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Pipelines are a nonlinear and complex component to transfer fluid or gas from one place to another. From economic and environmental points of view, the safety of transmission lines is incredibly important. Furthermore, condition monitoring and effective data analysis are important to leak detection and localization in pipelines. Thus, an effective technique for leak detection and localization is presented in this study. The proposed scheme has four main steps. First, the learning autoregressive technique is selected to approximate the flow signal under normal conditions and extract the mathematical state-space formulation with uncertainty estimations using a combination of robust autoregressive and support vector regression techniques. In the next step, the intelligence-based learning observer is designed using a combination of the robust learning backstepping method and a fuzzy-based technique. The learning backstepping algorithm is the main part of the algorithm that determines the leak estimation. After estimating the signals, in the third step, their classification is performed by the support vector machine algorithm. Finally, to find the size and position of the leak, the multivariable backstepping algorithm is recommended. The effectiveness of the proposed learning control algorithm is analyzed using both experimental and simulation setups.

It is not uncommon for underground water supply or heating pipelines to break and leak, but such events can cause serious inconvenience to the lives of residents. The accurate and timely detection of the leak locations is the primary objective in solving pipeline rupture issues. Based on the direct current (DC) resistivity method, which provides a stable signal, convenient interpretation, and strong anti-interference ability, we adopted the pole-pole device form of the DC potential method to detect the locations of leaks in high-resistivity water supply or heating pipelines. A finite element numerical simulation method was used to study the characteristics of the leakage point potential distribution with and without metal pipeline interference. The numerical simulation results show that the pole-pole DC potential method has strong anti-interference ability and can effectively locate locations of leaks in pipelines with metal interference. The numerical simulation results were verified by physical experiments. The research results show that the pole-pole DC potential method can effectively detect the locations of leaks in high-resistivity pipelines.

There is an increasing need for timely pipeline leak detection and localization methods, pipeline leak could lead to not only the loss of the goods but also considerable environmental and economic problems. With the rapid development of hardware and software, the pipeline leak detection and localization algorithms have been widely researched and applied in many Fields. However, traditional methods are usually limited by extracting features manually, which is inefficient and time-consuming. Convolutional neuron network is an effective method to extract features automatically. In this paper, a novel ensemble transfer learning one-dimension convolutional neural network (TL1DCNN) for the pipeline leak detection and localization is proposed. The TL1DCNN plays the role of base learner. The results of a set of obtained base learners are integrated to achieve the task of pipeline leak detection and localization. Firstly, one-dimension convolutional neural network (1DCNN) models with different parameters are pretrained with source domain data. A small learning rate is set to retrain the above 1DCNN models for target task with target domain data in order to obtain TL1DCNN base learners. Then, the four ensemble strategies with different number base learners whose ensemble weights are optimized by particle swarm optimization algorithm are obtained by minimizing the sum of similarity. The dataset simulated by pipeline network model is used to evaluate the effectiveness of the proposed approach. The indicators such as classification accuracy, precision, recall, F_score and confusion matrix are used to compare the proposed approach with traditional methods and other deep learning methods. The experimental results show that the performance of the proposed approach is superior to other compared methods.

Aiming at the difficulty of leak detection of urban hazardous chemical pipelines, this paper proposes a method for locating pipeline leaks based on the complementary ensemble local mean decompositi...

Wireless sensor networks (WSN) provide a powerful solution to the task of monitoring the operational conditions of buried and non-buried pipes of different lengths and materials. Due to the limited energy stored in the sensor nodes, the use of low-power vibration sensors becomes the preferred choice. However, the monitoring of vibrations for leak detection in wall-mounted pipelines, and the associated complexities are not adequately dealt with in the literature. This article offers to fill this gap by presenting a feasibility study of leak detection in wall-mounted water pipelines through vibrations measurements using low-power accelerometers. The work is divided into two steps: Firstly, a careful analysis is performed to understand the effect of various fittings such as clamps, bends, and leaks of various sizes, on the vibrations produced. Then this knowledge is used to find the best locations for placement of nodes in order to efficiently detect leaks of various sizes. This analysis revealed two important facts: (a) difficulty in detecting medium-size leaks as their vibrations and those from the no-leak condition are very indistinguishable, (b) vibrations measured away from the leak are of a small benefit to the leak detection process. Consequently, 3 different learning models are applied, all fed with information from multiple nodes, in order to reliably detect leaks and classify their size. Comparing the performances of these models shows that the Support Vector Machine (SVM)-based model gives the best results, in that for the worst case of medium-size leaks and with the use of one sensor, the worst accuracies for leak detection and leak size classification have remarkably been improved from being respectively 51% and 36% with one sensor, to being 88% and 93%, respectively, with only a moderate increase in the number of sensors to four.

Статья посвящена актуальной проблеме электроснабжения систем обнаружения утечек и электроприводов запорной арматуры на нефтепроводах. Приведен обзор существующей нормативно-технической документации, регламентирующей установку систем обнаружения утечек и запорной арматуры на проектируемых и эксплуатируемых нефтепроводах. Рассмотрены требования к расположению систем обнаружения утечек и запорной арматуры на нефтепроводах, а также требования к их электроснабжению. Авторами предложено решение проблемы автономного электроснабжения узлов сбора информации систем обнаружения утечек и электроприводов запорной арматуры на нефтепроводах. В качестве решения предлагается использование разрабатываемого электротехнического комплекса на базе термоэлектрогенераторных модулей и фотоэлектрических панелей. В работе представлены результаты компьютерного моделирования работы электротехнического комплекса.

The increase in pipeline safety would prevent incidents that can result in fatalities, environmental disasters, and economic losses. The present work proposes a technique that combines acoustic sensors and machine learning algorithms to identify and locate leakages in low-pressure gas pipelines. The patterns on the sound signal captured by microphones were used to accomplish those two tasks. The technique aims to solve two persistent problems, the detection of small leakages on pipelines operating under low pressures and the reduction of false alarms in the presence of external disturbances. The experimental results showed that the method identified 99.6% of the leakages and achieved a rate of false alarms of 0.3%, while the position of the leakages was estimated with a maximum location error of 4.31%. These results show that the technique proposed is an efficient and reliable alternative to monitor low-pressure pipelines.

Leak detection of gas pipelines has attracted extensive attention in recent years because such a leak could result in significant damage to society. This paper proposes an integrated leak detection method using acoustic signals based on wavelet transform and Support Vector Machine (SVM). Specifically, the optimal wavelet basis is selected by the entropy-based algorithm adaptively, with which acoustic signals gathered by acoustic sensors are first pre-processed by wavelet transform. Then useful features containing leak severity information are extracted from multi-domain components of the acoustic signals. Moreover, for leak detection and severity classification, the Relief-F algorithm is applied to select the most discriminative features. Furthermore, selected features are used as the input of SVM classifiers to identify the leak severity of gas pipelines. The effectiveness of the proposed method is validated using laboratory experiments. The results demonstrate that the proposed method achieves high accuracy of 99.4% to determine the leak state and non-leak state by using the first three most discriminative features and 95.6% to classify the normal and several leak severity conditions by using the first five most discriminative features. Therefore, it is effective for leak detection and promising for the development of a real-time monitoring system.

Detecting small leaks in time can prevent an accident from getting worse, which plays an extremely important role in safe pipeline transportation. In order to improve the ability to detect ...

Detection of anomalies in pipe networks (leaks, blockages, and wall deterioration) is critical for targeted pipe section replacement and maintenance in water distribution systems. A hydraul...

Leaks in water pipelines can create many problems, such as financial loss, environmental pollution, and public health hazards. Previous scholars and engineers have researched leak detection...

International audience; One of the major failures attributed to pipeline transportation of crude oil is oil leakages and spills. Hence, it is monitored via several classical leak detection techniques (LDTs), which are more recently implemented on centralised wireless sensor networks (WSN)-based leak detection and monitoring systems (LDMS). However, the LDTs are sometimes prone to high false alarms, and the LDMS are sensitive to single points of failure. Thus, we propose HyDiLLEch, a distributed leakage detection and localisation technique based on a fusion of several LDTs. In this work, we implemented HyDiLLEch and compared it to the individual LDTs in terms of communication efficiency and leakage detection and localisation accuracy. With HyDiLLEch, the number of nodes detecting and localising leakages increases by a maximum of four to six times, thereby eliminating single points of failures. In addition, we improve the accuracy of localisation in nodes physically-close to the leak and maintain an average of 96% accuracy with little to no communication overhead.

Big impacts from pressure transients are known to be major contributors to pipeline failures, and even small impacts have the potential to result in pipeline deterioration. The effects of these impacts on a pipeline are not disclosed in detail and are hard to evaluate completely by theoretical modeling or numerical simulation. The impacts excite cylindrical structures containing liquid, which results in the development of wave propagation along pipelines. In particular, quasi-longitudinal waves are known to be developed by fluid-structure interaction during propagation. However, impact signal detection needs great care because extreme noise may corrupt the signal. An enhanced wavelet-based approach is proposed to detect and localize the impact source. The method makes use of continuous wavelet transform and band summation within a band of interest along scales to enhance time-difference detectability. In addition, a noise reduction algorithm intended to remove burst noises, in practice, frequently contaminates the impact signals of interest. Experimental results from a water supply network under operation demonstrate that the proposed approach is able to suppress noise and successfully reveal the impact location. The proposed approach provides a more precise and robust way to localize impacts missed by the conventional cross-correlation algorithm.

The article describes a study using discrete fibre optic sensing as a means of leak detection on water distribution pipes installed in unsaturated ground. A short length of pipe fitted with artificial leak points was installed, to which a fibre optic cable with fibre Bragg gratings was attached. An optical fibre with fibre Bragg gratings was also installed in the ground parallel to but separate from the pipe. Thermistors were installed at selected locations to measure temperature changes independent of strain. It was found that a simulated water leak resulted in clearly detectable temperature changes and thermally induced fibre Bragg grating wavelength changes in the ground around the pipe. However, significantly larger deformation-induced fibre Bragg grating wavelength changes were measured on the pipe walls and also in the initially unsaturated ground in response to leaks. A wetting front originating from a water leak propagating through unsaturated soil is associated with significant effective stress changes because the infiltrating water alters the ambient matric suction in the soil. This effective stress change is associated with significant ground deformation resulting in a fibre Bragg grating response which significantly exceeds the thermal response associated with (usually) colder water leaking into unsaturated soil. The study illustrates advantages of measuring ground deformation-induced fibre Bragg grating wavelength changes over pure temperature changes as an efficient means of leak detection in unsaturated ground. However, due to the limited number of fibre Bragg gratings that can be monitored along a single optical fibre, a leak detection system suitable for practical implementation should be based on distributed fibre optic strain sensing, an aspect requiring further research.

In this paper, we present an experimental investigation of a water pipeline leak detection system based on a low-cost, tiny-sized hydrophone sensor fabricated using the microelectromechanical system (MEMS) technologies. A 10 × 10 element arrayed MEMS hydrophone device with chip size of 3.5 × 3.5 mm $^2$ was used in the experiment. The hydrophone device is packaged with a customized on-board preamplification circuit using an acoustic transparent material. The overall package size of the MEMS hydrophone is $\Phi$ 1.2 × 2.5 cm. The packaged MEMS hydrophone achieves an acoustic sensitivity of −180 dB (re: 1 V/ $\mu$ Pa), a bandwidth from 10 Hz to 8 kHz, and a noise resolution of around 60 dB (re: 1 $\mu \text{Pa/}\sqrt{\text{Hz}}$ ) at 1 kHz. A section of ductile iron water pipeline with an internal diameter of 10 cm, wall thickness of 0.73 cm, and length of 30 m is constructed as the test bed for the water leak detection. Two different leak sizes with leak flow rates of about 30 and 180 L/min are designed along the pipe, which is pressurized at 3.2 bar. Analysis of the transient signals and spectrograms shows that the MEMS hydrophone can capture the key acoustic information of the water leak, i.e., identifying the leak and locating the leak position. The measurement results demonstrate the feasibility to construct an affordable, highly efficient, real-time, and permanent in-pipe pipeline health monitoring network based on the MEMS hydrophones due to their high performance, low cost, and tiny size.

One of the largest movers of the world economy is the oil and gas industry. The industry generates billions of barrels of oil to match more than half the world’s energy demands. Production of energy products at such a massive scale is supported by the equally massive oil and gas infrastructure sprawling around the globe. Especially characteristic of the industry are vast networks of pipelines that traverse tens of thousands of miles of land and sea to carry oil and gas from the deepest parts of the earth to faraway destinations. With such lengths come increased chances for damage, which can have disastrous consequences owing to the hazardous substances typically carried by pipelines. Subsea pipelines in particular face increased risk due to the typically harsher environments, the difficulty of accessing deepwater pipelines, and the possibility of sea currents spreading leaked oil across a wide area. The opportunity for research and engineering to overcome the challenge of subsea inspection and monitoring is tremendous and the progress in this area is continuously generating exciting new developments that may have far reaching benefits far outside of subsea pipeline inspection and monitoring. Thus, this review covers the most often used subsea inspection and monitoring technologies as well as their most recent developments and future trends.

The characteristics of acoustic source of gas pipeline leakage determine the accuracy and adaptability of leak detection for gas pipelines based on acoustic method. In order to explore the characteristics of leakage acoustic source of gas pipeline, a physical model of gas pipeline leakage was developed. Initially, generation mechanism of acoustic source of gas pipeline leakage is studied in order to identify the basic principle of leak detection of gas pipeline based on acoustic method. After that, the simulation model is developed according to the basic principle of aeroacoustics, the flow field, and sound source characteristics of the gas pipeline leakage. Then, the experimental platform for the study on acoustic source characteristics of gas pipeline leakage is set up using the basic principle of the leak detection based on acoustic method and simulation results. The characteristics of acoustic source of leakage are obtained through experiments. Finally, the simulation and experimental results of acoustic source of leakage under different operating conditions are compared, and the accuracy of the simulation results is verified, which provides a theoretical basis for the engineering application of leak detection for gas pipeline based on acoustic method. The results show that the acoustic source of gas pipeline leakage is mainly quadrupole source and the leakage acoustic energy is mainly in the range of less than 100 Hz. The mean value of sound pressure level is increased with the pressure in pipeline and the size of leak aperture.

Multiproduct pipelines play an important role in linking the upstream and downstream ends of the petroleum industry. Promoting energy conservation and carbon abatement in a multiproduct pipeline is imperative to the entire petroleum industry in China to move toward sustainable and cleaner production. This paper studies the promotion of technical progress on the operation of a multiproduct pipeline under policy control in terms of economic, energy-saving and carbon abatement potential (EECP) from a long-term perspective, which fills a gap in the reference literature. A complete bottom-up framework for detailed calculations of energy consumption and carbon dioxide emissions for four categories of energy-saving technical measures is proposed, including pipeline scheduling, transmix treatment, pressure loss control and a leak detection system. Three scenarios are applied to forecast the regional demand of the pipeline in response to different low-carbon policy contexts, and a stepwise multiple linear regression is used to realize the forecast of prices of oil products under the regulation of government. To demonstrate the proposed method, a real multiproduct pipeline in the Zhejiang Province, China is used as an example. The results show that two-thirds of selected technical measures are economically feasible. The forecasted EECP of the studied multiproduct pipeline is rather significant, with the NPV, energy-saving potential and carbon-abatement potential being 198.32 million CNY, 60.25% and 49.57% from 2016 to 2050, respectively. The proposed method is not case-specific and can be used for any multiproduct pipeline in China.

In this paper, a novel particle swarm optimization (PSO) algorithm is proposed in order to improve the accuracy of the traditional support vector machine (SVM) approaches with applications in analyzing data of oil pipeline leak detection. In the proposed saturated and mix-delayed particle swarm optimization (SMDPSO) algorithm, the evolutionary state is determined by evaluating the evolutionary factor in each iteration, based on which the velocity updating model switches from one to another. With the purpose of reducing the possibility of getting trapped in the local optima and also expanding the search space, time-varying time-delays and distributed time-delays are introduced in the velocity updating model to respectively reflect the history of previous personal and global optimum particles. The introduction of saturation constraint ensures that the particles will convergence in case that the velocity of the particles is too large. Eight well-known benchmark functions are employed to evaluate the proposed SMDPSO algorithm which is shown via extensive comparisons to outperform some currently popular PSO algorithms. To further illustrate the application potential, the developed framework SMDPSO-based SVM algorithm is exploited in the problem of oil pipeline leak detection. Experiment results demonstrate that the SMDPSO-based SVM method is superior over other well-known classification algorithms.

Water may be a precious resource that ought to be managed fastidiously. But because of leak in water distributed networks an outsized quantity of water is lost annually. The innovative plan is automatism of the water leak detection in water distributed network system. The amount of water distributed is detected using water flow device. The situation of autonomous water leak detection in an exceedingly immense space are often tracked and monitored. The paradigm has been designed for water leak detection. The system is controlled by ATMEGA328 microcontroller. The direction of water is monitored by resistance detector. Correct location and leak are the areas of concern that have been concentrated in the system.

Pipeline systems are a safe method of transporting oil and gas from one place to another, although they suffer from leaks due to external and internal factors and fail to deliver the requir...

In this work, an innovative system for structural health monitoring of metallic pipes is presented. The proposed system relies on using the pipeline as a waveguide for the propagation of an electromagnetic (EM) signal; possible anomalies in the pipe provoke the partial reflection of the propagating EM signal. Different from previous works that address similar applications, in this work, the EM test signal is injected in the pipeline/waveguide through a coaxial/waveguide transition that is made on the pipe surface (rather than on a cross section as generally done). In practical applications, this strategy would allow connecting more easily the test equipment to the operating, buried pipes. In addition, because reflections caused by anomalies may be difficult to identify with adequate accuracy, the authors have developed a dedicated processing strategy for the analysis of the reflections caused by anomalies. More specifically, by analyzing the measured reflected signal and correlating it to the ideal reflected signal, it is possible to improve the localization of anomalies along the pipe. The proposed system, along with the processing and operating strategies, was validated through full-wave simulations and experimental tests carried out on a steel pipe in the presence of intentionally provoked anomalies.

Pipeline leak detection sensors are created by using fiber Bragg gratings (FBGs) that are inscribed through polyimide coatings of optical fiber with an infrared femtosecond laser and then specially packaged in materials susceptible to hydrocarbons. Depending on the sensor geometry, strain is either applied or released upon exposure to toluene or crude oil., Optical Fiber Sensors 2020, June 8–12, 2020 , Washington, DC, USA

Pipeline leak detection technology is one of the core issues in the field of pipeline safety research. Based on the classification of hardware-based and software-based methods, this paper summarizes the current mainstream pipeline leak detection technology at home and abroad, and expounds the mechanism, advantages and disadvantages and applicable conditions of each detection technology. This paper mainly introduces the pipeline leak detection technology based on DAS system, summarizes the current detection technology of this system at home and abroad, and proposes to apply VMD composite algorithm and deep learning neural network to pipeline leak detection. This paper also puts forward the design of pipeline leakage monitoring platform based on DAS system and the prospect of applying this system to pipeline monitoring in petrochemical wharf.

A better understanding of the sensitivity threshold of external leak detection systems can assist pipeline operators in predicting detection performance for a range of possible leak scenarios, thereby helping them to make more informed decisions regarding procurement and deployment of such systems. The analysis approach described herein was developed to characterize the leak detection sensitivity of select fiber optic cable-based systems that employ Distributed Acoustic Sensing (DAS). The detection sensitivity analysis consisted of two steps. The first step involved identifying a suitable release parameter capable of providing a defensible basis for defining detection sensitivity; the second step involved the application of logistic regression analysis to characterize detection sensitivity as a function of the chosen release parameter. The detection sensitivity analysis described herein provides a means by which to quantitatively determine the leak detection sensitivity threshold for each technology and sensor deployment position evaluated in a set of full-scale tests. The chosen sensitivity threshold measure was the release parameter value associated with release events having a 90% probability of being detected. Thresholds associated with a higher probability level of 95% were also established for comparison purposes. The calculated sensitivity thresholds can be interpreted to mean that release events associated with release parameter values above the sensitivity threshold have a very high likelihood (either 90 or 95%) of being detected.

Most pipeline control systems use some sort of autonomous leak detection system as a safety feature. Among the pipeline leak detection techniques, state observers stand out as the most sophisticated and promising technique. But its use has been inhibited as the dynamic models employed so far are large and estimating the states of nonlinear systems is not trivial. Pipeline pressure and flow dynamics have been modelled in the literature by means of different numerical solutions to a pair of first order partial differential equations that express mass and linear momentum conservation. The numerical solution requires discretizing the pipeline length in a finite number of segments, resulting in a system of equations with size of twice the number of segments. Although there is nothing wrong with this approach, a smaller system is more convenient if one is concerned exclusively with pressure and flow at the pipeline entrance and exit sections. In this paper, energetic modelling principles are employed to obtain a pair of first order ordinary differential equations representing the dynamics of long liquid pipelines. A recently introduced nonlinear observer enables straightforward use of linear, constant-gain observers with Lipschitz nonlinear dynamics. This observer gives the designer freedom to choose the observer eigenvalues and enables mathematically proven asymptotic stability with low gains. In this paper this observer, using a second-order model to represent the pipeline dynamics, is used as a pipeline leak detection algorithm. Initially the observer was employed directly as a leak detection algorithm, the leak being indicated by a non-transient difference between the measured and the estimated flows. Afterwards the leak was modeled as a disturbance flow and a disturbance observer was designed. Both algorithms were verified by means of computer simulations. It was found that the two methodologies are capable of detecting and estimating very small leaks, but the disturbance observer seems capable of indicating small holes further way from the measuring points.

The dataset presented in this article was collected in a laboratory flow circuit, which was designed to investigate high-viscosity flows. The data set is composed of 1200 s (equivalent to 12,000 samples) of mass flow and pressure measurements taken at five points along the pipeline. The first 300 s were recorded when the flow in the loop was composed only of glycerol. The remaining data were acquired when the flow was composed of a water–glycerol mixture. During the data acquisition, two extractions were produced. The research reported in [1] uses 160 s of the data provided here. This article explains in detail the experimental set-up and the principal instruments used for obtaining the dataset. The dataset is in the form of seven columns: Time, Mass Flow, Pressure 1, Pressure 2, Pressure 3, Pressure 4, Pressure 5, in supplementary Excel and Matlab files.

The leak problem in water distribution systems has attracted the attention of many researchers during the past few decades. Water losses through leakages represent an issue that has negative impacts on the environmental and economic systems. Detecting and locating the source of leakage in the water distribution network can significantly reduce the amount of water lost and prevent the destruction of the infrastructure due to water leakage. In this paper, several methods for water leak detection are presented while indicating their advantages and drawbacks. In addition, a new technique based on using multiple sensing wires with different lengths is proposed. This solution consists in detecting and locating water leak by measuring the variations in soil moisture. The experiments made are presented to prove the simplicity, ease of use and the ability of the proposed technique to detect and pinpoint the location of water leak in underground pipelines.

Many methods have been proposed for leak detection in pipelines. The method based on the acoustic signals created by the leakage is a non-destructive method and has presented reliable results. However, applying such a method in noisy environments is still a challenge. In this paper, a leak detection method applied in pipes which are located in a highly noise environment of the process area of an oil refinery is introduced. The proposed detection algorithm takes into account the frequency contents of the noise and the signals of the leaks as well as some statistical characteristics of the noise. Processing of measurements in time windows of 1s are found enough for decision making, keeping the response time of leakage detection very low. Results have shown reliable detection of leaks in sufficient distances without false alarms while there is the ability for the discrimination of some accidental or periodic events from defining them as leaks. The limited processing and storage requirements of the algorithm make it appropriate for efficient implementation.

Condition assessment of water pipelines using fluid transient waves is a noninvasive technique that has been investigated for the past 25 years. Approaches to identify different anomalies a...

In this work, an innovative system for structural health monitoring of metallic pipes is presented. The proposed system relies on exploiting the pipeline as a waveguide for the propagation of an electromagnetic (EM) signal. By analyzing the reflected signal, it is possible to assess the possible presence of anomalies or damage in the pipe.The innovative aspect of the proposed monitoring system is that the EM test signal is injected in the pipeline/waveguide through a coaxial/waveguide transition that is made on the surface of the pipe. In practical applications, this strategy would allow to connect more easily to operating, buried pipes.To characterize the propagation of the EM signal in such conditions and to verify the changes on the reflected signal provoked by variation of the conditions of the pipe, experimental tests were carried out and a dedicated processing strategy was developed. The obtained results, although preliminary, confirmed that the proposed method holds unexplored potential to be used as a structural health monitoring solution.

Pipeline leak detection is critical for targeted maintenance and water loss reduction within water distribution systems. This paper proposes a hydraulic transient, impulse response function...

The purpose of this paper is to provide a structural review of the progress made on the detection and localization of leaks in pipelines by using approaches based on the Kalman filter. To the best of the author’s knowledge, this is the first review on the topic. In particular, it is the first to try to draw the attention of the leak detection community to the important contributions that use the Kalman filter as the core of a computational pipeline monitoring system. Without being exhaustive, the paper gathers the results from different research groups such that these are presented in a unified fashion. For this reason, a classification of the current approaches based on the Kalman filter is proposed. For each of the existing approaches within this classification, the basic concepts, theoretical results, and relations with the other procedures are discussed in detail. The review starts with a short summary of essential ideas about state observers. Then, a brief history of the use of the Kalman filter for diagnosing leaks is described by mentioning the most outstanding approaches. At last, brief discussions of some emerging research problems, such as the leak detection in pipelines transporting heavy oils; the main challenges; and some open issues are addressed.

The article is devoted to a survey of the known basic Time-Delay Estimation (TDE) methods in the application for detecting fluid leaks in pipeline transport and distribution systems. In the paper, both active and passive estimation methods are considered with respect to the presented generalized classification. However, the main attention is paid to the study of passive methods and their algorithmic implementation. Among the passive methods the Absolute Difference Function (ADF) minimizing algorithms, the Basic Cross-Correlation (BCC) algorithm, the adaptive Least Mean Square (LMS) filtering algorithm are implemented, investigated and compared using a mathematical model of a leak noise signal. All the algorithms are described and the results of the comparison are provided. The advantage of the LMS algorithm in processing the noisy signals is shown. However, its implementation requires choosing a convergence parameter. The necessity in choice of the additional parameter makes the LMS algorithm less practically applicable than the BCC algorithm. The ADF algorithm showed the lowest noise resistivity and can be considered as useful for signal detection, but not for TDE. Some conclusions regarding algorithms’ use for solving the problem of locating leaks are stated. In particular, correlation-based TDE algorithms, as generalized cross-correlation and time-frequency cross-correlation, appear to be the best choice for the implementation of leak detection software.

Providing quick response time and accurate detection of small leaks in multiphase pipelines is of great importance for risk mitigation in the oil and gas industry. The emphasis of implementing state-of-the-art technologies to mitigate both safety and environmental risks in the field becomes of particular importance as aging pipelines transport sour hydrocarbon products crossing populated areas. Fiber Optics Leak Detection Systems (FOLDS) have the capability to detect small pinhole leaks even in multiphase flow, due to its distributed sensing and advanced signal processing features. With this motivation in mind, it is important to evaluate the ability of FOLDS to detect leaks under different scenarios of varying fiber cable location, product phase distribution, propagation media, pressure, temperature, leak sizes and leak locations. Due to the high amount of varying parameters, safety hazards and environmental constraints associated with field testing FOLDS, an industrial third-party multiphase flow facility enables FOLDS performance evaluation across the range of applications of interest. In this particular case, different Distributed Acoustic Sensing (DAS) and Distributed Temperature Sensing (DTS) FOLDS were simultaneously tested for detecting the pipeline leaks. The overall performance test scope and procedure presented in this paper was custom developed to simulate as close as field parameters for validation across multiphase and wet gas flow conditions. The customized performance evaluation test program led to results that show the sensitivity of technology performance to different operational conditions, ranging from physical flow parameters to fiber optic location with respect and leak propagation media. FOLDS solutions performance validation is presented in terms of leak detection, stability and consistency. This leads to a conclusive benchmarking of each solution performance based on the test results for multiphase pipelines. This paper guides the audience through the methodology of customized performance evaluation testing of pinhole leak detection for oil multiphase and wet gas pipelines. It also provides value by highlighting the impact of testing procedures, different flow parameters and installation setups on the actual system performance.

Water distribution networks (WDNs) are vital infrastructure which serve as a means for public utilities to deliver potable water to consumers. Naturally, pipelines degrade over time, causing leakages and pipe bursts. Damaged pipelines allow water to leak through, incurring significant economic losses. Mitigating these losses are important, especially in areas with water scarcity, to allow consumers to have adequate water supply. Globally, as the population increases, there is a need to make water distribution efficient, due to the rising demand. Thus, leak detection is vital for reducing the system loss of the network and improving efficiency.

Natural gas leaks are a serious concern not only because of methane emission but also an explosive threat. We report designing of a portable, miniaturized, laser-based methane tracker for rapid detection of natural gas leaks.

The widespread use of plastic pipes in different fluid conveyance systems has greatly driven the recent development and application of transient-based methods (TBMs) for leak detection in viscoelastic/polymeric pipelines. Current TBMs for viscoelastic pipe leak detection are usually achieved by a two-step procedure, namely viscoelastic parameters identification and leak detection, which requires the pre-knowledge of intact system states (i.e., non-leak) for comparative analysis. This paper presents an efficient single-step frequency domain inverse transient analysis (FDITA) method for simultaneous identifications of viscoelastic parameters and leaks in plastic pipes, so as to enhance the applicability and accuracy of TBMs. Both the single and branched polymeric pipe systems are applied for the method development and application. To this end, analytical solutions of single and branched systems from the transfer matrix method are firstly derived to represent the transient frequency responses of viscoelastic pipelines with leaks. A global optimized nonlinear curve fitting method is then employed to identify both viscoelastic parameters and potential leaks by knowing/measuring other system and flow conditions. Extensive experimental validations and numerical applications of both single and branched pipe systems demonstrate the very good efficiency and accuracy of the developed method for leak detection in different viscoelastic pipe systems. Furthermore, the mechanism of transient wave-leak-viscoelasticity is analysed based on these application results and theoretical evidence. Finally, a sensitivity analysis is performed to quantify and discuss the advantages and potential limitations of the developed method in the paper.

Leak-detection systems (LDS) have become a standard component of pipeline-management systems. There are various reasons which may prompt the pipeline operator to introduce or modernize the LDS. It became necessary to introduce a new system for the MOL company’s pipelines because the previous system was out-of-date, and its operation was accompanied by a multitude of shortcomings. This article examines the real experience and long practical search carried out by MOL’s logistics department, thanks to which effective LDS functioning could be provided for pipelines which had been in long-term operation. Having considered the advantages and disadvantages of current technologies, pipeline characteristics, and operating conditions, the decision was taken to apply PWM (the pressure-wave method) as the basic method for detecting leaks. New LDS efficiency design factors were identified. Test results show that the new system has justified the calculated expectations for efficiency.

This paper describes a numerical analysis supported by small scale experiments for demonstrating a monitoring and leak detection methodology. This study can be used to build a full-scale water pipeline monitoring and response system. The monitoring system is able to monitor the pipeline health and respond to hazard conditions through the use of multiple sensors driven by a hybrid rule-based and statistical monitoring control strategy. The system is programmed to automatically shut off its pump in the event of out-of-bounds/out-of-statistical control conditions detected by its sensors. In addition, this paper presents a numerical simulation analysis approach supported by an experiment that aims at finding a relationship between the location and size of an induced leak and the reported sensor data. The obtained results are used to inform a probabilistic model that can be used to estimate the leak location and size based on flow rate variations. The proposed project will enhance remote pipeline monitoring and structural safety by offering real-time data and automatic emergency response capabilities.

This paper presents the validation of a novel leak detection method for water distribution pipelines, although it could be applied to any buried pressurized fluid flow pipe. The detection method is...

THIS ARTICLE ANALYSES the current situation in the global market for leak-detection systems (LDS). The main operational problems for existing parametric LDSs are identified based on a comparative analysis of statistical data on leaks in European and American trunk pipeline operators, and the proportion of leaks detected by LDSs. Reasons are formulated for these problems, which lead to deterioration in LDS characteristics. The study includes a brief classification and description of the most commonly used leak detection methods. Based on comparison of regulatory documentation in the field of LDSs in Russia and abroad, provisions are identified describing the requirements and important processes within the design and operation of LDSs, which are missing in the domestic regulatory framework and are recommended for inclusion. A comprehensive approach is proposed for the task of improving and introducing parametric LDS, as used by the Pipeline Transport Institute.