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
Detecting a pipeline's abnormal status,which is typically a blockage and leakage accident,is important for the continuity and safety of mine backfill.The pipeline system for gravity-transport high-density backfill...Detecting a pipeline's abnormal status,which is typically a blockage and leakage accident,is important for the continuity and safety of mine backfill.The pipeline system for gravity-transport high-density backfill(GHB)is complex.Specifically designed,efficient,and accurate abnormal pipeline detection methods for GHB are rare.This work presents a long short-term memory-based deep learning(LSTM-DL)model for GHB pipeline blockage and leakage diagnosis.First,an industrial pipeline monitoring system was introduced using pressure and flow sensors.Second,blockage and leakage field experiments were designed to solve the problem of negative sample deficiency.The pipeline's statistical characteristics with different working statuses were analyzed to show their complexity.Third,the architecture of the LSTM-DL model was elaborated on and evaluated.Finally,the LSTM-DL model was compared with state-of-the-art(SOTA)learning algorithms.The results show that the backfilling cycle comprises multiple working phases and is intermittent.Although pressure and flow signals fluctuate stably in a normal cycle,their values are diverse in different cycles.Plugging causes a sudden change in interval signal features;leakage results in long variation duration and a wide fluctuation range.Among the SOTA models,the LSTM-DL model has the highest detection accuracy of98.31%for all states and the lowest misjudgment or false positive rate of 3.21%for blockage and leakage states.The proposed model can accurately recognize various pipeline statuses of complex GHB systems.展开更多
For solving the difficult problem of leakage detection in city gas pipelines, a method using acoustic technique based on instantaneous energy (IE) distribution and correlation analysis was proposed. Firstly, the basic...For solving the difficult problem of leakage detection in city gas pipelines, a method using acoustic technique based on instantaneous energy (IE) distribution and correlation analysis was proposed. Firstly, the basic theory of leakage detection and location was introduced. Then the physical relationship between instantaneous energy and structural state variation of a system was analyzed theoretically. With HILBERT-HUANG transformation (HHT), the instantaneous energy distribution feature of an unstable acoustic signal was obtained. According to the relative contribution method of the instantaneous energy, the noise in signal was eliminated effectively. Furthermore, in order to judge the leakage, the typical characteristic of the instantaneous energy of signal in the input and output end was discussed using correlative analysis. A number of experiments were carried out to classify the leakage from normal operations, and the results show that the leakages are successfully detected and the average recognition rate reaches 93.3% among three group samples. It is shown that the method using acoustic technique with IED and correlative analysis is effective and it may be referred in other pipelines.展开更多
In the traditional pipeline magnetic flux leakage(MFL)detection technology,circumferential or axial excitation is mainly used to excite the magnetic field of defects.However,the domestic and foreign pipeline detection...In the traditional pipeline magnetic flux leakage(MFL)detection technology,circumferential or axial excitation is mainly used to excite the magnetic field of defects.However,the domestic and foreign pipeline detection devices currently in operation are mainly axial excitation MFL detection tools,in which circumferential cracks can be clearly identified,but the detection sensitivity of axial cracks is not high,thus forming a detection blind zone.Therefore,a composite excitation multi-extension direction defect MFL detection method is proposed,which can realize the simultaneous detection of axial and circumferential defects.On the basis of the electromagnetic theory Maxwell equation and Biot Savart law,a mathematical model of circumferential and axial magnetization is firstly established.Then finite element simulation software is used to establish a model of a new type of magnetic flux leakage detection device,and a simulation analysis of crack detection in multiple extension directions is carried out.Finally,under the conditions of the relationship model between the change rate of leakage magnetic field and external excitation intensity under unsaturated magnetization and the multi-stage coil magnetization model,the sample vehicle towing experiment is carried out.The paper aims to analyze the feasibility and effectiveness of the new magnetic flux leakage detection device for detecting defects in different extension directions.Based on the final experimental results,the new composite excitation multi extension direction leakage magnetic field detector has a good detection effect for defects in the axial and circumferential extension directions.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
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
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.
基金financially supported by the China Postdoctoral Science Foundation (No.2021M690362)the National Natural Science Foundation of China (Nos.51974014 and U2034206)。
文摘Detecting a pipeline's abnormal status,which is typically a blockage and leakage accident,is important for the continuity and safety of mine backfill.The pipeline system for gravity-transport high-density backfill(GHB)is complex.Specifically designed,efficient,and accurate abnormal pipeline detection methods for GHB are rare.This work presents a long short-term memory-based deep learning(LSTM-DL)model for GHB pipeline blockage and leakage diagnosis.First,an industrial pipeline monitoring system was introduced using pressure and flow sensors.Second,blockage and leakage field experiments were designed to solve the problem of negative sample deficiency.The pipeline's statistical characteristics with different working statuses were analyzed to show their complexity.Third,the architecture of the LSTM-DL model was elaborated on and evaluated.Finally,the LSTM-DL model was compared with state-of-the-art(SOTA)learning algorithms.The results show that the backfilling cycle comprises multiple working phases and is intermittent.Although pressure and flow signals fluctuate stably in a normal cycle,their values are diverse in different cycles.Plugging causes a sudden change in interval signal features;leakage results in long variation duration and a wide fluctuation range.Among the SOTA models,the LSTM-DL model has the highest detection accuracy of98.31%for all states and the lowest misjudgment or false positive rate of 3.21%for blockage and leakage states.The proposed model can accurately recognize various pipeline statuses of complex GHB systems.
基金Project(51004005) supported by the National Natural Science Foundation of ChinaProject supported by Open Research Fund Program of Beijing Engineering Research Center of Monitoring for Construction Safety (Beijing University of Civil Engineering and Architecture), China
文摘For solving the difficult problem of leakage detection in city gas pipelines, a method using acoustic technique based on instantaneous energy (IE) distribution and correlation analysis was proposed. Firstly, the basic theory of leakage detection and location was introduced. Then the physical relationship between instantaneous energy and structural state variation of a system was analyzed theoretically. With HILBERT-HUANG transformation (HHT), the instantaneous energy distribution feature of an unstable acoustic signal was obtained. According to the relative contribution method of the instantaneous energy, the noise in signal was eliminated effectively. Furthermore, in order to judge the leakage, the typical characteristic of the instantaneous energy of signal in the input and output end was discussed using correlative analysis. A number of experiments were carried out to classify the leakage from normal operations, and the results show that the leakages are successfully detected and the average recognition rate reaches 93.3% among three group samples. It is shown that the method using acoustic technique with IED and correlative analysis is effective and it may be referred in other pipelines.
基金National Natural Science Foundation of China(No.51804267)State Key Laboratory of Petroleum Resources and Prospecting,China University of Petroleum,Beijing(No.PRP/open-1610)。
文摘In the traditional pipeline magnetic flux leakage(MFL)detection technology,circumferential or axial excitation is mainly used to excite the magnetic field of defects.However,the domestic and foreign pipeline detection devices currently in operation are mainly axial excitation MFL detection tools,in which circumferential cracks can be clearly identified,but the detection sensitivity of axial cracks is not high,thus forming a detection blind zone.Therefore,a composite excitation multi-extension direction defect MFL detection method is proposed,which can realize the simultaneous detection of axial and circumferential defects.On the basis of the electromagnetic theory Maxwell equation and Biot Savart law,a mathematical model of circumferential and axial magnetization is firstly established.Then finite element simulation software is used to establish a model of a new type of magnetic flux leakage detection device,and a simulation analysis of crack detection in multiple extension directions is carried out.Finally,under the conditions of the relationship model between the change rate of leakage magnetic field and external excitation intensity under unsaturated magnetization and the multi-stage coil magnetization model,the sample vehicle towing experiment is carried out.The paper aims to analyze the feasibility and effectiveness of the new magnetic flux leakage detection device for detecting defects in different extension directions.Based on the final experimental results,the new composite excitation multi extension direction leakage magnetic field detector has a good detection effect for defects in the axial and circumferential extension directions.
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.
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.
