技术领域technical field
本发明涉及一种输气管道泄露检测系统，尤其涉及一种基于声波法的输气管道泄露检测系统及检测方法。The invention relates to a gas transmission pipeline leakage detection system, in particular to a gas transmission pipeline leakage detection system and detection method based on an acoustic wave method.
背景技术Background technique
管道输送具有成本低，运输量大，运输稳定，自动化程度高，可在较恶劣环境下连续输送等诸多优点，尤其适用于石油天然气等易燃易爆流体的长距离输送。Pipeline transportation has many advantages such as low cost, large transportation volume, stable transportation, high degree of automation, and continuous transportation in harsh environments. It is especially suitable for long-distance transportation of flammable and explosive fluids such as oil and gas.
近年来，世界各国对能源的需求量大大增加，气体管线的建设进入快速发展阶段，自动化水平迅速提高，其中，我国气体管道建设总里程已超过8万千米，但是由于管道设备的老化，地理条件的变化(如滑坡、地震等)以及人为的原因(如施工、盗油等)，管道泄漏事故经常发生。管道一旦发生泄漏，不仅会带来因流体流失而造成的直接经济损失和环境污染，严重情况下，还可能发生爆炸和引起火灾，甚至造成人员伤亡。In recent years, the demand for energy in countries around the world has greatly increased, the construction of gas pipelines has entered a stage of rapid development, and the level of automation has increased rapidly. Among them, the total mileage of gas pipeline construction in my country has exceeded 80,000 kilometers. Pipeline leakage accidents often occur due to changes in environmental conditions (such as landslides, earthquakes, etc.) and man-made reasons (such as construction, oil theft, etc.). Once the pipeline leaks, it will not only bring direct economic losses and environmental pollution caused by fluid loss, but in severe cases, explosions and fires may occur, and even casualties may occur.
目前，国内外广泛使用的实时泄漏检测与定位系统大多采用基于负压波的泄漏检测和定位方法，实践表明，这种方法对明显的突发性泄漏的检测与定位具有较好的效果，在实际中发挥了比较好的作用，取得了明显的经济效益和社会效益，但这类方法存在固有的不足：At present, most of the real-time leak detection and location systems widely used at home and abroad use the leak detection and location method based on negative pressure waves. Practice shows that this method has a good effect on the detection and location of obvious sudden leaks. In practice, it has played a relatively good role and achieved obvious economic and social benefits, but this type of method has inherent shortcomings:
(1)、对明显的突发性泄漏的检测与定位效果比较好，对缓变的小泄漏漏报比较多，定位精度比较差；(1) The detection and positioning effect of obvious sudden leakage is relatively good, and there are many missed reports for slowly changing small leakage, and the positioning accuracy is relatively poor;
(2)、对工况平稳的长距离输送管道来说效果比较好，对工况扰动频繁的管道来说，误报比较多；(2) The effect is better for long-distance transmission pipelines with stable working conditions, and more false alarms for pipelines with frequent disturbances in working conditions;
(3)、对性能接近不可压缩流体的液体管道来说效果比较好，对气体管道的泄漏检测与定位效果比较差，甚至基本不可行。(3) The effect is relatively good for liquid pipelines whose performance is close to that of incompressible fluids, and the effect of leak detection and location for gas pipelines is relatively poor, or even basically unfeasible.
而且现有的气体管道泄漏检测系统，普遍采用的是固定式检测站的模式，使用不便。从泄漏被检测出来到采取措施的时间比较长，不能有效的抓住处理泄漏事故的最佳时机。Moreover, the existing gas pipeline leakage detection system generally adopts the mode of a fixed detection station, which is inconvenient to use. It takes a long time from the detection of the leak to the taking of measures, and it is impossible to effectively seize the best time to deal with the leak.
由此可见，现有技术有待于进一步的改进和提高。This shows that the prior art needs to be further improved and improved.
发明内容Contents of the invention
本发明为避免上述现有技术存在的不足之处，提供了一种实时性强、灵敏度高、响应速度快且可实现异地检测的基于声波法的输气管道泄露检测系统。In order to avoid the shortcomings of the above-mentioned prior art, the present invention provides a gas pipeline leakage detection system based on the acoustic wave method, which has strong real-time performance, high sensitivity, fast response speed and can realize remote detection.
气体管道发生泄露时，在管道内外压差的作用下，气体从泄漏口喷射而出，产生强烈的速度和压力脉动，从而产生以四极子声源为主的气动噪声。气体管道泄漏时产生的声波信号，一部分沿着管壁传播，但是由于管壁和外部介质(如土壤、空气等)的互相作用，这部分声波在传播一定距离后就基本上衰减掉，另一部分则沿着管道内的气体传播，不易受到外界环境的干扰。本发明即基于气体管道泄漏时声波信号的传递特点所设计。When a gas pipeline leaks, under the action of the pressure difference between the inside and outside of the pipeline, the gas is ejected from the leakage port, resulting in strong velocity and pressure pulsations, resulting in aerodynamic noise dominated by quadrupole sound sources. Part of the acoustic wave signal generated when the gas pipeline leaks along the pipe wall, but due to the interaction between the pipe wall and the external medium (such as soil, air, etc.), this part of the sound wave is basically attenuated after a certain distance, and the other part Then it propagates along the gas in the pipeline and is not easily disturbed by the external environment. The present invention is designed based on the transmission characteristics of the acoustic wave signal when the gas pipeline leaks.
本发明所采用的技术方案为：The technical scheme adopted in the present invention is:
一种基于声波法的输气管道泄露检测系统，包括用户终端、云端服务器及沿输气管道设置的若干个检测段，每个检测段处均设置有两个低频声波传感器、两个温度传感器、两个密度传感器、两个压力传感器及两个数字化网络传输仪，其中，A gas pipeline leakage detection system based on the acoustic wave method, including a user terminal, a cloud server, and several detection sections arranged along the gas pipeline, each detection section is equipped with two low-frequency acoustic sensors, two temperature sensors, Two density sensors, two pressure sensors and two digital network transmitters, wherein,
每个检测段内的两个低频声波传感器分别设置在该检测段内的输气管道的两端，各低频声波传感器用于采集该检测段内输气管道中的低频声波信号，并将采集到的低频声波信号进行放大和初步滤波后输送给与该低频声波传感器处于同一检测位置的数字化网络传输仪；The two low-frequency acoustic wave sensors in each detection section are respectively arranged at the two ends of the gas transmission pipeline in the detection section, and each low-frequency acoustic wave sensor is used to collect the low-frequency acoustic wave signals in the gas transmission pipeline in the detection section, and collect the The low-frequency acoustic wave signal is amplified and preliminarily filtered, and then sent to the digital network transmitter at the same detection position as the low-frequency acoustic wave sensor;
每个检测段内的两个温度传感器均设置在该检测段内的输气管道内且分别位于该段输气管道的两侧，各温度传感器均用于测定该检测段内输气管道中介质的温度，并将检测到的温度信号传输给与该温度传感器处于同一检测位置的数字化网络传输仪；The two temperature sensors in each detection section are set in the gas transmission pipeline in the detection section and are respectively located on both sides of the gas transmission pipeline in this section. Each temperature sensor is used to measure the medium in the gas transmission pipeline in the detection section. temperature, and transmit the detected temperature signal to the digital network transmitter at the same detection position as the temperature sensor;
每个检测段内的两个密度传感器均设置在该检测段内的输气管道内且分别位于该段输气管道的两侧，各密度传感器均用于测定该检测段内输气管道中介质的密度，并将检测到的密度信号传输给与该密度传感器处于同一检测位置的数字化网络传输仪；The two density sensors in each detection section are set in the gas pipeline in the detection section and are respectively located on both sides of the gas pipeline in this section. Each density sensor is used to measure the medium in the gas pipeline in the detection section. density, and transmit the detected density signal to the digital network transmitter at the same detection position as the density sensor;
每个检测段内的两个压力传感器均设置在该检测段内的输气管道内且分别位于该段输气管道的两侧，各压力传感器均用于测定该检测段内输气管道中介质的压力，并将检测到的压力信号传输给与该压力传感器处于同一检测位置的数字化网络传输仪；The two pressure sensors in each detection section are set in the gas pipeline in the detection section and are respectively located on both sides of the gas pipeline in this section. Each pressure sensor is used to measure the medium in the gas pipeline in the detection section. pressure, and transmit the detected pressure signal to the digital network transmitter at the same detection position as the pressure sensor;
数字化网络传输仪，用于将收集到的低频声波信号、温度信号、密度信号、压力信号转换为数字信号，同时将数据保存到本地并在网络畅通时及时将最新数据传输到云端服务器；The digital network transmission instrument is used to convert the collected low-frequency sound wave signals, temperature signals, density signals, and pressure signals into digital signals, and at the same time save the data locally and transmit the latest data to the cloud server in time when the network is unblocked;
云端服务器，用于对从数字化网络传输仪传来的数字信号进行信号处理，并利用FSVM算法进行模式识别，通过模式识别提取并分析输气管道泄漏时的低频声波信号的特征量，判断泄露是否发生并确定泄漏口的大小和形状；同时对泄漏时两个低频声波传感器接收到的低频声波信号进行互相关分析，并结合输气管道内的声速，对泄露口的位置实现定位；最终云端服务器将处理完的数据通过无线网络传送给用户终端；The cloud server is used to process the digital signal transmitted from the digital network transmission instrument, and use the FSVM algorithm to perform pattern recognition, extract and analyze the characteristic quantity of the low-frequency sound wave signal when the gas pipeline leaks through pattern recognition, and judge whether the leak is Generate and determine the size and shape of the leak; at the same time, perform cross-correlation analysis on the low-frequency acoustic signals received by the two low-frequency acoustic sensors during the leak, and combine the sound velocity in the gas pipeline to locate the leak; finally, the cloud server Send the processed data to the user terminal through the wireless network;
用户终端，根据云端服务器传来的数据对输气管道的运行状况进行判断，若发生泄露，则发出报警信号，并给出输气管道泄漏口的位置以及泄漏口的大小和形状。The user terminal judges the operation status of the gas pipeline according to the data sent from the cloud server. If a leak occurs, an alarm signal is sent, and the location, size and shape of the leak of the gas pipeline are given.
所述云端服务器中设置有数据库，所述数据库中存储有若干个不同泄漏口的大小数据和形状数据及其所对应的泄漏声波信号的特征量，所述泄漏声波信号的特征量是根据理论分析及实验测定所得到的；当实际应用过程中的输气管道发生泄漏时，云端服务器通过模式识别提取出泄漏时的低频声波信号的特征量，并根据此低频声波信号的特征量对数据库中现有的数据进行插值与拟合，进而确定泄漏口的大小和形状，最后人为地测量出该泄漏口的实际大小和形状，并将该测量得到的数据与系统判断出来的数据进行比较，将两者的偏差保存到数据库中；系统内植具有机器自学习功能的算法，可根据上述偏差对数据库中泄漏声波信号的特征量及其所对应的泄漏口的大小数据和形状数据进行修正；当再次发生泄漏时，系统就会根据数据库中新的数据选择新的插值和拟合方法，确定泄漏口的大小和形状，再次人为地测量出此时泄漏口的实际大小和形状，并将该测量得到的数据与系统判断出来的数据进行比较，将两者的偏差保存到数据库中；如此反复进行，判断出泄漏口的大小和形状。The cloud server is provided with a database, and the database stores the size data and shape data of several different leak openings and the characteristic quantities of the corresponding leakage acoustic wave signals, and the characteristic quantities of the leakage acoustic wave signals are based on theoretical analysis. and experimental measurements; when the gas pipeline leaks in the actual application process, the cloud server extracts the characteristic quantity of the low-frequency sound wave signal at the time of leakage through pattern recognition, and compares the characteristics of the low-frequency sound wave signal in the database according to the characteristic quantity of the low-frequency sound wave signal. Some data are interpolated and fitted to determine the size and shape of the leak, and finally the actual size and shape of the leak is measured artificially, and the measured data is compared with the data judged by the system. The deviation of the tester is saved in the database; the algorithm with machine self-learning function is built in the system, and the feature quantity of the leaked acoustic signal in the database and the size data and shape data of the corresponding leak port can be corrected according to the above deviation; When a leak occurs, the system will select a new interpolation and fitting method based on the new data in the database to determine the size and shape of the leak, and again artificially measure the actual size and shape of the leak at this time, and obtain The data is compared with the data judged by the system, and the deviation between the two is saved in the database; so repeated, the size and shape of the leak can be judged.
将每个检测段内的其中一个低频声波传感器选作基点，作为基点低频声波传感器，与之相对应的另一低频声波传感器作为非基点低频声波传感器；将基点低频声波传感器接收到泄漏声波信号的时间设置为t0，非基点低频声波传感器接收到泄漏声波信号的时间设置为t1，One of the low-frequency acoustic wave sensors in each detection section is selected as the base point as the base point low-frequency acoustic wave sensor, and the other corresponding low-frequency acoustic wave sensor is used as a non-base point low-frequency acoustic wave sensor; the base point low-frequency acoustic wave sensor receives the leakage acoustic wave signal The time is set as t0 , the time when the non-base point low-frequency acoustic wave sensor receives the leakage acoustic wave signal is set as t1 ,
所述泄露口距离发生泄露的检测段内的基点低频声波传感器的位置由下式确定：The position of the base point low-frequency acoustic wave sensor in the detection section where the leakage occurs from the leakage port is determined by the following formula:
其中，x为泄漏口距离发生泄露的检测段内的基点低频声波传感器的距离，l为每个检测段内的基点低频声波传感器与非基点低频声波传感器间的距离，v为云端服务器对声速数据进行插值和拟合后所获得的输气管道泄漏时的声速，△t为发生泄露的检测段内的基点、非基点低频声波传感器接收到输气管道泄露时的低频声波信号的时间差；所述泄漏口的位置最终由发生泄露的检测段内的基点低频声波传感器的位置和上述计算所得距离x确定；Among them, x is the distance between the leak opening and the base point low-frequency acoustic wave sensor in the detection section where leakage occurs, l is the distance between the base point low-frequency acoustic wave sensor and the non-base point low-frequency acoustic wave sensor in each detection section, and v is the sound velocity data of the cloud server The sound velocity when the gas pipeline leaks obtained after interpolation and fitting, Δt is the time difference between the base point and non-base point low-frequency acoustic wave sensor receiving the low-frequency acoustic signal when the gas pipeline leaks in the detection section where the leak occurs; The position of the leakage port is finally determined by the position of the base point low-frequency acoustic wave sensor in the detection section where the leakage occurs and the distance x obtained from the above calculation;
所述声速v通过下述途径获得：所述数据库内还存储有事先在实验室中测定的不同介质密度、不同介质温度和不同输气管道内压力下的输气管道介质中的声速c的数据，所述云端服务器能够根据实时检测到的输气管道内的介质的密度、介质的温度和输气管道内的压力对数据库中的声速数据进行插值和拟合，即可获得输气管道泄漏时的声速v；The sound velocity v is obtained through the following approach: the database also stores the data of the sound velocity c in the gas pipeline medium under different medium densities, different medium temperatures and different gas pipeline internal pressures previously measured in the laboratory , the cloud server can interpolate and fit the sound velocity data in the database according to the density of the medium in the gas pipeline detected in real time, the temperature of the medium, and the pressure in the gas pipeline, so as to obtain the leakage time of the gas pipeline. The speed of sound v;
所述时间差△t通过下述途径获得：云端服务器对发生泄露的检测段内的基点、非基点低频声波传感器测得的低频声波信号进行互相关分析，能够获得发生泄露的检测段内基点、非基点低频声波传感器接收到的输气管道泄露时的低频声波信号的时间差，并比较t0和t1的大小，当t0>t1时，时间差为正；当t0<t1时，时间差为负；当t0＝t1时，时间差为零。The time difference Δt is obtained in the following way: the cloud server performs cross-correlation analysis on the low-frequency acoustic wave signals measured by the base point and non-base point low-frequency acoustic wave sensor in the leak detection section, and can obtain the base point, non-base point low-frequency acoustic wave signal in the leak detection section. The base point low-frequency acoustic wave sensor receives the time difference of the low-frequency acoustic wave signal when the gas pipeline leaks, and compares the size of t0 and t1. When t0 >t1 , the time difference is positive; when t0 <t1 , the time difference is negative; when t0 =t1 , the time difference is zero.
所述用户终端为智能手机、平板电脑或个人计算机。The user terminal is a smart phone, a tablet computer or a personal computer.
由于采用了上述技术方案，本发明所取得的有益效果为：Owing to adopting above-mentioned technical scheme, the beneficial effect that the present invention obtains is:
1、本发明实时性强，灵敏度高，适应能力强，响应时间快，定位精度高，能够实现全天候、实时、异地检测，不但能够实现对泄漏位置的定位还能够实现对泄漏口径大小和形状的判断。为使用者快速确定最佳的解决方案提供第一手资料。1. The present invention has strong real-time performance, high sensitivity, strong adaptability, fast response time, high positioning accuracy, and can realize all-weather, real-time, remote detection, not only can realize the location of the leakage position but also can realize the detection of the size and shape of the leakage aperture judge. Provide first-hand information for users to quickly determine the best solution.
2、本发明中的检测系统具有机器自学习功能，可以利用以往泄漏口的特征，进行自动优化升级，故随着应用时间的增长，系统的灵敏度和精确度会越来越高。2. The detection system in the present invention has a machine self-learning function, and can automatically optimize and upgrade by using the characteristics of the previous leaks. Therefore, the sensitivity and accuracy of the system will become higher and higher as the application time increases.
3、本发明采用数字化网络传输仪向云端服务器传输数据，适应性强。无论是人口稠密的城市，还是人迹罕至的荒野，都可以很好的工作。3. The present invention uses a digital network transmission device to transmit data to the cloud server, which has strong adaptability. Whether it is a densely populated city or an inaccessible wilderness, it can work very well.
4、本发明中的检测系统采用对声速数据库进行拟合和插值的办法来获得实时声速，大大简化了系统结构，提高了定位精度，加快了系统的响应速度。一个云端服务器可以多个检测点共用，从而能降低了成本。本发明采用次声波结合FSVM智能算法和机器自学习算法进行气体管道的泄漏检测，可大大提高系统的灵敏度和精度。4. The detection system in the present invention obtains the real-time sound velocity by fitting and interpolating the sound velocity database, which greatly simplifies the system structure, improves the positioning accuracy, and accelerates the response speed of the system. A cloud server can be shared by multiple detection points, thereby reducing costs. The invention uses the infrasound wave combined with the FSVM intelligent algorithm and the machine self-learning algorithm to detect the leakage of the gas pipeline, which can greatly improve the sensitivity and precision of the system.
5、本发明中，智能手机、平板电脑、PC皆可作为用户终端，进而提高了系统的灵活性，可实现多人、多地、实时的在线监控，可最大限度地缩短从发生泄漏到采取措施的时间并可最大限度地降低错报、漏报的概率。5. In the present invention, smart phones, tablet computers, and PCs can all be used as user terminals, thereby improving the flexibility of the system, realizing real-time online monitoring of multiple people, multiple places, and shortening the process from leakage to recovery as much as possible. The timing of the measures can minimize the probability of misstatements and omissions.
6、本发明利用管道泄漏时发出的低频声波来检测输气管道运行状况，是一种无损检测的手段。6. The present invention uses the low-frequency sound waves emitted when the pipeline leaks to detect the operation status of the gas pipeline, which is a means of non-destructive testing.
附图说明Description of drawings
图1为本发明的系统原理图。Fig. 1 is a schematic diagram of the system of the present invention.
图2为本发明中低频声波传感器的组成框图。Fig. 2 is a composition block diagram of the middle and low frequency acoustic wave sensor of the present invention.
图3为本发明中数字化网络传输仪的组成框图。Fig. 3 is a composition block diagram of the digital network transmission device in the present invention.
其中，in,
1、泄漏口 2、低频声波传感器 3、温度传感器 4、密度传感器 5、压力传感器 6、数字化网络传输仪 7、云端服务器 8、用户终端 9、3G网络 10、电容式传感器 11、放大、滤波电路 12、A/D转换模块 13、存储模块 14、网络传输模块 15、主板模块1. Leak port 2. Low frequency acoustic sensor 3. Temperature sensor 4. Density sensor 5. Pressure sensor 6. Digital network transmitter 7. Cloud server 8. User terminal 9. 3G network 10. Capacitive sensor 11. Amplifying and filtering circuits 12. A/D conversion module 13, storage module 14, network transmission module 15, motherboard module
具体实施方式detailed description
下面结合附图和具体的实施例对本发明作进一步的详细说明，但本发明并不限于这些实施例。The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments, but the present invention is not limited to these embodiments.
如图1至图3所示，一种基于声波法的输气管道泄露检测系统，包括用户终端8、云端服务器7及沿输气管道设置的若干个检测段，每个检测段处均设置有两个低频声波传感器2、两个温度传感器3、两个密度传感器4、两个压力传感器5及两个数字化网络传输仪6。As shown in Figures 1 to 3, a gas pipeline leakage detection system based on the acoustic wave method includes a user terminal 8, a cloud server 7 and several detection sections arranged along the gas pipeline, each detection section is provided with Two low-frequency acoustic wave sensors 2 , two temperature sensors 3 , two density sensors 4 , two pressure sensors 5 and two digital network transmitters 6 .
每个检测段内的两个低频声波传感器2分别设置在该检测段内的输气管道的两端，各低频声波传感器2用于采集该检测段内输气管道中的低频声波信号，并将采集到的低频声波信号进行放大和初步滤波后输送给与该低频声波传感器2处于同一检测位置的数字化网络传输仪6。如图2所示，各所述低频声波传感器2选用变极距型电容式传感器10作为换能元件。电容式传感器10的主要应用特点是机构简单，灵敏度高，动态响应好，稳定性高等。其基本原理是：金属膜片与极板平行放置，组成平行板电容器，当介质中的低频声信号作用于金属膜片时，膜片会在低频信号的作用下产生微小的位移，平行板电容器的极距发生改变，电容值发生改变；电容值的变化通过测量电路的反馈转化为电压信号，实现声信号到电信号的转化。但测量电路接受到的信号通常极其微弱，并伴随有其他噪声，因此信号必须经过放大并初步滤波后才能输出，因此需要有放大、滤波电路11。Two low-frequency acoustic wave sensors 2 in each detection section are respectively arranged at the two ends of the gas transmission pipeline in the detection section, and each low-frequency acoustic wave sensor 2 is used to collect the low-frequency acoustic wave signal in the gas transmission pipeline in the detection section, and The collected low-frequency acoustic wave signal is amplified and pre-filtered, and then sent to the digital network transmitter 6 at the same detection position as the low-frequency acoustic wave sensor 2 . As shown in FIG. 2 , each of the low-frequency acoustic wave sensors 2 uses a variable-pole-pitch capacitive sensor 10 as a transducer element. The main application features of the capacitive sensor 10 are simple mechanism, high sensitivity, good dynamic response and high stability. The basic principle is: the metal diaphragm is placed in parallel with the polar plate to form a parallel plate capacitor. When the low-frequency acoustic signal in the medium acts on the metal diaphragm, the diaphragm will produce a small displacement under the action of the low-frequency signal. The parallel-plate capacitor The pole distance changes, the capacitance value changes; the change of capacitance value is converted into a voltage signal through the feedback of the measurement circuit, and the conversion of the acoustic signal to the electrical signal is realized. However, the signal received by the measurement circuit is usually extremely weak and accompanied by other noises, so the signal must be amplified and preliminarily filtered before it can be output, so an amplifying and filtering circuit 11 is required.
每个检测段内的两个温度传感器3均设置在该检测段内的输气管道内且分别位于该段输气管道的两侧，各温度传感器3均用于测定该检测段内输气管道中介质的温度，并将检测到的温度信号传输给与该温度传感器3处于同一检测位置的数字化网络传输仪6。The two temperature sensors 3 in each detection section are all arranged in the gas transmission pipeline in the detection section and are respectively located on both sides of the gas transmission pipeline in this section, and each temperature sensor 3 is used to measure the gas transmission pipeline in the detection section. The temperature of the medium, and transmit the detected temperature signal to the digital network transmitter 6 at the same detection position as the temperature sensor 3 .
每个检测段内的两个密度传感器4均设置在该检测段内的输气管道内且分别位于该段输气管道的两侧，各密度传感器4均用于测定该检测段内输气管道中介质的密度，并将检测到的密度信号传输给与该密度传感器4处于同一检测位置的数字化网络传输仪6。The two density sensors 4 in each detection section are all arranged in the gas transmission pipeline in the detection section and are respectively located on both sides of the gas transmission pipeline in this section, and each density sensor 4 is used to measure the gas transmission pipeline in the detection section. The density of the medium, and transmit the detected density signal to the digital network transmitter 6 which is at the same detection position as the density sensor 4 .
每个检测段内的两个压力传感器5均设置在该检测段内的输气管道内且分别位于该段输气管道的两侧，各压力传感器5均用于测定该检测段内输气管道中介质的压力，并将检测到的压力信号传输给与该压力传感器5处于同一检测位置的数字化网络传输仪6。The two pressure sensors 5 in each detection section are all arranged in the gas transmission pipeline in the detection section and are respectively located on both sides of the gas transmission pipeline in this section, and each pressure sensor 5 is used to measure the gas transmission pipeline in the detection section. The pressure of the medium, and transmit the detected pressure signal to the digital network transmitter 6 which is at the same detection position as the pressure sensor 5 .
所述数字化网络传输仪6，用于将收集到的低频声波信号、温度信号、密度信号、压力信号转换为数字信号。数字化网络传输仪6实现从模拟信号到数字信号的转换，同时将数据保存到本地并在网络通畅时及时将最新数据传输到云端服务器7。The digitized network transmitter 6 is used to convert the collected low-frequency sound wave signal, temperature signal, density signal and pressure signal into digital signals. The digital network transmitter 6 realizes the conversion from analog signal to digital signal, and at the same time saves the data locally and transmits the latest data to the cloud server 7 in time when the network is smooth.
如图3所示，本发明中的数字化网络传输仪6包括主板模块15、A/D转换模块12、存储模块13及网络传输模块14，且主板模块15采用嵌入式系统，嵌入式系统由ARM处理器，外围设备、操作系统和应用软件等几部分组成，操作系统用来有效的控制和管理仪器的硬件和软件资源，主要实现处理器管理、存储管理、设备管理、文件管理和用户接口。本发明所述数字化网络传输仪6搭载的是实时操作系统，以调用一切可利用资源完成实时控制任务。通过ARM软硬件系统的调度与控制，实现对数字化网络传输仪6采样、存储、传输、中断、复位的控制。网络传输模块14将数字声波信号及时准确地发送到云端服务器7，这是管道泄漏实时性的重要保证。As shown in Figure 3, digitized network transmission instrument 6 among the present invention comprises main board module 15, A/D conversion module 12, storage module 13 and network transmission module 14, and main board module 15 adopts embedded system, and embedded system is made of ARM Processor, peripheral equipment, operating system and application software are composed of several parts. The operating system is used to effectively control and manage the hardware and software resources of the instrument, mainly to realize processor management, storage management, device management, file management and user interface. The digital network transmission instrument 6 of the present invention is equipped with a real-time operating system to call all available resources to complete real-time control tasks. Through the scheduling and control of the ARM software and hardware system, the control of sampling, storage, transmission, interruption and reset of the digital network transmission instrument 6 is realized. The network transmission module 14 sends the digital sound wave signal to the cloud server 7 in a timely and accurate manner, which is an important guarantee for the real-time performance of pipeline leakage.
由于管道分布区域广泛，既有人口稠密的城市，也有人迹罕至的荒野，这就要求传输方式有很强的环境适应性，本发明所述数字化网络传输仪6采用H330S-Q50-00型3G模块，该模块能自动切换合适的频段发射信号，确保信号及时有效发送到云端服务器7。同时为了增强数字化网络传输仪6对复杂网络环境特别是没有足够强度网络覆盖的地区的适应性，增大模块的发射功率，配备高增益天线。Due to the wide distribution of pipelines, there are both densely populated cities and inaccessible wilderness, which requires that the transmission method has strong environmental adaptability. The digital network transmission device 6 of the present invention adopts the H330S-Q50-00 type 3G module, The module can automatically switch the appropriate frequency band to transmit signals, ensuring that the signals are sent to the cloud server 7 in a timely and effective manner. At the same time, in order to enhance the adaptability of the digital network transmitter 6 to complex network environments, especially areas without sufficient network coverage, the transmit power of the module is increased and a high-gain antenna is equipped.
所述云端服务器7，用于对从数字化网络传输仪6传来的数字信号进行信号处理(主要包括FFT变换和滤波，其中FFT变换为快速傅里叶变换)，并利用FSVM算法进行模式识别，通过模式识别提取并分析输气管道泄漏时的低频声波信号的特征量(如声压级、声功率等)，判断泄露是否发生并确定泄漏口的大小和形状；其中，FSVM算法为模糊支持向量机算法，是一个有监督的学习模型，通常用来进行模式识别、分类以及回归分析。同时对泄漏时两个低频声波传感器接收到的低频声波信号进行互相关分析，并结合输气管道内的声速，对泄露口的位置实现定位；最终云端服务器7将处理完的数据通过3G网络9传送给用户终端8。具体地说，所述云端服务器7中设置有数据库，所述数据库中存储有若干个不同泄漏口的大小数据和形状数据及其所对应的泄漏声波信号的特征量，所述泄漏声波信号的特征量是根据理论分析及实验测定所得到的；当实际应用过程中的输气管道发生泄漏时，云端服务器通过模式识别提取出泄漏时的低频声波信号的特征量，并根据此低频声波信号的特征量对数据库中现有的数据进行插值与拟合，进而确定泄漏口的大小和形状，最后人为地测量出该泄漏口的实际大小和形状，并将该测量得到的数据与系统判断出来的数据进行比较，将两者的偏差保存到数据库中；系统内植具有机器自学习功能的算法，可根据此偏差对数据库中泄漏声波信号的特征量及其所对应的泄漏口的大小数据和形状数据进行修正。当再次发生泄漏时，系统就会根据数据库中新的数据选择新的插值和拟合方法，来确定泄漏口的大小和形状，再次人为地测量出此时泄漏口的实际大小和形状，并将该测量得到的数据与系统判断出来的数据进行比较，将两者的偏差保存到数据库中。如此反复进行，就可越来越准确的判断出泄漏口的大小和形状。The cloud server 7 is used to carry out signal processing (mainly including FFT transformation and filtering, wherein the FFT transformation is Fast Fourier Transform) to the digital signal transmitted from the digital network transmission instrument 6, and utilizes the FSVM algorithm to carry out pattern recognition, Extract and analyze the feature quantity (such as sound pressure level, sound power, etc.) of the low-frequency acoustic signal when the gas pipeline leaks through pattern recognition, judge whether the leak occurs and determine the size and shape of the leak; among them, the FSVM algorithm is a fuzzy support vector Machine algorithm is a supervised learning model, usually used for pattern recognition, classification and regression analysis. At the same time, the cross-correlation analysis is performed on the low-frequency acoustic wave signals received by the two low-frequency acoustic wave sensors during the leakage, and the location of the leak is realized by combining the sound velocity in the gas pipeline; finally, the cloud server 7 passes the processed data through the 3G network 9 sent to the user terminal 8. Specifically, the cloud server 7 is provided with a database, which stores the size data and shape data of several different leak openings and the feature quantities of the corresponding leaking acoustic wave signals, and the characteristics of the leaking acoustic wave signals The quantity is obtained based on theoretical analysis and experimental measurement; when the gas pipeline leaks in the actual application process, the cloud server extracts the characteristic quantity of the low-frequency sound wave signal at the time of leakage through pattern recognition, and according to the characteristics of the low-frequency sound wave signal Quantitatively interpolate and fit the existing data in the database, and then determine the size and shape of the leak, and finally measure the actual size and shape of the leak artificially, and compare the measured data with the data judged by the system Compare and save the deviation between the two in the database; the system is built with an algorithm with machine self-learning function, which can analyze the characteristic quantity of the leaked acoustic signal in the database and the size data and shape data of the corresponding leak port according to this deviation Make corrections. When a leak occurs again, the system will select a new interpolation and fitting method based on the new data in the database to determine the size and shape of the leak, artificially measure the actual size and shape of the leak again, and The measured data is compared with the data judged by the system, and the deviation between the two is saved in the database. Repeatedly, the size and shape of the leak can be judged more and more accurately.
将每个检测段内的其中一个低频声波传感器选作基点，作为基点低频声波传感器，与之相对应的另一低频声波传感器作为非基点低频声波传感器；将基点低频声波传感器接收到泄漏声波信号的时间设置为t0，非基点低频声波传感器接收到泄漏声波信号的时间设置为t1，One of the low-frequency acoustic wave sensors in each detection section is selected as the base point as the base point low-frequency acoustic wave sensor, and the other corresponding low-frequency acoustic wave sensor is used as a non-base point low-frequency acoustic wave sensor; the base point low-frequency acoustic wave sensor receives the leakage acoustic wave signal The time is set as t0 , the time when the non-base point low-frequency acoustic wave sensor receives the leakage acoustic wave signal is set as t1 ,
所述泄露口1距离发生泄露的检测段内的基点低频声波传感器的位置由下式确定：The position of the base point low-frequency acoustic wave sensor in the detection section where the leak occurs from the leak port 1 is determined by the following formula:
其中，x为泄漏口1距离发生泄露的检测段内的基点低频声波传感器的距离，l为每个检测段内的基点低频声波传感器与非基点低频声波传感器间的距离，v为云端服务器7对声速数据进行插值和拟合后所获得的输气管道泄漏时的声速，△t为发生泄露的检测段内的基点、非基点低频声波传感器接收到输气管道泄露时的低频声波信号的时间差；所述泄漏口1的位置最终由发生泄露的检测段内的基点低频声波传感器的位置和上述计算所得距离x确定；Among them, x is the distance between the leak port 1 and the base point low-frequency acoustic wave sensor in the detection section where leakage occurs, l is the distance between the base point low-frequency acoustic wave sensor and the non-base point low-frequency acoustic wave sensor in each detection section, and v is the cloud server 7 pairs The sound velocity when the gas pipeline leaks is obtained after the sound velocity data is interpolated and fitted, and Δt is the time difference between the base point and the non-base point low-frequency acoustic wave sensor receiving the low-frequency acoustic signal when the gas pipeline leaks in the detection section where the leak occurs; The position of the leak port 1 is finally determined by the position of the base point low-frequency acoustic wave sensor in the detection section where the leak occurs and the distance x obtained from the above calculation;
所述声速v通过下述途径获得：所述数据库内还存储有事先在实验室中测定的不同介质密度、不同介质温度和不同输气管道内压力下的输气管道介质中的声速c的数据，所述云端服务器7能够根据实时检测到的输气管道内的介质的密度、介质的温度和输气管道内的压力对数据库中的声速数据进行插值和拟合，即可获得输气管道泄漏时的声速v；The sound velocity v is obtained through the following approach: the database also stores the data of the sound velocity c in the gas pipeline medium under different medium densities, different medium temperatures and different gas pipeline internal pressures previously measured in the laboratory , the cloud server 7 can interpolate and fit the sound velocity data in the database according to the density of the medium in the gas pipeline detected in real time, the temperature of the medium and the pressure in the gas pipeline, so as to obtain the leakage of the gas pipeline The speed of sound v at time;
所述时间差△t通过下述途径获得：云端服务器7对发生泄露的检测段内的基点、非基点低频声波传感器2测得的低频声波信号进行互相关分析，能够获得发生泄露的检测段内的基点、非基点低频声波传感器2接收到输气管道泄露时的低频声波信号的时间差，并比较t0和t1的大小，当t0>t1时，时间差为正；当t0<t1时，时间差为负；当t0＝t1时，时间差为零。The time difference Δt is obtained through the following approach: the cloud server 7 performs cross-correlation analysis on the base point and non-base point low-frequency acoustic wave signals measured by the low-frequency acoustic wave sensor 2 in the detection section where leakage occurs, and can obtain the time difference in the detection section where leakage occurs. Base point, non-base point low-frequency acoustic wave sensor 2 receives the time difference of the low-frequency acoustic wave signal when the gas pipeline leaks, and compares the size of t0 and t1. When t0 >t1 , the time difference is positive; when t0 <t1 When t 0 =t 1 , the time difference is negative; when t0 =t1 , the time difference is zero.
所述用户终端8，根据云端服务器7传来的数据对输气管道的运行状况进行判断，若发生泄露，则发出报警信号，并给出输气管道泄漏口1的位置以及泄漏口1的大小和形状。所述用户终端8为智能手机、平板电脑或个人计算机。使用者只要知道用户终端的用户名和密码，登录系统后即可实现对管道运行状况的监控，进而实现多人多地实时地对气体管道的运行状况的监控。The user terminal 8 judges the operating status of the gas pipeline according to the data transmitted from the cloud server 7, and if a leak occurs, an alarm signal is sent, and the position of the gas pipeline leak 1 and the size of the leak 1 are given and shape. The user terminal 8 is a smart phone, a tablet computer or a personal computer. As long as the user knows the user name and password of the user terminal, he can monitor the operation status of the pipeline after logging in to the system, and then realize real-time monitoring of the operation status of the gas pipeline by multiple people.
利用本发明所述的基于声波法的输气管道泄露检测系统检测管道运行状况的原理为：The principle of utilizing the gas pipeline leakage detection system based on the acoustic wave method of the present invention to detect the pipeline operation status is as follows:
(1)当输气管道正常运行时，低频声波传感器2的输出信号在误差范围内可认为是零。用户终端8不报警并显示输气管道运行正常。(1) When the gas pipeline is running normally, the output signal of the low-frequency acoustic wave sensor 2 can be considered to be zero within the error range. The user terminal 8 does not report to the police and displays that the gas pipeline is operating normally.
(2)当输气管道发生泄漏时，泄漏声压级的时域特性较未发生泄漏时存在较大变化；不同的泄漏口1大小和形状对应的泄漏声压级的频谱也不同。在应用前期，首先在理论分析的基础上，对低频声波传感器2测得的低频声波信号，进行变换、滤波和处理；进而得到声压级的时域和频域特性，根据声压级的时域特性即可判断出输气管道是否发生泄漏，根据声压级的频域特性即可判断出泄漏口的大小和形状。本发明所述的基于声波法的输气管道泄漏检测系统具有的机器学习功能可以减小由于理论分析的结果和实际应用之间存在偏差而导致的对泄漏口1大小和形状判断不准确的程度，即：当泄漏发生后，人为地测量出泄漏口1的大小和形状，并将该测量得到的数据与系统判断出来的数据进行比较，将两者的偏差保存到数据库中；系统内植具有机器自学习功能的算法，可根据此偏差对数据库中泄漏声波信号的特征量及其所对应的泄漏口的大小数据和形状数据进行修正。(2) When the gas transmission pipeline leaks, the time-domain characteristics of the leakage sound pressure level change greatly compared with that when no leakage occurs; the spectrum of the leakage sound pressure level corresponding to the size and shape of the leakage port 1 is also different. In the early stage of application, on the basis of theoretical analysis, the low-frequency acoustic wave signal measured by the low-frequency acoustic wave sensor 2 is transformed, filtered, and processed; then the time-domain and frequency-domain characteristics of the sound pressure level are obtained, and According to the frequency domain characteristics of the sound pressure level, the size and shape of the leakage can be judged. The machine learning function of the gas pipeline leakage detection system based on the acoustic wave method of the present invention can reduce the degree of inaccuracy in judging the size and shape of the leakage port 1 due to the deviation between the theoretical analysis results and the actual application , that is: when a leak occurs, measure the size and shape of the leak port 1 artificially, compare the measured data with the data judged by the system, and save the difference between the two in the database; the built-in system has The algorithm of the machine self-learning function can correct the feature quantity of the leaked acoustic wave signal in the database and the corresponding size data and shape data of the leak port according to this deviation.
对两低频声波传感器2测得的低频声波进行互相关分析，即可获得泄漏口1两侧的相邻两低频声波传感器2接收到泄漏声波信号的时间差△t，再结合两低频声波传感器2之间的距离l和管道介质中的声速c即可实现对泄漏口1的定位。但是，由于管道介质中的声速和管道介质的密度、介质的温度、管道的压力等诸多因素有关，故其不是一定值，而是随着输气管道工况的变化不断发生着变化。这就给定位的精度造成了一定的影响。本系统采用的解决办法是：先在实验室中测定出不同介质密度、介质温度和管道内压力下的一系列声速，然后在云端服务器7中利用这些数据建立数据库，当发生泄漏时，系统自动根据实时测定的介质的密度、介质的温度和管道内的压力对数据库中的数据进行插值和拟合，即可获得泄漏发生时的声速v。再由公式：The cross-correlation analysis of the low-frequency acoustic waves measured by the two low-frequency acoustic wave sensors 2 can obtain the time difference Δt between two adjacent low-frequency acoustic wave sensors 2 on both sides of the leak port 1 receiving the leakage acoustic wave signal, and then combine the two low-frequency acoustic wave sensors 2 The location of the leakage port 1 can be realized by the distance l between them and the sound velocity c in the pipeline medium. However, since the sound velocity in the pipeline medium is related to many factors such as the density of the pipeline medium, the temperature of the medium, and the pressure of the pipeline, it is not a constant value, but changes continuously with the change of the working conditions of the gas transmission pipeline. This has a certain impact on the positioning accuracy. The solution adopted by this system is: first measure a series of sound velocities under different medium densities, medium temperatures and pipeline internal pressures in the laboratory, and then use these data to establish a database in the cloud server 7. When a leak occurs, the system automatically According to the density of the medium measured in real time, the temperature of the medium and the pressure in the pipeline, the data in the database are interpolated and fitted, and the sound velocity v when the leakage occurs can be obtained. Then by the formula:
即可测出泄漏口1距发生泄露的检测段内的基点低频声波传感器的距离，再根据该基点低频声波传感器在输气管道上的设置位置即可准确判断出泄漏口在输气管道上的具体位置。The distance between the leakage port 1 and the base point low-frequency acoustic wave sensor in the leak detection section can be measured, and then the position of the leak port on the gas transmission pipeline can be accurately judged according to the setting position of the base point low-frequency acoustic wave sensor on the gas transmission pipeline. specific location.
本发明中未述及的部分采用或借鉴已有技术即可实现。The parts not mentioned in the present invention can be realized by adopting or referring to the prior art.
本文中所描述的具体实施例仅仅是对本发明的精神所作的举例说明。本发明所属技术领域的技术人员可以对所描述的具体实施例做各种各样的修改或补充或采用类似的方式替代，但并不会偏离本发明的精神或者超越所附权利要求书所定义的范围。The specific embodiments described herein are merely illustrative of the spirit of the invention. Those skilled in the art to which the present invention belongs can make various modifications or supplements to the described specific embodiments or adopt similar methods to replace them, but they will not deviate from the spirit of the present invention or go beyond the definition of the appended claims range.
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610912886.6ACN106352243B (en) | 2016-10-20 | 2016-10-20 | A Gas Pipeline Leak Detection System Based on Acoustic Wave Method |
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610912886.6ACN106352243B (en) | 2016-10-20 | 2016-10-20 | A Gas Pipeline Leak Detection System Based on Acoustic Wave Method |
| Publication Number | Publication Date |
|---|---|
| CN106352243Atrue CN106352243A (en) | 2017-01-25 |
| CN106352243B CN106352243B (en) | 2018-06-26 |
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610912886.6AActiveCN106352243B (en) | 2016-10-20 | 2016-10-20 | A Gas Pipeline Leak Detection System Based on Acoustic Wave Method |
| Country | Link |
|---|---|
| CN (1) | CN106352243B (en) |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107101682A (en)* | 2017-05-23 | 2017-08-29 | 张允峰 | A kind of detecting system of ultrasound examination quantity of gas leakage |
| CN107122719A (en)* | 2017-04-07 | 2017-09-01 | 北京讯腾智慧科技股份有限公司 | Pipeline deformation automatic recognition system and method based on image recognition |
| CN107435817A (en)* | 2017-08-15 | 2017-12-05 | 常州大学 | A kind of 2 leak detection accurate positioning methods of pressure pipeline |
| CN107748273A (en)* | 2017-09-29 | 2018-03-02 | 中国石油大学(华东) | A kind of pipeline pressure wave velocity testing device and method based on casing experimental loop |
| CN108387346A (en)* | 2018-01-18 | 2018-08-10 | 西安航天动力试验技术研究所 | A kind of airtight leak detection system of liquid-propellant rocket engine Propellant Supply pipeline and method |
| CN109027705A (en)* | 2018-07-28 | 2018-12-18 | 中海油能源发展股份有限公司 | A kind of submarine pipeline infrasound leakage monitoring system |
| CN109282155A (en)* | 2017-07-22 | 2019-01-29 | 周尧 | A kind of petroleum pipeline leakage active detecting device |
| CN109282154A (en)* | 2017-07-22 | 2019-01-29 | 周尧 | A kind of petroleum pipeline leakage monitoring method |
| CN109357171A (en)* | 2018-11-30 | 2019-02-19 | 北京无线电计量测试研究所 | A kind of monitoring of underground pipeline leak and localization method and device |
| CN109442228A (en)* | 2018-11-23 | 2019-03-08 | 水联网技术服务中心（北京）有限公司 | Identify that listening for leak sound leaks device and method by cloud |
| CN109737315A (en)* | 2019-03-14 | 2019-05-10 | 广东聚源管业实业有限公司 | A kind of line leakage method and system |
| CN110107816A (en)* | 2018-02-01 | 2019-08-09 | 北京声创新技术发展有限责任公司 | Oil and gas pipeline pipe cleaner/detector infrasonic sound real-time tracking positioning system and method |
| CN110469782A (en)* | 2019-07-31 | 2019-11-19 | 中国科学院声学研究所 | A kind of pipe leakage positioning device based on sef-adapting filter |
| CN110925603A (en)* | 2019-12-06 | 2020-03-27 | 集美大学 | Pipeline state detection method and system |
| CN111350945A (en)* | 2018-12-24 | 2020-06-30 | 无锡热能在线科技有限公司 | Method for detecting medium leakage of system pipeline |
| WO2020155002A1 (en)* | 2019-01-29 | 2020-08-06 | 南京工业大学 | Pipeline leakage detection apparatus and method employing pso-vmd algorithm |
| CN112066272A (en)* | 2020-09-17 | 2020-12-11 | 昆明理工大学 | Gas pipeline leakage detection device and detection method based on infrasonic waves |
| CN112432061A (en)* | 2019-08-26 | 2021-03-02 | 苏州安能通智能科技有限公司 | Pipeline leakage monitoring method and system |
| CN113090958A (en)* | 2021-03-12 | 2021-07-09 | 西南石油大学 | Gas pipeline safety prevention and control monitoring method |
| CN115953854A (en)* | 2021-10-08 | 2023-04-11 | 中国石油天然气集团有限公司 | Inspection device of oil-gas pipeline, control method, control device and inspection system thereof |
| CN118391606A (en)* | 2024-04-26 | 2024-07-26 | 江西圣杰市政工程有限公司 | Pipeline trenchless inspection method based on acoustic wave data |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108930915B (en)* | 2018-08-01 | 2020-05-05 | 北京中彤节能技术有限公司 | Pipeline leakage identification method, device and system based on qualitative mapping |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040186861A1 (en)* | 2003-01-17 | 2004-09-23 | Phatak Shirish Hemant | Method and system for use of storage caching with a distributed file system |
| CN101196872A (en)* | 2007-11-19 | 2008-06-11 | 清华大学 | Leak detection and localization method based on pressure and acoustic wave information fusion |
| KR20110062956A (en)* | 2009-12-03 | 2011-06-10 | 주식회사 지앤매직 | Gas safety valve shutoff system and method |
| CN102095083A (en)* | 2010-12-16 | 2011-06-15 | 上海工业自动化仪表研究院 | Detecting system used for leakage of gas valve |
| CN202082629U (en)* | 2011-04-06 | 2011-12-21 | 黄定军 | Natural gas pipeline leakage monitoring system |
| CN203395600U (en)* | 2013-08-19 | 2014-01-15 | 东北石油大学 | Pipeline leakage detecting and locating device |
| CN204062504U (en)* | 2014-08-04 | 2014-12-31 | 青岛厚科信息工程有限公司 | Pipeline leakage testing device |
| CN104595729A (en)* | 2015-01-15 | 2015-05-06 | 中国石油大学(华东) | Oil and gas pipeline leakage positioning method based on sound wave amplitude |
| CN104654024A (en)* | 2015-02-12 | 2015-05-27 | 常州大学 | Method for locating and analyzing leakage of city gas pipeline based on GRNN (Generalized Regression Neural Network) |
| CN105351756A (en)* | 2015-11-23 | 2016-02-24 | 杨璐馨 | Pipeline leakage recognizing and positioning system and method based on sound wave imaging |
| CN205090197U (en)* | 2015-07-09 | 2016-03-16 | 南京声宏毅霆网络科技有限公司 | Leak detection system and monitoring facilities of pipeline |
| CN205402250U (en)* | 2016-03-08 | 2016-07-27 | 于晓洋 | Intelligent monitoring system of petroleum transportation pipeline |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040186861A1 (en)* | 2003-01-17 | 2004-09-23 | Phatak Shirish Hemant | Method and system for use of storage caching with a distributed file system |
| CN101196872A (en)* | 2007-11-19 | 2008-06-11 | 清华大学 | Leak detection and localization method based on pressure and acoustic wave information fusion |
| KR20110062956A (en)* | 2009-12-03 | 2011-06-10 | 주식회사 지앤매직 | Gas safety valve shutoff system and method |
| CN102095083A (en)* | 2010-12-16 | 2011-06-15 | 上海工业自动化仪表研究院 | Detecting system used for leakage of gas valve |
| CN202082629U (en)* | 2011-04-06 | 2011-12-21 | 黄定军 | Natural gas pipeline leakage monitoring system |
| CN203395600U (en)* | 2013-08-19 | 2014-01-15 | 东北石油大学 | Pipeline leakage detecting and locating device |
| CN204062504U (en)* | 2014-08-04 | 2014-12-31 | 青岛厚科信息工程有限公司 | Pipeline leakage testing device |
| CN104595729A (en)* | 2015-01-15 | 2015-05-06 | 中国石油大学(华东) | Oil and gas pipeline leakage positioning method based on sound wave amplitude |
| CN104654024A (en)* | 2015-02-12 | 2015-05-27 | 常州大学 | Method for locating and analyzing leakage of city gas pipeline based on GRNN (Generalized Regression Neural Network) |
| CN205090197U (en)* | 2015-07-09 | 2016-03-16 | 南京声宏毅霆网络科技有限公司 | Leak detection system and monitoring facilities of pipeline |
| CN105351756A (en)* | 2015-11-23 | 2016-02-24 | 杨璐馨 | Pipeline leakage recognizing and positioning system and method based on sound wave imaging |
| CN205402250U (en)* | 2016-03-08 | 2016-07-27 | 于晓洋 | Intelligent monitoring system of petroleum transportation pipeline |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107122719A (en)* | 2017-04-07 | 2017-09-01 | 北京讯腾智慧科技股份有限公司 | Pipeline deformation automatic recognition system and method based on image recognition |
| CN107101682A (en)* | 2017-05-23 | 2017-08-29 | 张允峰 | A kind of detecting system of ultrasound examination quantity of gas leakage |
| CN109282155A (en)* | 2017-07-22 | 2019-01-29 | 周尧 | A kind of petroleum pipeline leakage active detecting device |
| CN109282154A (en)* | 2017-07-22 | 2019-01-29 | 周尧 | A kind of petroleum pipeline leakage monitoring method |
| CN107435817A (en)* | 2017-08-15 | 2017-12-05 | 常州大学 | A kind of 2 leak detection accurate positioning methods of pressure pipeline |
| CN107435817B (en)* | 2017-08-15 | 2019-01-25 | 常州大学 | An accurate positioning method for two-point leak detection in pressure pipelines |
| CN107748273A (en)* | 2017-09-29 | 2018-03-02 | 中国石油大学(华东) | A kind of pipeline pressure wave velocity testing device and method based on casing experimental loop |
| CN107748273B (en)* | 2017-09-29 | 2019-12-20 | 中国石油大学(华东) | Pipeline pressure wave velocity testing device and method based on pipe flow test loop |
| CN108387346A (en)* | 2018-01-18 | 2018-08-10 | 西安航天动力试验技术研究所 | A kind of airtight leak detection system of liquid-propellant rocket engine Propellant Supply pipeline and method |
| CN110107816A (en)* | 2018-02-01 | 2019-08-09 | 北京声创新技术发展有限责任公司 | Oil and gas pipeline pipe cleaner/detector infrasonic sound real-time tracking positioning system and method |
| CN109027705A (en)* | 2018-07-28 | 2018-12-18 | 中海油能源发展股份有限公司 | A kind of submarine pipeline infrasound leakage monitoring system |
| CN109442228A (en)* | 2018-11-23 | 2019-03-08 | 水联网技术服务中心（北京）有限公司 | Identify that listening for leak sound leaks device and method by cloud |
| CN109357171A (en)* | 2018-11-30 | 2019-02-19 | 北京无线电计量测试研究所 | A kind of monitoring of underground pipeline leak and localization method and device |
| CN111350945A (en)* | 2018-12-24 | 2020-06-30 | 无锡热能在线科技有限公司 | Method for detecting medium leakage of system pipeline |
| WO2020155002A1 (en)* | 2019-01-29 | 2020-08-06 | 南京工业大学 | Pipeline leakage detection apparatus and method employing pso-vmd algorithm |
| CN109737315A (en)* | 2019-03-14 | 2019-05-10 | 广东聚源管业实业有限公司 | A kind of line leakage method and system |
| CN110469782A (en)* | 2019-07-31 | 2019-11-19 | 中国科学院声学研究所 | A kind of pipe leakage positioning device based on sef-adapting filter |
| CN110469782B (en)* | 2019-07-31 | 2020-07-17 | 中国科学院声学研究所 | Pipeline leakage positioning device based on self-adaptive filter |
| CN112432061A (en)* | 2019-08-26 | 2021-03-02 | 苏州安能通智能科技有限公司 | Pipeline leakage monitoring method and system |
| CN110925603A (en)* | 2019-12-06 | 2020-03-27 | 集美大学 | Pipeline state detection method and system |
| CN112066272A (en)* | 2020-09-17 | 2020-12-11 | 昆明理工大学 | Gas pipeline leakage detection device and detection method based on infrasonic waves |
| CN112066272B (en)* | 2020-09-17 | 2022-03-01 | 昆明理工大学 | Gas pipeline leakage detection device and detection method based on infrasonic waves |
| CN113090958A (en)* | 2021-03-12 | 2021-07-09 | 西南石油大学 | Gas pipeline safety prevention and control monitoring method |
| CN115953854A (en)* | 2021-10-08 | 2023-04-11 | 中国石油天然气集团有限公司 | Inspection device of oil-gas pipeline, control method, control device and inspection system thereof |
| CN118391606A (en)* | 2024-04-26 | 2024-07-26 | 江西圣杰市政工程有限公司 | Pipeline trenchless inspection method based on acoustic wave data |
| Publication number | Publication date |
|---|---|
| CN106352243B (en) | 2018-06-26 |
| Publication | Publication Date | Title |
|---|---|---|
| CN106352243B (en) | A Gas Pipeline Leak Detection System Based on Acoustic Wave Method | |
| Islam et al. | A review on current technologies and future direction of water leakage detection in water distribution network | |
| CN106369288B (en) | Water supply network leakage loss monitoring system | |
| CN101968162B (en) | Pipeline leakage positioning system and method based on collaborative detection with negative pressure wave and sound wave | |
| CN111271610B (en) | Liquid pipeline leakage detection early warning device and method | |
| Van Hieu et al. | Wireless transmission of acoustic emission signals for real-time monitoring of leakage in underground pipes | |
| CN201273457Y (en) | A pipeline leakage monitoring device | |
| Kartakis et al. | Adaptive edge analytics for distributed networked control of water systems | |
| CN103216732B (en) | Real-time monitoring and positioning system for water leakage of pipe network | |
| CN203477909U (en) | Pipeline leakage automatic monitoring positioning device based on low-frequency sound waves and negative-pressure waves | |
| CN105840987A (en) | Pipeline leakage weighted positioning method and device based on pressure waves and sound waves | |
| CN1435678A (en) | Method and device for intelligent diagnosis and location of leakage fault of fluid delivery pipeline | |
| CN101598263A (en) | Portable pipeline leakage detection method and device | |
| CN101684894A (en) | Pipeline leakage monitoring method and device | |
| CN112664842A (en) | Device for positioning leakage of water delivery pipeline | |
| CN204964493U (en) | A wireless long-distance seawater quality environment detection equipment | |
| EP3707489A1 (en) | Methods and apparatus to detect leaks | |
| Perdana et al. | Water leak detection and shut-off system on water distribution pipe network using wireless sensor network | |
| CN102168809A (en) | Method and system for detecting leakage of parking apron aviation gasoline pipe network based on pressure and temperature analysis | |
| CN102174992A (en) | Pressure curve feature extraction method for pressure pipeline | |
| Han et al. | Localization of CO2 gas leakages through acoustic emission multi-sensor fusion based on wavelet-RBFN modeling | |
| Liu et al. | Application of VMD in pipeline leak detection based on negative pressure wave | |
| CN118582674A (en) | Water supply network leakage identification and analysis system based on cross-spectral analysis | |
| CN109297552A (en) | Fast flow detection method, device and fast flow detector | |
| Singh et al. | iPipe: water pipeline monitoring and leakage detection |
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |