Leak Location Research Articles

Enhancing Leak Location in Buried Water Pipes using Array Signal Processing Techniques: the Effect of Wave Velocity Variation

Enhancing Leak Location in Buried Water Pipes using Array Signal Processing Techniques: the Effect of Wave Velocity Variation

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.

Air-Leak-Management Supported by Ventilation-SPECT/CT: A New Perspective on a Long-Known Problem

Abstract Background Prolonged air leakage (PAL) is a common problem after lung surgery. Localization of air leaks (AL) remains challenging in some cases especially in minimally invasive surgery. Ventilation-SPECT/Computertomography (vSPECT/CT) has been reported as potential aid to identify and localize AL. Aims To evaluate vSPECT/CT as potential aid for the localization of AL and explore its use as support for PAL management. Methods Retrospective analysis of a case series of seven patients with PAL after minimally invasive thoracic surgery at our institution in 2023 for which vSPECT/CT was applied. Results Six patients underwent oncologic lung resections, one patient underwent thymectomy for a thymoma. In 5/7 cases an AL was spotted with vSPECT/CT (maximal AL ranged from 40-2000 ml/min). In the remaining two cases (with a maximal AL of 1900ml/min for 1 day, and 10 ml/min after more than 4 weeks and continuous clinical signs of PAL), the AL could neither be localized during revision surgery. Nevertheless, the AL ceased in all patients after revision surgery. In two cases the detected AL supported the continuance of conservative treatment as it confirmed a leak within the prior resection site (maximal AL 40ml/min and 200ml/min). These results are depicted in the attached visual abstract. Conclusion The use of vSPECT/CT for localizing ALs proved to be precise in all instances where an imaging spot was observed (5/5 cases). Additionally, in the two cases where no spot was detected, no AL was found during subsequent revision surgery. These initial results are promising and support further prospective studies of a broader group of cases, reinforcing the role of vSPECT/CT as valuable aid in managing PAL.

Pipe Crawler Robot

This pipe inspection robot aims at detecting the exact location of leakage and clearing the blockages, thus removing human factors from labor-intensive and dangerous work, thereby reducing the number of accidents that happen due to the lack of regular inspection. Detection Pipe leakage, water distribution system. An autonomous pipe crawler robot is designed to navigate through pipelines, offering a practical and efficient solution for inspecting and monitoring the condition of pipe interiors. This robot leverages advanced locomotion mechanisms and control strategies to maneuver through various pipe diameters and configurations, including straight sections, bends, and junctions. Equipped with sensors and cameras, it can detect anomalies such as cracks, corrosion, blockages, and leaks, providing real-time, high-resolution imagery and data. This technology significantly reduces the need for manual inspections, minimizes human exposure to hazardous environments, and improves the precision and efficiency of pipeline maintenance operations. The development of pipe crawler robots represents a significant advancement in the field of robotics and infrastructure management, promising enhanced safety, reliability, and cost-effectiveness for industries reliant on extensive pipeline networks. Key words: inspection, autonomous, locomotion, robot, crawler., etc.

Convolutional neural network for leak location in buried pipes of underground water supply

Convolutional neural network for leak location in buried pipes of underground water supply

Identification research of chemical process leakage based on deep learning and correlation-distance graph coding

As a leak identification method in chemical processes, deep learning is limited by inadequate sample labels and highly coupled, nonlinear process variables, therefore difficult to model with a high identification accuracy. To address these problems, a fusion correlation-distance graph (CDG) coding aided deep learning method is proposed. First, the mechanism model is built based on dynamic process data with different leakage labels. Then, the weighted degree and spatial distribution of the dynamic dataset are computed through innovatively designed correlation and distance coding, aided by sliding window fusion into a correlation-distance matrix. Finally, the well-trained long short-term memory (LSTM) is constructed to learn the deep features of the leakage process and identify them. The applications in two industrial cases, CO2 capture and ammonia synthesis, show that CDG-LSTM presents state-of-the-art identification performance with R2 scores of 0.994 and 0.986. 6.6% and 18.4% improvement over LSTM. In addition, the proposed method effectively categorizes the unknown leak samples into known labels with nearby locations, providing a valuable reference for operators to determine the unknown leak locations.

Detection of Dural Defect Localization Using 4-Dimensional Dynamic Computed Tomography Myelography for Patients with Superficial Siderosis

There are cases of superficial siderosis (SS) with spinal ventral fluid-filled collection in the spinal canal. In our previous study, the balanced steady-state free precession sequence magnetic resonance imaging is useful in identifying the location of dural defects. However, because of its narrow scan area and long scan time, it cannot easily detect the defect location in some patients with small dural defect. In this study, we applied 4-dimensional (4D) dynamic computed tomography (CT) imaging, including time-axis imaging, to myelography using the latest CT imaging equipment, which can perform short-time continuous imaging, to identify the dural defect site. Twenty SS patients with ventral fluid-filled collection in the spinal canal (9 males, 11 females; mean age 61.6years) underwent 4D dynamic CT myelography. A 192-row helical CT (SOMATOM Force, SIEMENS, Munich, Germany) with high-speed scanning capability was used to obtain 9-11 scans per minute at low dose while passing contrast medium into the subarachnoid space. Then, contrast leakage sites were identified. The contrast leakage sites could be identified in all 20 cases: C7/Th1, 2 cases; Th1/2, 5 cases; Th2/3, 9 cases; Th3/4, 1 case; Th5/6, 1 case; Th7/8, 1 case; and Th8/9, 1 case. Eighteen cases underwent surgical operation, and actual dural defects were confirmed at the contrast leakage sites. The mean±standard deviation of leakage time from contrast agent injection was 19.0±9.2s. The 4D dynamic CT myelography can be used to reliably identify the location of spinal fluid leakage. In SS cases, dural defects could be visualized in an average of 19seconds.

A Survey and Study of Signal and Data-Driven Approaches for Pipeline Leak Detection and Localization

A Survey and Study of Signal and Data-Driven Approaches for Pipeline Leak Detection and Localization

Experimental and numerical investigation on soil erosion under different pipeline-leaking locations

In recent years, cave-in ground failures have become increasingly common worldwide. The leakage of defective pipelines buried below ground level has been identified as a significant contributor to these failures. This study utilized experimental seepage-erosion tests to characterize ground collapses resulting from pipeline leakage and examined the impacts of leakage locations on soil and water losses. Subsequently, discrete element method-finite difference method (DEM-FDM) coupling analyses were conducted to elucidate the mesoscopic mechanisms of pipeline leakage, including changes in seepage force, effective stress, and soil pressure on the pipeline during leakage. The findings include: (1) The locations of leaking spots on pipelines significantly influenced soil and water losses; (2) Leaking spots closer to the pipe bottom led to more severe ground collapses due to larger seepage forces; (3) Soil pressure exhibited decreasing trends near the leaking spot and increasing trends at other locations due to the soil arching effect. These insights provide valuable guidance for preventing pipeline leakages.

AN NEGATIVE PRESSURE WAVE METHOD OPTIMIZATION FOR GAS PIPELINE LEAK LOCATION

The article is devoted to the optimization of the negative pressure wave method, designed to determine the location of leaks from a gas pipeline.The proposed negative pressure wave method provides a more accurate determination of the leak location, because a group of readable equally spaced sensors is used. To determine the location of a leak, the sign of the appearance of a minimum of a certain functional is used, which contains, as an integrand, the product of two exponential functions that cha racterize the weakening of a negative pressure wave propagating from the location of the leak to the position of the selected left and right sensors.Calculation formulas are given for calculating the optimal distance between a displaced pair of readable sensors, and the form of the optimal functional relationship between the distances from the place of leakage to the position of the sensors is established.

Prediction of hydrogen leakage location and intensity in hydrogen refueling stations based on deep learning

The common risk of high-pressure hydrogen leakage seriously affects the safety of hydrogen refueling stations (HRS). In this study, we have developed a hybrid model (WD-KNN-CNN) based on wavelet denoising (WD) and deep learning to predict the hydrogen leakage location and intensity in HRS. The raw data were calculated using validated Computational Fluid Dynamics (CFD), and the WD-processed raw data were utilized as input data for the hybrid model. The K-nearest Neighbors (KNN) is employed to identify the leak location, while a two-dimensional Convolutional Neural Network (CNN) is utilized to extract temporal and spatial distribution features from the concentration sequence for intensity prediction. Bayesian optimization is applied to determine the optimal hyperparameter combination for the model. The results demonstrate that the model achieves a prediction accuracy of 99.14% for leak location and 97.42% for intensity level. Furthermore, through the reduction of sensor quantity, we tested the model's performance and found that utilizing 14 sensors as input resulted in 91.3% accuracy for leak location and 90.8% for intensity level prediction. This hybrid model provides a method for safety monitoring in HRS and offers guidance for personnel response during leak incidents.

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.

Safety Through Visibility: Tracing Hydrogen in Colors with Highly Customizable and Flexibly Applicable Supraparticle Additives

AbstractThe flammability of H2‐air mixtures demands timely detection and precise localization of H2 leakages to ensure safety and targeted maintenance measures in the anticipated hydrogen economy. Herein, H2 indicator supraparticles (SPs) are demonstrated that meet this demand by making H2 visible to the naked eye by a rapid (ir)reversible color change. Their toolbox‐like manufacturing from SiO2 nanoparticles (NPs), Pt NPs, and indicator dye molecules via spray‐drying allows for engineering their structure, texture, and detection performance by systematically tuning their composition. It is demonstrated that decreasing the SiO2 NP size, increasing the Pt NP concentration, and adjusting the amount of dye molecules improve the SPs’ response times. This opens up the option for multidimensional customization of their detection performance. Furthermore, the toolbox of H2 indicator SPs containing the established resazurin system is expanded to other indicator dyes, which makes a color change and reversibility of choice feasible. H2 indicator SPs meet many of the target characteristics for H2 detectors, e.g., low limit of detection, cycle stability, and high selectivity. Moreover, the SPs’ particulate nature allows for their flexible application as an additive, e.g., in coatings or clothing, which enables the detection and localization of H2 leakages in relevant application scenarios.

A remote detection method for leak location of external ports in a tokamak: Theory and preliminary experimental validation

The vacuum vessel of fusion devices, equipped with an array of external ports characterized by intricate flange connections and welding joints, inherently carries a notable risk of leakage. With the impending introduction of deuterium-tritium fusion reactions, restrictions on technician access necessitate a more innovative approach. This paper introduces a groundbreaking leak detection method for fusion device ports, leveraging the differential pumping effects of molecular screens. A robotic arm is utilized to transport a mass spectrometer equipped with a molecular screen into the tokamak for detecting leaks at external ports. Through theoretical calculations, simulations, and proof-of-principle experiments, our method demonstrates the feasibility in detecting and localizing leaks, offering a promising solution for leak detection in tritium-operated fusion devices.

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

The mesenteric entry site as a potential weak point in gastrointestinal anastomoses – findings from an ex-vivo biomechanical analysis

PurposeGastrointestinal disorders frequently necessitate surgery involving intestinal resection and anastomosis formation, potentially leading to severe complications like anastomotic leakage (AL) which is associated with increased morbidity, mortality, and adverse oncologic outcomes. While extensive research has explored the biology of anastomotic healing, there is limited understanding of the biomechanical properties of gastrointestinal anastomoses, which was aimed to be unraveled in this study.MethodsAn ex-vivo model was developed for the biomechanical analysis of 32 handsewn porcine end-to-end anastomoses, using interrupted and continuous suture techniques subjected to different flow models. While multiple cameras captured different angles of the anastomosis, comprehensive data recording of pressure, time, and temperature was performed simultaneously. Special focus was laid on monitoring time, location and pressure of anastomotic leakage (LP) and bursting pressures (BP) depending on suture techniques and flow models.ResultsSignificant differences in LP, BP, and time intervals were observed based on the flow model but not on the suture techniques applied. Interestingly, anastomoses at the insertion site of the mesentery exhibited significantly higher rates of leakage and bursting compared to other sections of the anastomosis.ConclusionThe developed ex-vivo model facilitated comparable, reproducible, and user-independent biomechanical analyses. Assessing biomechanical properties of anastomoses offers an advantage in identifying technical weak points to refine surgical techniques, potentially reducing complications like AL. The results indicate that mesenteric insertion serves as a potential weak spot for AL, warranting further investigations and refinements in surgical techniques to optimize outcomes in this critical area of anastomotic procedures.

Automated extraction of tunnel leakage location and area from 3D laser scanning point clouds

Water leakage is one of the common tunnel disease problems. Fast and accurate detection is crucial for timely management of leakage disease. This paper introduces a method for automatically extracting the location and area of tunnel leakage from 3D laser scanning point clouds. The method involves three key steps: segmenting the point cloud data along the tunnel axis based on the width of the tube sheet, correcting the intensity of the point cloud using a linear intensity correction model, and binarizing the point cloud data to extract the leakage area and calculate its area. To evaluate the performance of the proposed methodology, water leakage detection software is prepared based on the method, and tests are carried out in the urban subway tunnels in the leakage disease-producing sections. The results show a detection accuracy of 92 % and an improvement in detection efficiency of 7–8 times. The method proposed can be used to quickly and accurately extract the location and area of water leakage from point cloud data.

Development of an AI-based image/ultrasonic convergence camera system for accurate gas leak detection in petrochemical plants

Outdoor pipeline leaks are difficult to accurately measure using existing concentration measurement systems installed in petrochemical plants owing to external air currents. Besides, leak detection is only possible for a specific gas. The purpose of this study was to develop an image/ultrasonic convergence camera system that incorporates artificial intelligence (AI) to improve pipe leak detection and establish a real-time monitoring system. Our system includes an advanced ultrasonic camera coupled with a deep learning-based object-detection algorithm trained on pipe image data from petrochemical plants. The collected data improved the accuracy of detected gas leak localization through deep learning. Our detection model achieves an mAP50 (Mean average precision calculated at an intersection over union (IoU) threshold of 0.50)score of 0.45 on our data and is able to detect the majority of leak points within a system. The petrochemical plant environment was simulated by visiting petrochemical plants and reviewing drawings, and an outdoor experimental demonstration site was established. Scenarios such as flange connection failure were set under medium-/low-pressure conditions, and the developed product was experimented under gas leak conditions that simulated leakage accidents. These experiments enabled the removal of potentially confounding surrounding noise sources, which led to the false detection of actual gas leaks using the AI piping detection technique.

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.