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.     

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.     

A wave change analysis (WCA) method for pipeline leak detection using Gaussian mixture model

This paper presents a novel pipeline leak detection scheme based on gradient and slope turns rejection (GSTR). Instead of monitoring the pipeline under constant working pressure, GSTR introduces a new testing method which obtains data during the transient periods of different working pressures. A novel pipeline leak detection method based on those transient data without failure history is proposed. Wavelet packet analysis (WPA) is applied to extract features which capture the dynamic characteristics from the non-stationary pressure data. Principal component analysis (PCA) is used to reduce the dimension of the feature space. Gaussian mixture model (GMM) is utilized to approximate the density distribution of the lower-dimensional feature space which consists of the major principal components. Bayesian information criterion (BIC) is used to determine the number of mixtures for the GMM and a density boosting method is applied to achieve better accuracy of the distribution estimation. An experimental case study for oil pipeline system is used as an example to validate the effectiveness of the proposed method.     

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.     

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.     

鲸鱼优化算法下气体泄漏源波达方向估计法

为减小压力容器气体泄漏实时位置估算误差,准确监测容器工况,首先,从声学监测角度提出一种引入鲸鱼优化算法(WOA)的泄漏源估计方法,采用波达方向(DOA)估计法预测气体泄漏位置方向,获得泄漏源角坐标;然后,引入WOA自适应选择方法分解DOA的特征值,多次迭代得到最精确的泄漏位置;最后,以某化工厂中压力容器数据为实际算例,...     

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.     

Safety performance functions with measurement errors in traffic volume

One of the most common and important predictors in safety performance functions (SPFs) is traffic volume which is known to be measured with uncertainty. Such measurement errors (ME) can attenuate the respective predictors’ effect and also increase dispersion. This paper proposes an approach which involves the use of a ME model based on traffic flow time series data. The model is used in conjunction with the negative binomial SPF to circumvent the bias in predicting the aggregate number of accidents during the time period under study. The proposed approach (denoted by MENB), was compared with the traditional negative binomial (NB) technique by way of Monte Carlo simulation. Furthermore, both approaches were applied to two datasets corresponding to 131 and 130 road segments in British Columbia. The full Bayes method was utilized for parameter estimation, performance evaluation and inference through the use of Markov Chain Monte Carlo (MCMC) techniques. The simulation results showed that MENB has outperformed NB when large measurement errors are present. The goodness-of-fit statistics showed that MENB has provided a slightly better fit to the data. However, in the presence of measurement errors, the NB has underestimated the predicted number of accidents for heavy traffic on long road segments and vice versa. The use of MENB is justified when the variance in volume between years is large otherwise both approaches yield comparable results.     

ARX modeling approach to leak detection and diagnosis

This work presents a time series strategy for detection, location and quantification of leaks in large pipeline systems. The technology has two active components, which operate sequentially: the Detector and the Localizer. The Detector continuously screens real-time data, searching for any anomalies such as leaks, which are detected (or not) depending on their size and position. The Detector is based on auto-regressive multi-input/multi-output (MIMO) ARX predictors with one input filter. Subsequent to successful leak detection, the Localizer is launched to diagnose the leak via estimation of its parameters – diameter and location – using recorded data on a Search Time Window that includes information in the neighborhood of the instant of detection. The Localizer is also an ARX predictor, but with two input processors, the first is a filter for dynamic plant inputs and the second filter processes “parameter signals” of active leaks. The Localizer is developed beforehand via model identification with plant data under the action of known, artificially simulated, leaks. It is, therefore, able to recognize an active pattern of leak parameters, by maximizing the adherence of its predictions to data in the Search Time Window. The proposed detection and location methods were successfully tested in simulated leak scenarios for an industrial naphtha pipeline.     

基于贝叶斯估计的炼化装置动态告警管理方法研究

针对现有告警管理方法无法判断炼化装置运行中的过渡过程,对过渡过程不能进行准确有效地监测和管理,提出 了基于贝叶斯估计的动态告警线计算方法。通过训练历史过渡过程数据得到先验概率,自适应判断过渡过程和估计动态 告警线,解决了传统告警线的斜率不能赋值且只能依靠人为调节的问题,并可通过判断系统状态调节告警管理方式。经 常压塔原油进料流量调整和减压炉干气流量工艺调节的过渡过程现场数据验证,结果表明:与传统告警管理方法相比, 基于贝叶斯估计的动态告警管理方法的误告警总数量减少了87.34%,避免了告警洪水的发生,提高了炼化工艺运行的安 全性和可靠性。     

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.     

基于VPL的输油管道实时泄漏检测系统

针对现有管道泄漏检测技术在管道微小泄漏检测和油品泄漏损失计算方面存在的不足,基于流体动力学和容量守恒原理,开发基于VPL（Visual Pipeline）的输油管道实时泄漏检测系统。建立管道模拟平台,通过组态编辑器、图像界面、管道工作室和SCADA 接口,开展输油管道实时泄漏检测、定位与分析,实现管道实时泄漏检测、报警,以及泄漏量的定量计算。现场测试结果显示,所开发的输油管道泄漏检测系统最小泄漏检出率为0.7%,泄漏点定位精度为96.96%,泄漏量计算准确率为94.42%。研究结果表明:基于VPL的输油管道实时泄漏检测系统漏报、误报率低,检测定位精度较高,尤其对于微小泄漏优势明显;同时,该系统能够实时定量计算油品泄漏损失,可以用于输油管道泄漏检测与应急辅助决策。     

Leak detection of hydrogen–natural gas mixtures in pipes using the characteristics method of specified time intervals

This paper presents a technique for detection and location of leaks in a single pipe, by means of transient analysis, of hydrogen–natural gas mixtures flows. The method uses transient pressure waves initiated by the sudden closure of a downstream shut-off valve. The presence of a leak in a pipe partially reflects these pressure waves and allows for the location of the leak. Pressure waves are governed by two coupled non-linear, hyperbolic partial differential equations with pressure dependent coefficients. The fluid pressure and velocity are considered as two principal dependent variables. To determine the leak location, the mathematical formulation has been solved by the characteristics method of specified time intervals. The computed results describe the influence of the leak on pressure time-history and the effect of hydrogen mass fraction in the mixture on the leak discharge behaviour. It was found that transient pressure is much important in the case of hydrogen than that in the case of natural gas.     

Reducing security vulnerabilities for critical infrastructure

In this paper, we show the need for improved Process Control System (PCS) security, and describe some of the promising research areas in PCS security. One implementation of PCS in critical infrastructure and factory automation is a supervisory, control, and data acquisition (SCADA) system, a real-time industrial process control system which centrally monitors and controls remote and/or local processes utilizing plant, equipment, or devices (such as switches, valves, pumps, relays, etc.) while collecting and logging field data. Current SCADA systems are distributed, networked, and dependent on open protocols for the internet, which are exposed to remote cyber terrorism. They are particularly vulnerable to unauthorized access. We give some examples of SCADA processes with natural gas control systems in USA and the Ubiquitous Sensor Network (USN) in Korea. We also examine a representative vulnerability and corresponding measures for security, and present an example of concrete measures for the security of mass transportation as a critical infrastructure.     

瞬态温度载荷下固液混合装药内部温度响应研究

为了研究瞬态温度载荷作用下固液混合装药起爆点火过程,设计了瞬态温度载荷试验装置和瞬态温度测试系统。用黑火药爆炸温度作为瞬态温度载荷,测试了固液混合装药内部的温度响应过程,并与数值模拟进行对比分析。结果表明,设计的瞬态温度载荷测试系统可以研究装药对瞬态温度的响应。     

Leak location of pipelines based on transient model and PSO-SVM

An improved and integrated approach of support vector machine and particle swarm optimization theory (PSO-SVM) is first used to detect the leak location of pipelines and overcome the problem of multiple leaks. The calibration and predictive ability of improved PSO-SVM is investigated and compared with that of other common method, back-propagation neural network (BPNN). Two conditions are evaluated. One with a leak involves a set of 20 samples, while another with two leaks has 127 samples. Both internal and external validations are performed to validate the performance of the resulting models. The results show that, for the two conditions, the values calculated by improved PSO-SVM are in good agreement with those simulated by transient model, and the performances of improved PSO-SVM models are superior to those of BPNN. This paper provides a new and effective method to inspect the multiple leak locations, and also reveals that improved PSO-SVM can be used as a powerful tool for studying the leak of pipeline.     

天然气集输站泄漏监控系统研究

为克服目前天然气集输站站控系统存在的主要技术问题,探讨了建立泄漏监控安全系统的技术要求.该系统应用负压波检漏技术、模式识别技术和虚拟仪器技术,实现了.对天然气泄漏的信号检测、处理、传输和实时动态显示等.借助于这些技术开发的天然气泄漏监控系统,具有数据处理准确可靠,精度高,误报率低等特点.通过该系统,可以及时获取相关参数和信息,实现早期预警,降低天然气泄漏的事故风险.     

Dispersion modeling approach for quantification of methane emission rates from natural gas fugitive leaks detected by infrared imaging technique

Recently, infrared optical imaging has been applied in the oil and gas industry as a method to detect potential leaks in pipelines, components and equipment. The EPA suggested that this impending technique is considered as a smart gas LDAR (leak detection, monitoring and repair) for its rapid recognition of leaks, accuracy and robustness. In addition, compared to the conventional method using Total Vapor Analyzer (TVA) or gas sniffer, it has several other advantages, such as the ability to perform real-time scanning and remote sensing, ability to provide area measurement instead of point measurement, and provide an image of the gas which is not visible to naked eye. However, there is still some limitation in the application of optical imaging techniques; it does not give any measurement of gas emissions rates or concentrations of the leaking gas. Infrared cameras can recognize a target gas and distinguish the gas from its surrounding up to a certain concentration, namely the minimum detectable concentration. The value of the minimum detectable concentration depends on the camera design, environmental conditions and surface characteristics when the measurement is taken. This paper proposed a methodology to predict gas emissions rates from the size of the dispersed gas plume or cloud to the minimum detectable concentration. The gas emissions rate is predicted from the downwind distance and the height of the cloud at the minimum detectable concentration for different meteorological conditions. Gas release and dispersion from leaks in natural gas pipeline systems is simulated, and the results are presented.     

Two leaks detection in viscoelastic pipeline systems by means of transient

This paper presents a technique for detecting and locating leaks in a single viscoelastic pipe, by means of transient analysis. The system studied is a reservoir-pipe-valve structure. The viscoelastic behavior of the pipe wall material is modeled by a generalized KelvinVoigt model. To determine the leak location, the mathematical formulation has been solved by the method of characteristics. The approach by the method of characteristics is often chosen because it is based on the concept of acoustic wave propagation which is the main mechanism of all transient events considered. The presence of the two leaks in a pipe partially reflects pressure waves initiated by the sudden closure of a downstream shut-off valve. These waves affect the shape and the amplitude of the time-history-pressure. The computed results describe the influence of the presence of two leaks on pressure time-history and the effect of leaks locations and sizes on the pressure signal behavior. The effect of the pipe wall viscoelasticity on the two leaks detection and sizing is also discussed. The leaks discharges are determined by resolving two independent equations derived from literatures and based on transient analysis. The friction and leaks depths effects on two leaks locations and sizing are involved.     

基于TNPE的智能电网虚假数据注入攻击检测*

为实现智能电网中虚假数据注入攻击的实时检测,提高电力系统运行的安全性,采用1种基于时序近邻保持嵌入的方法,对正常状态下采集到的电网历史量测数据建立离线模型,得到T2统计限,将实时数据通过模型获得的T2统计量与离线模型的统计限进行对比,若超过统计限,则说明存在虚假数据注入攻击。该方法在提取局部空间结构特征的基础上,可同时获得与时间相关的动态特征。在IEEE30节点测试系统上进行仿真实验,并与ICA,PCA,NPE方法进行比较。结果表明:所提方法有高达100%的检测率,且有较低的误报率,能够有效应用在虚假数据注入攻击的检测中。