintain the safety of pipeline transportation.Recently,deep learning(DL)has emerged as a promising tool for pipeline leakage detection(PLD).However,most existing DL methods have difficulty in achieving good performance in identifying leakage types due to the complex time dynamics of pipeline data.On the other hand,the initial parameter selection in the detection model is generally random,which may lead to unstable recognition performance.For this reason,a hybrid DL framework referred to as parameter-optimized recurrent attention network(PRAN)is presented in this paper to improve the accuracy of PLD.First,a parameter-optimized long short-term memory(LSTM)network is introduced to extract effective and robust features,which exploits a particle swarm optimization(PSO)algorithm with cross-entropy fitness function to search for globally optimal parameters.With this framework,the learning representation capability of the model is improved and the convergence rate is accelerated.Moreover,an anomaly-attention mechanism(AM)is proposed to discover class discriminative information by weighting the hidden states,which contributes to amplifying the normalabnormal distinguishable discrepancy,further improving the accuracy of PLD.After that,the proposed PRAN not only implements the adaptive optimization of network parameters,but also enlarges the contribution of normal-abnormal discrepancy,thereby overcoming the drawbacks of instability and poor generalization.Finally,the experimental results demonstrate the effectiveness and superiority of the proposed PRAN for PLD.展开更多
This letter investigates the wavelet transform, as well as the principle and the method of the noise reduction based on wavelet transform, it chooses the threshold noise reduction, and discusses in detail the principl...This letter investigates the wavelet transform, as well as the principle and the method of the noise reduction based on wavelet transform, it chooses the threshold noise reduction, and discusses in detail the principles, features and design steps of the threshold method. Rigrsure, heursure, sqtwolog and minimization four kinds of threshold selection method are compared qualitatively, and quantitatively. The wavelet analysis toolbox of MATLAB helps to realize the computer simulation of the signal noise reduction. The graphics and calculated standard deviation of the various threshold noise reductions show that, when dealing with the actual pressure signal of the oil pipeline leakage, sqtwolog threshold selection method can effectively remove the noise. Aiming to the pressure signal of the oil pipeline leakage, the best choice is the wavelet threshold noise reduction with sqtwolog threshold. The leakage point is close to the actual position, with the relative error of less than 1%.展开更多
We conduct simulation study on the typical influencing factors for negative pressure wave in liquid pipeline leakage. We first analyse the liquid pipeline leakage detection based on negative pressure wave method and o...We conduct simulation study on the typical influencing factors for negative pressure wave in liquid pipeline leakage. We first analyse the liquid pipeline leakage detection based on negative pressure wave method and obtain the essential simulation parameters. Then based on the physical model of pipeline and by introducing leakage boundary condition, we simulate the variation of pressure and flow rate in pipeline after leakage, the influence of leakage scale and leakage position on the pressure and flow rate in the pipeline. The results show that the leakage scale mainly influences the amplitude of negative pressure wave, and that the leakage position inflnenees both the amplitude and the shape of the curves of negative pressure wave.展开更多
With the introduction of various carbon reduction policies around the world,hydrogen energy,as a kind of clean energy with zero carbon emission,has attracted much attention.The safe and economical transportation of hy...With the introduction of various carbon reduction policies around the world,hydrogen energy,as a kind of clean energy with zero carbon emission,has attracted much attention.The safe and economical transportation of hydrogen is of great significance to the development of hydrogen energy industries.Utilizing natural gas pipelines to transport hydrogen is considered to be an efficient and economical way.However,hydrogen has a higher risk of leakage due to its strong diffusion capacity and lower explosive limit than conventional natural gas.Therefore,it is of great significance to study the leakage and diffusion law of hydrogen-enriched natural gas(HENG)pipelines for the safe transportation of hydrogen energy.In this study,the leakage and diffusion characteristics of urban buried HENG pipelines are investigated numerically,and the dangerous degree of leakage is analyzed based on the time and area when the gas concentration reaches the lower explosive limit.The influences of hydrogen blending ratio(HBR),operating pressure,leakage hole size and direction,as well as soil type on the leakage and diffusion law of HENG are analyzed.Results show that the hydrogen mixing is not the key factor in increasing the degree of risk after gas leakage for urban buried HENG pipelines.When the HBR is 5%,10%,15% and 20%,the corresponding first dangerous time is 1053,1041,1019 and 998 s,respectively.Thiswork is expected to provide a valuable reference for the safe operation and risk prevention of HENG pipelines in the future.展开更多
Water leakage in drinking water distribution systems is a serious problem for many cities and a huge challenge for water utilities.An integrated system for the detection,early warning,and control of pipeline leakage h...Water leakage in drinking water distribution systems is a serious problem for many cities and a huge challenge for water utilities.An integrated system for the detection,early warning,and control of pipeline leakage has been developed and successfully used to manage the pipeline networks in selected areas of Beijing.A method based on the geographic information system has been proposed to quickly and automatically optimize the layout of the instruments which detect leaks.Methods are also proposed to estimate the probability of each pipe segment leaking (on the basis of historic leakage data),and to assist in locating the leakage points (based on leakage signals).The district metering area (DMA) strategy is used.Guidelines and a flowchart for establishing a DMA to manage the large-scale looped networks in Beijing are proposed.These different functions have been implemented into a central software system to simplify the day-to-day use of the system.In 2007 the system detected 102 non-obvious leakages (i.e.,14.2% of the total detected in Beijing) in the selected areas,which was estimated to save a total volume of 2,385,000 m 3 of water.These results indicate the feasibility,efficiency and wider applicability of this system.展开更多
Due to the rapid development in the petroleum industry,the leakage detection of crude oil transmission pipes has become an increasingly crucial issue.At present,oil plants at home and abroad mostly use manual inspecti...Due to the rapid development in the petroleum industry,the leakage detection of crude oil transmission pipes has become an increasingly crucial issue.At present,oil plants at home and abroad mostly use manual inspection method for detection.This traditional method is not only inefficient but also labor-intensive.The present paper proposes a novel convolutional neural network(CNN)architecture for automatic leakage level assessment of crude oil transmission pipes.An experimental setup is developed,where a visible camera and a thermal imaging camera are used to collect image data and analyze various leakage conditions.Specifically,images are collected from various pipes with no leaking and different leaking states.Apart from images from existing pipelines,images are collected from the experimental setup with different types of joints to simulate leakage conditions in the real world.The main contributions of the present paper are,developing a convolutional neural network to classify the information in red-green-blue(RGB)and thermal images,development of the experimental setup,conducting leakage experiments,and analyzing the data using the developed approach.By successfully combining the two types of images,the proposed method is able to achieve a higher classification accuracy,compared to other methods that use RGB images or thermal images alone.Especially,compared with the method that uses thermal images only,the accuracy increases from about 91%to over 96%.展开更多
Because pipeline has large pipe diameter, large throughout and high pressure, once pipeline leakage accident happens, the damage is quite serious. In addition, pipeline leakage accident caused by man-made drilling oil...Because pipeline has large pipe diameter, large throughout and high pressure, once pipeline leakage accident happens, the damage is quite serious. In addition, pipeline leakage accident caused by man-made drilling oil stolen every year results in huge economic losses on oilfield. Therefore, a real-time and accurate pipeline leak detection and location system not only can effectively decrease leakage loss and reduce the waste of manpower and material resources in patrolling work, but also is conductive to the management of oil pipeline and improvement of economic efficiency of enterprise. The paper determines leak detection and location project giving priority to negative pressure wave and supplemented by flow parameter analysis. The method not only can judge the accidence of leakage timely and accurately, but also can effectively avoid leakage false alarm caused by start or stop pumps in pipeline.展开更多
As an important part of lifeline engineering in the development and utilization of marine resources, the submarine fluid-filled pipeline is a complex coupling system which is subjected to both internal and external fl...As an important part of lifeline engineering in the development and utilization of marine resources, the submarine fluid-filled pipeline is a complex coupling system which is subjected to both internal and external flow fields. By utilizing Kennard's shell equations and combining with Helmholtz equations of flow field, the coupling equations of submarine fluid-filled pipeline for n=0 axisymmetrical wave motion are set up. Analytical expressions of wave speed are obtained for both s=1 and s=2 waves, which correspond to a fluid-dominated wave and an axial shell wave, respectively. The numerical results for wave speed and wave attenuation are obtained and discussed subsequently. It shows that the frequency depends on phase velocity, and the attenuation of this mode depends strongly on material parameters of the pipe and the internal and the external fluid fields. The characteristics of PVC pipe are studied for a comparison. The effects of shell thickness/radius ratio and density of the contained fluid on the model are also discussed. The study provides a theoretical basis and helps to accurately predict the situation of submarine pipelines, which also has practical application prospect in the field of pipeline leakage detection.展开更多
基金This work was supported in part by the National Natural Science Foundation of China(U21A2019,61873058),Hainan Province Science and Technology Special Fund of China(ZDYF2022SHFZ105)the Alexander von Humboldt Foundation of Germany.
文摘Accurate detection of pipeline leakage is essential to maintain the safety of pipeline transportation.Recently,deep learning(DL)has emerged as a promising tool for pipeline leakage detection(PLD).However,most existing DL methods have difficulty in achieving good performance in identifying leakage types due to the complex time dynamics of pipeline data.On the other hand,the initial parameter selection in the detection model is generally random,which may lead to unstable recognition performance.For this reason,a hybrid DL framework referred to as parameter-optimized recurrent attention network(PRAN)is presented in this paper to improve the accuracy of PLD.First,a parameter-optimized long short-term memory(LSTM)network is introduced to extract effective and robust features,which exploits a particle swarm optimization(PSO)algorithm with cross-entropy fitness function to search for globally optimal parameters.With this framework,the learning representation capability of the model is improved and the convergence rate is accelerated.Moreover,an anomaly-attention mechanism(AM)is proposed to discover class discriminative information by weighting the hidden states,which contributes to amplifying the normalabnormal distinguishable discrepancy,further improving the accuracy of PLD.After that,the proposed PRAN not only implements the adaptive optimization of network parameters,but also enlarges the contribution of normal-abnormal discrepancy,thereby overcoming the drawbacks of instability and poor generalization.Finally,the experimental results demonstrate the effectiveness and superiority of the proposed PRAN for PLD.
文摘This letter investigates the wavelet transform, as well as the principle and the method of the noise reduction based on wavelet transform, it chooses the threshold noise reduction, and discusses in detail the principles, features and design steps of the threshold method. Rigrsure, heursure, sqtwolog and minimization four kinds of threshold selection method are compared qualitatively, and quantitatively. The wavelet analysis toolbox of MATLAB helps to realize the computer simulation of the signal noise reduction. The graphics and calculated standard deviation of the various threshold noise reductions show that, when dealing with the actual pressure signal of the oil pipeline leakage, sqtwolog threshold selection method can effectively remove the noise. Aiming to the pressure signal of the oil pipeline leakage, the best choice is the wavelet threshold noise reduction with sqtwolog threshold. The leakage point is close to the actual position, with the relative error of less than 1%.
文摘We conduct simulation study on the typical influencing factors for negative pressure wave in liquid pipeline leakage. We first analyse the liquid pipeline leakage detection based on negative pressure wave method and obtain the essential simulation parameters. Then based on the physical model of pipeline and by introducing leakage boundary condition, we simulate the variation of pressure and flow rate in pipeline after leakage, the influence of leakage scale and leakage position on the pressure and flow rate in the pipeline. The results show that the leakage scale mainly influences the amplitude of negative pressure wave, and that the leakage position inflnenees both the amplitude and the shape of the curves of negative pressure wave.
基金supported by the National Key R&D Program of China (No.2021YFB4001602),the National Natural Science Foundation of China (No.51904031)the Award Cultivation Foundation from Beijing Institute of Petrochemical Technology (No.BIPTACF-002).
文摘With the introduction of various carbon reduction policies around the world,hydrogen energy,as a kind of clean energy with zero carbon emission,has attracted much attention.The safe and economical transportation of hydrogen is of great significance to the development of hydrogen energy industries.Utilizing natural gas pipelines to transport hydrogen is considered to be an efficient and economical way.However,hydrogen has a higher risk of leakage due to its strong diffusion capacity and lower explosive limit than conventional natural gas.Therefore,it is of great significance to study the leakage and diffusion law of hydrogen-enriched natural gas(HENG)pipelines for the safe transportation of hydrogen energy.In this study,the leakage and diffusion characteristics of urban buried HENG pipelines are investigated numerically,and the dangerous degree of leakage is analyzed based on the time and area when the gas concentration reaches the lower explosive limit.The influences of hydrogen blending ratio(HBR),operating pressure,leakage hole size and direction,as well as soil type on the leakage and diffusion law of HENG are analyzed.Results show that the hydrogen mixing is not the key factor in increasing the degree of risk after gas leakage for urban buried HENG pipelines.When the HBR is 5%,10%,15% and 20%,the corresponding first dangerous time is 1053,1041,1019 and 998 s,respectively.Thiswork is expected to provide a valuable reference for the safe operation and risk prevention of HENG pipelines in the future.
基金supported by the National Eleventh-Five Year Research Program of China(No.2006BAB17B03)
文摘Water leakage in drinking water distribution systems is a serious problem for many cities and a huge challenge for water utilities.An integrated system for the detection,early warning,and control of pipeline leakage has been developed and successfully used to manage the pipeline networks in selected areas of Beijing.A method based on the geographic information system has been proposed to quickly and automatically optimize the layout of the instruments which detect leaks.Methods are also proposed to estimate the probability of each pipe segment leaking (on the basis of historic leakage data),and to assist in locating the leakage points (based on leakage signals).The district metering area (DMA) strategy is used.Guidelines and a flowchart for establishing a DMA to manage the large-scale looped networks in Beijing are proposed.These different functions have been implemented into a central software system to simplify the day-to-day use of the system.In 2007 the system detected 102 non-obvious leakages (i.e.,14.2% of the total detected in Beijing) in the selected areas,which was estimated to save a total volume of 2,385,000 m 3 of water.These results indicate the feasibility,efficiency and wider applicability of this system.
文摘Due to the rapid development in the petroleum industry,the leakage detection of crude oil transmission pipes has become an increasingly crucial issue.At present,oil plants at home and abroad mostly use manual inspection method for detection.This traditional method is not only inefficient but also labor-intensive.The present paper proposes a novel convolutional neural network(CNN)architecture for automatic leakage level assessment of crude oil transmission pipes.An experimental setup is developed,where a visible camera and a thermal imaging camera are used to collect image data and analyze various leakage conditions.Specifically,images are collected from various pipes with no leaking and different leaking states.Apart from images from existing pipelines,images are collected from the experimental setup with different types of joints to simulate leakage conditions in the real world.The main contributions of the present paper are,developing a convolutional neural network to classify the information in red-green-blue(RGB)and thermal images,development of the experimental setup,conducting leakage experiments,and analyzing the data using the developed approach.By successfully combining the two types of images,the proposed method is able to achieve a higher classification accuracy,compared to other methods that use RGB images or thermal images alone.Especially,compared with the method that uses thermal images only,the accuracy increases from about 91%to over 96%.
文摘Because pipeline has large pipe diameter, large throughout and high pressure, once pipeline leakage accident happens, the damage is quite serious. In addition, pipeline leakage accident caused by man-made drilling oil stolen every year results in huge economic losses on oilfield. Therefore, a real-time and accurate pipeline leak detection and location system not only can effectively decrease leakage loss and reduce the waste of manpower and material resources in patrolling work, but also is conductive to the management of oil pipeline and improvement of economic efficiency of enterprise. The paper determines leak detection and location project giving priority to negative pressure wave and supplemented by flow parameter analysis. The method not only can judge the accidence of leakage timely and accurately, but also can effectively avoid leakage false alarm caused by start or stop pumps in pipeline.
基金financially supported by the National Natural Science Foundation of China(Grant No.50905036)
文摘As an important part of lifeline engineering in the development and utilization of marine resources, the submarine fluid-filled pipeline is a complex coupling system which is subjected to both internal and external flow fields. By utilizing Kennard's shell equations and combining with Helmholtz equations of flow field, the coupling equations of submarine fluid-filled pipeline for n=0 axisymmetrical wave motion are set up. Analytical expressions of wave speed are obtained for both s=1 and s=2 waves, which correspond to a fluid-dominated wave and an axial shell wave, respectively. The numerical results for wave speed and wave attenuation are obtained and discussed subsequently. It shows that the frequency depends on phase velocity, and the attenuation of this mode depends strongly on material parameters of the pipe and the internal and the external fluid fields. The characteristics of PVC pipe are studied for a comparison. The effects of shell thickness/radius ratio and density of the contained fluid on the model are also discussed. The study provides a theoretical basis and helps to accurately predict the situation of submarine pipelines, which also has practical application prospect in the field of pipeline leakage detection.