Leak Location Method Research Articles

Pipeline leak detection and localization based on the flow balance and negative pressure wave methods

The traditional water supply pipeline leak detection and location method has problems of false alarms, low positioning accuracy, and low stability. This paper focuses on the pipeline leak detection and location experimental platform to investigate water supply pipeline leaks, followed by simulation and experimental validation. This study integrates the mass flow balance method and pressure change inflection points to determine pipeline leaks and introduces an improved negative pressure wave method for locating leakage points. The results demonstrate that the system accurately detects pipeline leaks. The average positioning error of the improved negative pressure wave method for the leakage point is 4.76%, which has good stability and high positioning accuracy.

Pipeline leakage localization method based on transient signal detection and data fusion from multi-sensor

ABSTRACT Pipeline leaks pose significant risks to industries, the environment, and individuals, so localizing pipeline leaks is crucial for enhancing pipeline safety during operation. This paper applies a method for localizing pipeline leakages, integrating transient signal detection with multi-sensor data fusion. Addressing the challenges in detecting small leaks amidst strong noise and uncertainty, the method employs the Dempster–Shafer evidence framework for data fusion and an algorithm to analyze transient pressure waves. Comparing it with three spectrum-based methods, the performance of the fusion method is discussed in the single-leakage and multi-leakage cases. In the single-leakage case, even with high levels of noise, the fusion algorithm delivers precise localization estimates. The fusion method excels over the other three methods in the multi-leakage case. The approach significantly enhances the accuracy of leak localization in water pipelines. Extensive simulations demonstrate the method's effectiveness, particularly in noisy environments, offering a promising solution for maintaining pipeline integrity and reducing resource wastage.

Leak localization in District Heating Networks integrating physical model-based and data driven-based methods: Impact of dataset construction on model performance

Leaks in district heating networks result in waste of water and heat, and even threaten personal safety. Various machine learning-based methods have been proposed to realize leak localization and gain satisfying performance based on a large amount of leak samples. However, the creation of such samples demands a substantial investment of time and computational resources, presenting challenges for real-time applications. Previous studies have rarely addressed the influence of leak dataset construction and efficient creation methods. To fill this limited area, this paper conducts a comparative analysis on the variables in leak dataset and proposes an effective method for sample dataset generation, complemented by a novel accuracy evaluation that incorporates the topological relationship among pipelines. A multi-source looped heating network is employed to do the case study. The results demonstrate that the proposed method achieves satisfactory leak localization performance with a significantly reduced training set. Furthermore, by segmenting the physical pipeline into multiple virtual pipelines and using the adjacent evaluation metric, the leak localization method achieves a 75 m error with an accuracy of 0.998.

A weak leak location method based on signal attenuation mechanism for energy transportation system

Traditional leak location methods based on signal saltation points make it hard to locate the position of the weak leak event in the energy transportation system. To address it, a data-driven method based on signal attenuation mechanism is proposed in this paper. First, a wavelet adaptive threshold denoising procedure is proposed to obtain the pure signal from the collected data, and the decimal-coded genetic mechanism is designed to calculate the optimal threshold of wavelet decomposition for different layers. Second, the two-stage leak location method is proposed to calculate the position of the leak point through the combination analysis of pressure data change of both ends of the pipeline. Through the presented pressure signal attenuation mechanism, the leak location result is obtained from the data changes of both ends of the pipeline, not the time difference of arrived signal. With the above signal denoising and analysis way, the proposed method has the 1% location deviation of the leak point, which is superior to leak location methods based on signal saltation points such as wavelet transform. Finally, different experiment results are studied, and the analysis results show the effectiveness of the proposed method for the leak location problem.

Leak detection and localization of fluid-filled pipeline using accelerometer pairs and mode separation method

Acoustic methods are commonly used to detect and locate leaks in pipelines, but they often overlook important multimodal characteristics of acoustic waves, limiting their applicability. Based on propagation theories regarding F(1,1) mode signal extraction, a new and easy-to-use method for leak detection and localization in pressurized pipelines is proposed. The F(1,1) mode signals are obtained using accelerometer pairs and mode separation method, which are then used to train detection models and to estimate the time difference of arrival (TDOA) for leak localization. Mode separation empowers the data-driven models with physically meaningful samples, aiming to broaden their applicability. The mode consistency between TDOA and velocity is achieved to ensure accurate leak localization. Experiments in buried water-filled ductile iron pipe (experiment A) and in fuel-filled iron pipe (experiment B) are conducted and the longest test distance reaches 72 m. The results validate that the employment of mode separation notably improves current leak detection and localization methods which primarily rely on original signals. The recognition rates rise about 3 %∼11 %, and the false alarm rates reduce about 1 %∼2 %, even when confronted with pipe junction interferences. Also, the leak localization errors are overall lower, with absolute errors below 2 m and relative errors less than 5 % of the test segment length for most cases.

Research on pressure propagation in long-distance water pipelines for fault conditions by VMD and wavelet transform

Long-distance water pipelines in complex geographical environments face potential risks such as leaks. This study uses variational mode decomposition (VMD) and continuous wavelet transform (WT) to analyze pressure fluctuation signals in pipelines for detecting and locating leaks and other anomalies. CFD simulations are conducted to obtain pressure fluctuation data under various fault conditions. The pressure signals are then decomposed into multiple Intrinsic Mode Functions (IMFs) using VMD, and WT is applied to analyze their time–frequency characteristics. The results show significant differences in amplitude and frequency of IMF components between fault and normal conditions, with reduced amplitude and altered frequency under fault conditions, providing a basis for fault detection. Additionally, the IMF characteristics differ at different fault locations, aiding in fault localization. This research offers an effective method for leak detection and localization in long-distance pipelines, crucial for enhancing safety and stability.

Pipeline leak location method based on SSA-VMD with generalized quadratic cross-correlation*

Abstract Natural gas pipelines are an essential part of the economy. Natural gas pipelines may leak after aging, strong vibration signals may be generated in the pipeline when leakage occurs, and vibration signals may be noisy. Traditional variational mode decomposition (VMD) noise reduction methods need to set parameters in advance, and so may not achieve the best decomposition effect. To solve this problem, this paper proposes a method for pipeline leakage location based on the sparrow search algorithm (SSA) optimization of VMD combined with generalized quadratic cross-correlation. The method first calculates the original signal-to-noise ratio, and if the signal-to-noise ratio is low, wavelet threshold denoising is used to process the signal. Then, SSA optimization is used to refine the two key parameters of VMD (penalty parameter α and mode decomposition number K) based on sample entropy. Subsequently, the signal undergoes decomposition into K intrinsic mode function (IMF) components through VMD according to the obtained analysis parameter combination. Then, the IMF components are screened to obtain the reconstructed signal. Finally, the noise reduction signal is obtained. The signal delay after noise reduction is obtained through a generalized quadratic cross-correlation and the accurate leakage position is obtained using the delay. Experiments showed that the minimum relative error of this method could reach 0.6%, which was more accurate than the traditional VMD method, and effectively improved the accuracy of noisy signals in pipeline leakage locations.

Moving horizon estimation for pipeline leak detection, localization, and constrained size estimation

Advanced pipeline leak detection and localization techniques are needed to reduce greenhouse gas emissions from hydrocarbon transportation pipelines. Developing effective leak detection and localization methods is challenging due to the spatiotemporal dynamics of process variables, the presence of process/measurement disturbances and constraints, and the limited measurement data. To address this issue, this manuscript proposes a novel moving horizon estimation design for pipeline leak detection, constrained estimation of leak size and location by using an infinite-dimensional pipeline hydraulic model. Based on the mass and momentum balance laws and the Cayley–Tustin time-discretization method, an infinite-dimensional discrete-time pipeline hydraulic model is proposed considering (unknown but bounded) disturbance and leak. By introducing a coordinate transformation, we decouple the leak size and location estimation problems. The implementable discrete-time moving horizon estimator and observer are designed for constrained leak size and location estimation. The effectiveness of the proposed designs is validated via simulation examples.

A stepwise fast leakage localization method applying the strategy of dynamic area narrowing down for large-scale water distribution network

ABSTRACT Urban water distribution networks (WDNs) are facing serious leakage problems. As cities expand, the leakage localization burden on large WDNs gradually increases. Although methods have been widely researched, there is a lack of studies and successful applications for large-scale WDNs. To deal with this, a stepwise fast leakage localization Method, SFLLM, utilizing the `dynamic area narrowing down (DAND)' strategy and coupled leakage features (CLF) is proposed. The SFLLM includes a fast-and-dynamic stage using the DAND strategy to reduce the potential leakage area and an accurate localization stage. Only partial representative candidate locations are required to simulate leakages by DAND, and meantime CLF is used to analyze the leakage similarities so that the localization accuracy and efficiency can be improved. SFLLM was tested on a benchmark WDN, saving more than 88% of simulation by DAND strategy and achieving localization in 11 seconds. The results also proved the enhanced performance of CLF in ensuring the stability of the accuracy against various types of uncertainties that may occur in real WDNs. Moreover, three real burst leaks in an actual large-scale WDN were localized within 205 m in about 22 minutes by SFLLM, showing the method's reliable applicability in guiding field leak exploration.

Research on pipeline leakage localization method based on CZIM algorithm

The time difference of arrival is a common method to find the leakage point of water pipeline. The leakage point localization is achieved by calculating the time delay between the signals reaching different sensors. Mainstream time delay estimation algorithms based on signal correlation analysis are susceptible to the introduction of noise signals, low sampling rates, and signal clipping, resulting in inaccurate localization results. The article analyzed the impact of different interference factors and proposed a new time delay estimation algorithm based on signal cross-zero information modulation (CZIM) to address these problems. By normalizing the amplitude of the two signals at the detection points on both sides of the pipeline leakage position, two sets of sparse signal sequences with only two eigenvalues of 0 and 1 are obtained. The error coefficient function is calculated by a similar traversal method to finally index the time delay. In this paper, the principle and characteristics of the algorithm are analyzed and compared with the most commonly used GCC method. In both numerical simulations and actual pipe leakage localization experiments, the CZIM algorithm has shown its wide applicability, low impact by low sampling rate, and adaptability to low signal-to-noise ratios, etc. At the same time, the algorithm is simple in design and has a small amount of calculation and can meet the demand for real-time data processing, providing a new idea for the development of acoustic localization technology.

Locating leaks in geomembrane-lined ponds using the electrical leak location method: Case histories

Results from seven years of electrical leak location methods applied to geomembrane-lined ponds to locate leaks are presented. Inspections were conducted on 195 projects designed with single (n = 74), double (n = 120), or triple (n = 1) liners. These projects are located in the U.S.A. (n = 159), Canada (n = 30), and Mexico (n = 11). All inspections were conducted on full ponds, which resulted in the detection and repair of 1230 leaks during the study period across an analyzed area of 322 ha. From 2015 to 2021, the average was 14 leaks/ha, with values ranging from 0 to 689 leaks/ha. The results reveal that larger inspected areas (greater than 2 ha) tend to have fewer leaks. 63% of projects had up to 5 leaks/ha. 15% of projects had more than 20 leaks/ha. The results of this study could influence landfill designers, operators, and environmental agencies in defining new practices for designing and operating geomembrane-lined ponds.

Graph Laplace Regularization-based pressure sensor placement strategy for leak localization in the water distribution networks under joint hydraulic and topological feature spaces

Urban water distribution networks (WDNs) have wide range and intricate topology, which include leakage, pipe burst and other abnormal states during production and operation. With the continuous development of the Internet of Things (IoT) technology in recent years, the means of monitoring the WDNs by using wireless sensor network technology has gradually received attention and extensive research. Most of the existing researches select the deployment location of sensors according to the hydraulic state of the WDNs, but the connectivity and topology between the nodes of the WDNs are not fully considered and analyzed. In this study, a new method that can integrate the topological features and hydraulic model information of the WDN is proposed to solve the problem of optimal sensor placement. First, the method preprocesses the covariance matrix of the pressure sensitivity matrix of the water distribution network by a diffusion kernel-based data prefiltering method and obtains the new network topology weights and its Laplacian matrix under the constraints of the network topology through a data-based graphical Laplacian learning method. Then, the sensor placement problem is transformed into a matrix minimum eigenvalue constraint problem by the Graph Laplace Regularization (GLR)-based method, and finally the selection of sensor nodes is accomplished by the method based on Gershgorin Disc Alignment (GDA). The proposed strategy is tested on a passive Hanoi network, an active Net 3 network, and a larger network, PA2, and is compared with some existing methods. The results show that the proposed solution achieves good performance in three different leak localization methods.

A Pipeline Leak Localization Method Based on the EEMD-HT Algorithm and the Leakage-Grading Resolution Strategy

A Pipeline Leak Localization Method Based on the EEMD-HT Algorithm and the Leakage-Grading Resolution Strategy

Multipoint leak localization for pipelines via acoustic wave amplitude ratio

Pipeline leak localization is significant for maintaining the regular operation of pipelines. This paper proposes a multipoint leak localization method for pipelines via the acoustic wave amplitude ratio, which addresses the issue of the traditional time delay estimation method (TDE) not being able to locate multiple leaks simultaneously. Firstly, a multipoint leak localization method for pipelines is established without time delay estimation and acoustic velocity. This method merely needs to determine the amplitude of the leakage acoustic wave propagating to both ends of the pipeline to achieve leakage localization. Secondly, the improved empirical wavelet transform (IEWT) method denoises the leakage acoustic wave, which can obtain an accurate acoustic amplitude to reduce the localization error. Finally, the trouble that the mutation factor and crossover probability cannot be adaptively selected in the differential evolution (DE) algorithm is improved, and the improved differential evolution (IDE) algorithm is used to solve the objective function to obtain the position of each leakage point. Field experiments show that the multipoint leak localization method proposed in this paper has perfect accuracy, and the minimum localization error is 11 m.

Constructing a zero leak landfill cell

Engineered landfill barriers are the last line of defence in protecting precious groundwater resources from contamination. A landfill cell without any leaks at the conclusion of construction is the exception rather than the rule. From design to materials testing to installation practices and contractor care, all of these elements contribute to both the short and long-term integrity of the lining system. Electrical leak location (ELL) methods can be used to detect leaks in installed landfill cells after construction so that leaks can be repaired before a landfill cell begins filling operations. However, the thorough testing of landfill cells using ELL can be complicated by their complex configuration. The dipole method, employed after cover material placement, requires electrical isolation of the cover material, which is difficult to achieve with a landfill expansion cell configuration. Additionally, employing only this type of testing can leave the tie-in area untested, which is a critical area prone to damage. This paper provides solutions for complete landfill expansion cell testing, including a relatively new testing methodology (ASTM D8265, [1]), which provides protocol for verifying that a geomembrane has been successfully installed with no leaks. A case study is included to show how ASTM D8265 can be used to provide documentation of a zero-leak condition of a landfill cell after construction. Common elements of design, geomembrane installation, and quality assurance are discussed in order to provide a road map of how to reach a zero-leak condition at the end of a landfill expansion cell construction project. This paper can be used as a guide to constructing a zero-leak landfill cell using state-of-the art construction practices and ELL testing methodologies.

The state of Electrical Leak Location: New technologies and improved methodologies

The introduction of installed geomembranes revolutionized containment system effectiveness for the protection of groundwater quality and water resources. After over six decades of installing geomembranes and evaluating their performance, it has become clear that simply specifying the use of a geomembrane is not enough. Even the addition of Construction Quality Assurance (CQA) is often not enough. In order to ensure that an installed geomembrane will perform in accordance with its design intent, it must be tested for leaks before operation and sometimes even during operation. Several decades after the introduction of geomembranes, Electrical Leak Location (ELL) methods were developed in order to locate leaks in them once installed. The methods have undergone many stages of evolution since their inception and continue to be improved today. This paper details where the technologies began and the current state of the practice as well as the currently available state of the art. Newly developed technologies from across the globe are presented in order to showcase cutting edge capabilities. The spectrum of methodologies used by various practitioners is also presented and detailed. This paper will assist designers and facility owners to choose the best ELL solution in order to ensure containment facility performance, which is critical to the sustainability of the modern world.

Novel EMD with Optimal Mode Selector, MFCC, and 2DCNN for Leak Detection and Localization in Water Pipeline

Significant water loss caused by pipeline leaks emphasizes the importance of effective pipeline leak detection and localization techniques to minimize water wastage. All of the state-of-the-art approaches use deep learning (DL) for leak detection and cross-correlation for leak localization. The existing methods’ complexity is very high, as they detect and localize the leak using two different architectures. This paper aims to present an independent architecture with a single sensor for detecting and localizing leaks with enhanced performance. The proposed approach combines a novel EMD with an optimal mode selector, an MFCC, and a two-dimensional convolutional neural network (2DCNN). The suggested technique uses acousto-optic sensor data from a real-time water pipeline setup in UTAR, Malaysia. The collected data are noisy, redundant, and a one-dimensional time series. So, the data must be denoised and prepared before being fed to the 2DCNN for detection and localization. The proposed novel EMD with an optimal mode selector denoises the one-dimensional time series data and identifies the desired IMF. The desired IMF is passed to the MFCC and then to 2DCNN to detect and localize the leak. The assessment criteria employed in this study are prediction accuracy, precision, recall, F-score, and R-squared. The existing MFCC helps validate the proposed method’s leak detection-only credibility. This paper also implements EMD variants to show the novel EMD’s importance with the optimal mode selector algorithm. The reliability of the proposed novel EMD with an optimal mode selector, an MFCC, and a 2DCNN is cross-verified with cross-correlation. The findings demonstrate that the novel EMD with an optimal mode selector, an MFCC, and a 2DCNN surpasses the alternative leak detection-only methods and leak detection and localization methods. The proposed leak detection method gives 99.99% accuracy across all the metrics. The proposed leak detection and localization method’s prediction accuracy is 99.54%, precision is 98.92%, recall is 98.86%, F-score is 98.89%, and R-square is 99.09%.

Real-Time Leak Location of Long-Distance Pipeline Using Adaptive Dynamic Programming.

In traditional leak location methods, the position of the leak point is located through the time difference of pressure change points of both ends of the pipeline. The inaccurate estimation of pressure change points leads to the wrong leak location result. To address it, adaptive dynamic programming is proposed to solve the pipeline leak location problem in this article. First, a pipeline model is proposed to describe the pressure change along pipeline, which is utilized to reflect the iterative situation of the logarithmic form of pressure change. Then, under the Bellman optimality principle, a value iteration (VI) scheme is proposed to provide the optimal sequence of the nominal parameter and obtain the pipeline leak point. Furthermore, neural networks are built as the VI scheme structure to ensure the iterative performance of the proposed method. By transforming into the dynamic optimization problem, the proposed method adopts the estimation of the logarithmic form of pressure changes of both ends of the pipeline to locate the leak point, which avoids the wrong results caused by unclear pressure change points. Thus, it could be applied for real-time leak location of long-distance pipeline. Finally, the experiment cases are given to illustrate the effectiveness of the proposed method.

Geomembrane leaks detection and leakage correlation factor analysis of composite liner systems for fifty-five (55) solid waste landfills in China

In composite liner system (CLS), there are little field data on the number, size, and shape of geomembrane (GMB) holes, which are the key parameters to identify and evaluate the volume of leachate leakage from landfills. In this paper, in-situ detection of GMB holes in composite liner systems of 55 solid waste landfills in China was conducted by electrical leak location (ELL) method, and the following conclusions were drawn. The holes frequency, equivalent radius, and theoretical leakage volume of double-layer systems are lower than those of single-layer systems under the same geotextile (GT) density by 28.2%, 51.4%, and more than 60%, respectively. As the thickness of GMB increases, the holes frequency, equivalent radius and theoretical leakage volume reduce observably. Regarding protecting GMB, the density of geotextiles should not be less than 600 g/m2. Through the analysis of various correlation factors, the thickness of geomembrane is an important parameter affecting the amount of leakage. This study will provide a reference for analyzing the data of field investigation, which would provide constructive suggestions for the composite liner system.

Adaptive leak signal extraction based on EMD-CC for water pipeline leak location

When using acoustic methods for water pipeline leak localization, noise will significantly affect the accuracy of localization, especially for the case that the noise intensity far exceeds the signal intensity, the traditional noise reduction methods will be ineffective. Therefore, an empirical mode decomposition and cross-correlation (EMD-CC) method for leak localization is proposed, which can adaptively extract effective leak signals from low signal-to-noise ratio (SNR) detection signals, and then can significantly improve the accuracy of localization. In this paper, the principle and steps of the algorithm are elaborated, and the effectiveness is verified in simulations and experiments. In the simulations, the SNR of the reconstructed signal −15 dB is dramatically increased to −6 dB. Compared with conventional noise reduction methods, the time-delay value estimation of EMD-CC is more accurate, more reliable, and more noise resistant. In full-size experiments, the accuracy of localization after processing is significantly improved.