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.     

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.     

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.     

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.     

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.     

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.     

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.     

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。     

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.     

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.     

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.     

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.     

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.     

Acoustic detection technology for gas pipeline leakage

Gas leakage from pipeline leads to significant environmental damages and industrial hazards, so small leakage detection for gas pipeline is essential to avoid these serious leakages. However, because of the high frequency component of leakage signal attenuates quickly, traditional detection method which inspects pressure or vibration signal has problem to get effective information from leakage signal. So, a novel detection method based on acoustic wave is proposed. This paper, firstly, researches on the phonation principle of pipeline leakage and the characteristic of sound source, and simulates the leakage acoustic field on the basis of aero acoustics. Secondly, using Wavelet Packet Transform method and Fuzzy Support Vector Machine pattern classification, the laboratory testing for identifying acoustic signal of gas pipeline leakage is presented. Finally, the field application demonstrates that the detection system could identify small gas leakage effectively and avoids false-alarms which caused by running conditions with a good prospect.     

基于FUZZY-AHP的危化品管道运输 安全等级综合评判模型与应用

为有效控制与降低危化品管道运输风险,以国家安全生产法和危化品输送管道安全管理规定为基础依据,与我国危化品管道运输现状相结合,建立以危化品管道运输安全等级为目标层的评价指标体系后,基于层次分析法(AHP)理论与模糊数学综合评价法(FUZZY),构建其综合评价模型。通过实例验证表明,该模型对危化品管道运输安全等级的确立科学且有效,所得结论对于提升危化品管道运输安全等级有较好的理论和实践指导作用。     

基于模糊贴近度的组织安全文化评估方法

鉴于组织安全文化评估过程中,各因素指标及其相互作用关系具有模糊性,引入模糊数学理论,提出一种基于模糊贴近度的组织安全文化评估(SCA)方法。首先,构建组织安全文化因素指标体系以及确定SCA特征状态模式;其次,依据模糊语言隶属度函数以及特征状态模式,确定指标与SCA等级的隶属度;第三,确定指标与各等级的非对称贴近度,依据模糊贴近度判断矩阵进行评估决策;最后,以核电组织安全文化为例,通过对比分析进行方法的验证。实例分析结果的一致性表明:该方法能有效量化系统状态与各评估等级之间的贴近程度,能解决安全文化以及评估状态的模糊性问题,且评估结果与实际状态相吻合。     

压力脉冲波法复杂天然气管道堵塞检测实验研究*

为了解决复杂天然气管道堵塞定位难题,快速定位出堵塞位置,提出利用压力脉冲波法检测复杂管道堵塞。改造搭建长18.1 m、包含17个三通结构的压力波堵塞检测实验台,进行不同堵塞率以及气液混输管道的堵塞定位实验。结果表明:管道三通会引起频率范围为150~200 Hz的高频反射波,利用FFT谐波变换方法可以有效地对原始信号进行滤波分析,更利于对堵塞定位分析。在堵塞率100%时,定位误差为1.07%;在堵塞率50%时,定位误差最大,达到1.93%;对于含液率8%的气液混输管道,100%堵塞时,定位误差为0.48%。研究结果有效地证实了压力脉冲法检测复杂输运管道堵塞的可行性,可为该方法的现场应用提供指导和数据支撑。     

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.     

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.     

基于人机工程的物流运输三参数风险评估模型

为了有效分析物流运输的风险状况,针对传统风险评估技术的不足,构建了一种以风险发生概率、风险后果和风险重要度为技术参数的物流运输风险三参数评估模型。首先,依据安全人机工程原理系统分析物流运输过程风险的影响因素,并建立物流运输风险评价指标体系。然后考虑风险因素的随机性和模糊性,提出了一种基于三角模糊数和模糊层次分析耦合的参数确定方法。最后,通过一个算例分析说明构建模型的有效性。结果表明,所提出方法用于物流运输风险定量分析优于传统风险评估技术。