Leak detection for long transportation pipeline using a state coupling analysis of pump units

Leak detection for long transportation pipeline with a large economic and environmental impact has been an area of intensive research for more than five decades. This paper presents a novel pipeline leak detection scheme based on a state coupling analysis (SCA). Instead of monitoring the pipeline and pump units separately, SCA introduces a new detecting method of analyzing data in a coupling running condition. A novel capture method for abnormal pressure based on logical reasoning algorithm is proposed. Hamming approach degree arithmetic is applied to calculate the matching mode identifying the state of units. SCA is used to reduce the rate of false alarm and detect the leak with a high detecting sensitivity for long transportation pipeline. An on-line software system based on SCA is utilized to achieve superior accuracy and implementation. An industrial case study for coupling system pipeline leak detection is used as an example to validate the effectiveness of the proposed method.     

Integrated-signal-based leak location method for liquid pipelines

Leaks in pipelines can cause major incidents resulting in both human injuries and financial losses. Among the considerable leak detection and location methods, the Negative Pressure Wave (NPW) based method has been widely used in locating leaks in liquid pipelines. The NPW based method only monitors the pressure changes at two ends of a pipeline. But the pressure is apt to be fixed by the end equipment and the change of it induced by a small or slow leakage is too small to be detected, which limit the application of the NPW based method in these situations. This paper presents a novel leak location method based on integrated signal, which is a combination of the pressure and flow rate signals. The representation of the integrated signal is derived from the transient analysis of the leakage. For the change of the integrated signal induced by a leakage is larger than the pressure change and it is also unaffected by the end equipment, the proposed method can be used to detect and locate small or slow leakage in a pipeline and can also be used in pipelines which end pressures are fixed by some kinds of equipment. The validation of the proposed method also confirms its advantages.     

Numerical and experimental study on the generation and propagation of negative wave in high-pressure gas pipeline leakage

Negative-wave-based leakage detection and localization technology has been widely used in the pipeline system to diminish leak loss and enhance environmental protection from hazardous leak events. However, the fluid mechanics behind the negative wave method has yet been disclosed. The objective of this paper is to investigate the generation and propagation of negative wave in high-pressure pipeline leakage. A three-dimensional computational fluid dynamic (CFD) study on the negative wave was carried out with large eddy simulation (LES) method. Experimentally validated simulation presented the transient wave generation at the leak onset and the comprehensive wave evolution afterwards. Negative wave was proven to be a kind of rarefaction acoustic waves induced by transient mass loss at the onset of leakage. Diffusion due to the density difference at wave fronts drives the negative wave propagation. Propagation of negative wave can be categorized into three states – semi-spherical wave, wave superposition and plane wave, based on different wave forms. The wave characteristics at different states were elucidated and the attenuation effects were discussed respectively. Finally, a non-dimensional correlation was proposed to predict the negative wave amplitude based on pipeline pressure and leak diameter.     

2种基于模式识别的环状燃气管网泄漏检测方法

为了将模式识别技术应用于环状燃气管网泄漏检测并找到合适的特征提取方法,以天津城建大学实验室环状燃气管网泄漏为例,将实验的28种工况作为测试样本,与之对应的模拟工况作为训练样本,采用提取压力图像特征向量法和节点压力矩阵法分别进行环状燃气管网的泄漏检测,采用支持向量机分类器将2种方法获得的特征向量进行训练与分类检验,进而将其分类准确率进行对比分析。研究结果表明:该2种方法均可用于环状燃气管网泄漏检测,提取压力图像特征向量法因有效地降低了特征向量的维度和数据波动的干扰,其结果更优。结合SCADA和GIS系统,可将该法应用于实际水、气、油管网泄漏检测和定位,有助于降低成本,提高检测效果。     

Real-time leak detection in oil pipelines using an Inverse Transient Analysis model

Based on Inverse Transient Analysis (ITA) method, a real-time leak detection method is proposed to capture leak location and the associated leak rate in oil pipe conveyance systems. In the proposed approach, location and flow rate of leak (if any), the fluid properties, as well as physical parameters of the system, are calculated in consecutive periods through minimizing the discrepancy between the calculated and measured flow parameters of the system. The method of characteristics is employed to numerically calculate the transient responses of the system and the genetic algorithm is utilized as the optimization engine. The proposed approach was applied to several real pipeline systems in which the required transient flow data are either directly collected from the field or fabricated with a third-party numerical software. Extensive numerical explorations were conducted to investigate the performance of the proposed method in real-time leak detection and to determine the extent to which field data errors, stemming from Supervisory Control and Data Acquisition (SCADA) systems and measurement equipment, affect the leak flow rate and location detectability of the proposed approach. The results show that the proposed approach provides promising results under a variety of transient and steady-state flow conditions even in the case with small leak flow rate of around 2% of the line rate. The results also reveal that the noises in the measurement data and the errors originated from SCADA systems do not significantly compromise the leak detectability of the proposed approach, confirming that the proposed approach can be utilized in practice.     

Modelling Complex Pipeline Network Leak Detection Systems

A novel model for detecting leaks in complex pipeline network systems has been developed. The model derives from the theory of Liapunov stability criteria. A leak is detected if the resulting eigenvalues from the deviation flow matrix have values less than a predetermined value. An advanced mesh network algorithm was used to decompose the complex pipeline network system into sub-networks. The flow model (equations of motion and continuity) which incorporates a leaking factor, k_L, is solved by a numerical technique that uses the method of characteristics and an implicit finite function. The unsteady state flow matrix of the complex pipeline network system was analysed using a modified Hardy Cross algorithm, where the velocity and pressure were computed for each node and pipeline loop in the complex network. The plots for the characteristic pressure and velocity eigenvalues show that pressure measurements are faster parameters for leak detection than volume measurements. Volume measurements appear to be suitable for larger leak systems and longer response time.     

Detection of small leakage from long transportation pipeline with complex noise

In the long distance pipeline remote monitoring system, small leak detection becomes an important issue. Weak singularities in small leak signals are usually difficult to detect precisely under complicated noise background, which may cause false alarm or miss alarm. The advantage of applying the harmonic wavelet method is explored in this paper. Pipeline small leak sensitive characteristics are recognized and the negative pressure wave inflexions are extracted by harmonic wavelet analysis, expressed in terms of harmonic wavelet time-frequency mesh map, time-frequency contour map, and time-frequency profile plot. This paper also presents a comparative study of both Daubechies wavelet and harmonic wavelet analysis when applied to pipeline small leak detection under complicated background noises. Results of simulating test and field experiment show that it is possible to distinguish weak non-stationarities from complicated noises by harmonic wavelet analysis in pipeline small leak detection system. The comparison clearly illustrates that harmonic wavelet based pipeline small leakage detection method is significantly more accurate than other wavelets analysis such as Daubechies wavelet. This work provides a reliable and safe guarantee for oil and gas long distance transportation, reducing petroleum product losses and protecting surrounding environment.     

Probabilistic multiple model neural network based leak detection system: Experimental study

This paper presents an effective decision making system for leak detection based on multiple generalized linear models and clustering techniques. The training data for the proposed decision system is obtained by setting up an experimental pipeline fully operational distribution system. The system is also equipped with data logging for three variables; namely, inlet pressure, outlet pressure, and outlet flow. The experimental setup is designed such that multi-operational conditions of the distribution system, including multi pressure and multi flow can be obtained. We then statistically tested and showed that pressure and flow variables can be used as signature of leak under the designed multi-operational conditions. It is then shown that the detection of leakages based on the training and testing of the proposed multi model decision system with pre data clustering, under multi operational conditions produces better recognition rates in comparison to the training based on the single model approach. This decision system is then equipped with the estimation of confidence limits and a method is proposed for using these confidence limits for obtaining more robust leakage recognition results.     

A review on different pipeline fault detection methods

Pipeline faults like leakage and blockage always create problem for engineers. Detection of exact fault quantity and its location is necessary for smooth functioning of a plant or industry and safety of the environment. In this paper brief discussion is made on various pipeline fault detection methods viz. Vibration analysis, Pulse echo methodology, Acoustic techniques, Negative pressure wave based leak detection system, Support Vector Machine (SVM) based pipeline leakage detection, Interferometric fibre sensor based leak detection, Filter Diagonalization Method (FDM), etc. In this paper merit and demerits of all methods are discussed. It is found that these methods have been applied for specific fluids like oil, gas and water, for different layout patterns like straight and zigzag, for various lengths of pipeline like short and long and also depending on various operating conditions. Therefore, a comparison among all methods has been done based on their applicability. Among all fault detection methods, Acoustic reflectometry is found most suitable because of its proficiency to identify blockages and leakage in pipe as small as 1% of its diameter. Moreover this method is economical and applicable for straight, zigzag and long, short length pipes for low, medium and high density fluid.     

基于声信号特征分析的燃气管道探测识别方法

为了探测和辨识地下燃气管道,避免燃气管道改扩建的过程发生第三方破坏引发安全事故,提出1种基于声信号特征分析的燃气管道探测识别方法,该方法考虑燃气管道声信号声压级低以及易衰减的特点,采用Hilebert-Huang变换算法分析燃气管道流噪声信号特征,建立燃气管道流噪声信号的特征数据库,并通过BP神经网络进行模式识别,判别...     

Leak location of pipelines based on characteristic entropy

The leakage of oil/gas pipelines is one of the major factors to influence the safe operation of pipelines. So it is significant to detect and locate the exact pipeline leakage. A novel leak location method based on characteristic entropy is proposed to extract the input feature vectors. In this approach, the combination of wavelet packet and information entropy is called “wavelet packet characteristic entropy” (WP-CE). The combination of empirical mode decomposition and information entropy is called “empirical mode decomposition characteristic entropy” (EMD-CE). Both pressure signal and flow signal of low noise and high noise of pipeline leakage are decomposed to extract the characteristic entropy. The location of pipeline leak is determined by the combination of the characteristic entropy as the input vector and particle swarm optimization and support vector machine method (PSO-SVM). The results of proposed leak location method are compared with those of PSO-SVM based on physical parameters. Under the condition of high noise, the results of proposed leak location method are better than those of PSO-SVM based on physical parameters.     

Image rotation method for identification of NPW signals in the localization of pipeline leakage

Gas and oil are mainly transported through long-distance pipelines on land. Pipeline leaks lead to severe hazards to the environment and economy and even imperil human lives. Negative pressure wave (NPW)-based methods are fast and effective for leak monitoring and localization. The key problem for an NPW-based method is to determine the NPW and its arrival time, which is characterized by the knee point in the time domain signal. In this paper, an image rotation method is proposed based on the shape characteristic of the time domain signal induced by an NPW. Through image rotation, the knee point turns into the highest point, which is easy to detect. To verify the performance of the proposed method, leakage experiments were conducted on liquid and gas pipeline models. Previously developed FBG pipe fixture sensors were used to detect an NPW. These sensors were equidistantly installed on the pipeline, forming a sensor array. Based on the sensing array, a novel leak localization algorithm was used to compute the leakage position. The experimental results indicated that the image rotation method has good performance for identifying an NPW, even though many noise- and pressure-induced fluctuations exist in the signals. This method enables automated real-time monitoring and has potential for practical application.     

Integrated leakage detection and localization model for gas pipelines based on the acoustic wave method

With the development of natural gas transportation systems, major accidents can result from internal gas leaks in pipelines that transport high-pressure gases. Leaks in pipelines that carry natural gas result in enormous financial loss to the industry and affect public health. Hence, leak detection and localization is a major concern for researchers studying pipeline systems. To ensure the safety and improve the efficiency of pipeline emergency repair, a high-pressure and long-distance circular pipe leakage simulation platform is designed and established by similarity analysis with a field transmission pipeline, and an integrated leakage detection and localization model for gas pipelines is proposed. Given that the spread velocity of acoustic waves in pipelines is related to the properties of the medium, such as pressure, density, specific heat, and so on, this paper proposes a modified acoustic velocity and location formula. An improved wavelet double-threshold de-noising optimization method is also proposed to address the original acoustic wave signal collected by the test platform. Finally, the least squares support vector machine (LS-SVM) method is applied to determine the leakage degree and operation condition. Experimental results show that the integrated model can enhance the accuracy and precision of pipeline leakage detection and localization.     

Fault diagnosis of pipeline and pump unit systems using status coupling analysis

Earlier studies on fault diagnosis of the pipeline and pump unit systems (PPU) relied mainly on independent equipment analyses, which usually lead to false alarms because of the loss of information fusion. The aim of this study is to utilize the status coupling relationship to improve fault detection sensitivity and reduce false alarm rate. A real-time status identification of related equipment step is added between capturing abnormal signals and listing out diagnosis results. For example, when the pipeline pressure fluctuation is found abnormal, a status analysis of pump units is performed immediately, if the pump units are proven to be operational normally, then the pipeline leak alarm is acknowledged valid. The logical reasoning algorithm is used to capture abnormal conditions of pipeline pressures. The pump unit faults are captured by combining information from multiple sources. Field applications show that the proposed method significantly improves the PPU fault detection capability on fault detection sensitive and accuracy.     

基于广义概念的城市燃气管道泄漏精确定位

为了准确地检测城市燃气管道泄漏,提出了一种基于广义概念的管道泄漏检测定位方法。声发射技术对于管道泄漏的检测、定位是一个极好的工具,但由于泄漏源的传播容易受到周围背景噪声以及复杂工况的影响,其定位误差较大。基于时延估计的互相关信号处理方法被广泛用于管道泄漏检测定位,但由于泄漏应力波传播通道的动态特性,使得源信号在传播过程中会产生波形变化,给互相关函数峰值位置的确定带来困难。由此引入广义相关分析方法,通过对信号进行前置滤波,在一定程度上减少了传播通道动态特性因素对泄漏点定位的不利影响,得到了更为准确的时延估值。在此基础上,通过模拟实验,编写Matlab神经网络代码,构造GRNN模型,进一步预测定位。结果表明,GRNN预测的声发射检测值、互相关定位值以及广义相关定位值,相比之前定位精度分别得到提高,其中基于广义相关的延时估计方法定位最为精确,将该方法用于工程实际中,可以更加精确地定位出泄漏点。     

Decentralized scheme for leaks’ location in a branched pipeline

This article presents a novel scheme for the detection and isolation of single leaks in a pipeline with a branch junction by measuring only the flow rate and pressure at the ends of the line. For the solution of this realistic issue, which commonly occurs in pipeline networks, the position of the branch junction and its flow rate are supposed to be known. The idea consists of deriving a model considering the branch junction as a known point in the pipeline such that the leak position is characterized by two possible orientations with respect to the branch (upstream and downstream of it). Thus, this model allows proposing a diagnosis scheme which includes a static selector and two identifiers designed by using a continuous extended Kalman filter with only one deviation parameter to be estimated. This framework reduces the identification task to one parameter which is associated with the deviation from a prescribed positive base position located between one of the pipeline ends and the branch. Simulation and experimental results with data of a hydraulic pilot pipeline of 200 [m] show the promise of the novel scheme.     

Pipeline leakage detection and isolation: An integrated approach of statistical and wavelet feature extraction with multi-layer perceptron neural network (MLPNN)

Leakage diagnosis of hydrocarbon pipelines can prevent environmental and financial losses. This work proposes a novel method that not only detects the occurrence of a leakage fault, but also suggests its location and severity. The OLGA software is employed to provide the pipeline inlet pressure and outlet flow rates as the training data for the Fault Detection and Isolation (FDI) system. The FDI system is comprised of a Multi-Layer Perceptron Neural Network (MLPNN) classifier with various feature extraction methods including the statistical techniques, wavelet transform, and a fusion of both methods. Once different leakage scenarios are considered and the preprocessing methods are done, the proposed FDI system is applied to a 20-km pipeline in southern Iran (Goldkari-Binak pipeline) and a promising severity and location detectability (a correct classification rate of 92%) and a low False Alarm Rate (FAR) were achieved.     

某单井站场管道积液及腐蚀特性研究*

为应对湿天然气管道存在的明显积液,导致管材腐蚀,目前经验性的检测点选择方式导致检测成本和工作量过大问题。基于某单井站场工况条件和现场壁厚检测数据,针对站场主管线,采用Fluent软件建模分析管线内液相分布规律。结果表明:含液率大小随原料气含水量、流量、压力的变化而呈正相关变化,运用Correl函数对现场壁厚检测数据和模拟结果进行对比验证,发现两者的相关度达到0.61以上,这为现场合理选择腐蚀检测点提供依据,降低了现场检测工作量和检测成本。     

A dual-sensor-based method to recognize pipeline leakage and interference signals

During the detection of pipeline leakages, false alarms of leak detection could be markedly reduced if the interference signals resulting from pressure regulating, pump regulating or valve movements could be accurately distinguished. A digital recognition method for interference signals and leakage signals based on a dual-sensor system is proposed in this paper. It is demonstrated that the direction of the signal can be recognized by a cross-correlation calculation between two signals from the dual-sensor, one of which undergoes forward linear interpolation and backward linear interpolation. Based on this theory, the interference signal and the leak signal can be discriminated exactly, and the distance between the two sensors in the dual-sensor system can be considerably reduced without needing to increase the sampling frequency. The monotonicity of the cross-correlation function is demonstrated, and a fast discrimination algorithm based on a binary extreme search method, which decreases the computational load and maintains global optimization, is also proposed. A pre-processing method of the actual signal is proposed to decrease the identity requirement for the two sensors in a dual-sensor system. In the experiment based on artificial signals, the proposed discrimination algorithm could achieve accurate recognition of the abnormal signal, and as such, the theory and application of pipeline leak detection based on dual-sensor systems are extended.     

城市管道天然气在土壤中泄漏扩散实验研究

天然气在土壤中扩散行为的实验研究对埋地管道泄漏点的科学定位及泄漏事故的预防具有重要意义.采用全尺度气体泄漏实验系统,模拟真实埋地管道泄漏场景,对泄漏后的天然气在土壤中的扩散对流过程进行实验研究.基于自行研制的气体检测与数据采集系统和GasClam地下气体在线监测仪,分析天然气在土壤中的对流扩散规律.结果表明:埋地管道泄漏后天然气在土壤中的对流扩散过程可以分为4个阶段:孕育阶段、陡然增长阶段、缓慢增长阶段和稳定阶段,其浓度随泄漏时间的变化过程符合S型曲线特征.天然气扩散至检测点所需时间与距泄漏口距离呈现近似的幂指数关系.当检测点位于泄漏口附近区域时,泄漏压力起主导作用.当检测点位于远离泄漏口区域时,泄漏量起主导作用.