Leak Detection Research Articles

A pilot study of vision-based real-time detection of invisible gas leak and fire

A pilot study of vision-based real-time detection of invisible gas leak and fire

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.

Gasochromic WO3/MoO3 sensors prepared by co-sputtering for hydrogen leak detection with long-lasting visible signal

Gasochromic WO3/MoO3 sensors prepared by co-sputtering for hydrogen leak detection with long-lasting visible signal

Underground Hydrogen Storage: Comparison of High-pressure Hydrogen, Liquid Hydrogen, and Ammonia

One of the alternatives for effective storage of irregularly produced renewable energy is hydrogen energy. In order to realize a hydrogen-based society, not only environmentally friendly production of hydrogen but also effective storage is very important. Underground hydrogen storage technology is a technology that has evolved from the technology for storing natural gas underground, and includes waste gas fields, salt domes, aquifers, and rock cavities. When stored underground, hydrogen is converted into high-pressure gaseous hydrogen, liquefied hydrogen, and ammonia. Liquefied hydrogen requires extremely low storage temperatures, and ammonia is a toxic substance that requires separate handling, and energy loss occurs during the conversion process. To compensate for this, research on liquefied hydrogen, such as multilayer insulation technology, is being conducted. Ammonia has successfully extracted high-purity hydrogen by developing a membrane reactor. Ammonia toxicity can be prevented by strengthening leak detection and blocking facilities. Among these, ammonia was found to be the most suitable for underground storage in terms of economic feasibility, environment, and commercialization.

IoT Leak Detection System for Onshore Oil Pipeline Based on Thermography.

The vast expanses of remote onshore areas in oil-producing countries are home to a network of flow and collection pipelines that are susceptible to leaks. Most of these areas lack the infrastructure to enable the use of remote monitoring systems equipped with sensors and real-time data analysis to provide early detection of anomalies. This paper proposes a proof of concept for a monitoring system based on the Internet of Things (IoT) for real-time detection of pipeline leaks in onshore oil production fields. The proposed system, based on a thermal imaging leak detection method, informs the operator of the system's operating status via a web page. The leak detection system communicates via a Zigbee network between the IoT devices and a 4G mobile network. The results of the tests carried out show that a visual and automatic IoT-based leak detection system is possible and plausible. The proposed leak detection system enables supervisors at remote stations and field workers to monitor the operating status of pipelines via computers, tablets, or smartphones, regardless of where they are.

Quantitative Evaluation of Deformation in High-Speed Magnetic Flux Leakage Signals for Weld Defects in Oil and Gas Pipelines

Complex multiphase flow in oil and gas pipelines raises safety risks. Magnetic flux leakage (MFL) detection effectively identifies pipeline defects. However, the high-speed movement of MFL inspection tools induces motion-induced eddy currents (MIECs), complicating defect recognition and quantification. Most prior research has primarily focused on rectangular defects, leaving a gap in understanding the impact of MIECs on weld defects. This paper proposes the amplitude and shape deformation coefficients to analyze the influence of velocity on various weld defects, including internal reinforcement, lack of penetration, crack, external corrosion, internal corrosion, porosity, and lack of fusion. Utilizing these coefficients, this study examines the influence of the defect size and magnetizer configuration on these velocity-induced effects. The results show that the shape deformation coefficients range from 2.75 to 3.57 for Bx and from −0.13 to −0.3 for By, indicating a significant change in the MFL signal shape at 10 m/s compared to 0 m/s. The amplitude deformation coefficients for lack of penetration, internal corrosion, and porosity range from −0.01 to 0.1 for Bx, and from 0.86 to 0.98 for By, suggesting a decrease in peak-to-peak values. In contrast, other defects exhibit an increase in peak-to-peak values, indicating that the velocity effect may enhance the MFL signal. Also, the defect size and magnetizer configuration can affect the velocity effect on signals. These findings provide essential guidance for quantifying defect sizes and a solid foundation for designing more effective magnetization devices.

Segmentation of tunnel water leakage based on a lightweight DeepLabV3+ model

Abstract The accurate and efficient detection of water leakage with complex backgrounds is crucial for the safety of metro operations. A lightweight segmentation method for metro tunnel water leakage based on transfer learning is proposed. Firstly, this is based on the Deeplabv3+ model and adopts MobileNetv3-Large as the backbone feature extraction network, which significantly reduces the network parameters and improves the detection speed; secondly, it incorporates the efficient channel attention mechanism, which enables the model to adaptively adjust the weights of the channel features and capture the inter-channel relationships in the image, which significantly improves the model’s ability for feature extraction ability; furthermore, for the problem of severe imbalance between positive and negative samples in the dataset, the recognition accuracy of complex samples is increased by optimizing the loss function; finally, the training method of transfer learning is utilized to solve the problem of scarcity of water leakage dataset, and to improve the model’s accuracy and generalization ability. The results show that the model has more significant detection accuracy and segmentation speed advantages than today’s mainstream semantic segmentation model. With strong generalization ability in complex environments (e.g. low illumination and multiple obstructions), model can be used for intelligent operation and maintenance in metro tunnel projects.

In-site experimental study on the effects of infrared thermal imaging technology on levee leakage detection

Excessive seepage significantly affects the stability of levees during flood seasons. Recently, infrared thermal imaging technology has been proposed to detect the seepage leakage of levees for its simplicity, high efficiency and non-contact. However, previous studies mainly focused on theoretical and laboratory tests to verify its effect, which involve many assumptions and simplification. This paper aims to study the practical application effects of infrared thermal imaging technology on levee leakage detection. A typical embankment in Xiangyin County, Hunan Province is selected as the studied area, where a specific point containing water is considered as the potential leakage point. Then, DJI M300RTK UAV equipped with Zenith H20T gimbal is adopted to collect the visible and infrared images of the embankment’s leaking point. The visible and infrared image data collected at different times and at different measurement heights are analyzed to investigate the detection accuracy of infrared thermal imaging technology. Results show that the infrared thermal imaging technology can accurately identify the potential seepage leakage point of levees. Compared with the visible light recognition method, the infrared thermal imaging technology can identify the location and scope of leakage hazards more clearly and accurately at night. During the daytime, infrared detection is less effective than visible light method. Moreover, the measurement altitude between the drone and the target leakage points significantly affects the detection accuracy of infrared thermal imaging technology.

Improved Early Detection of Tube Leaks Faults in Pulverised Coal-fired Boiler Using Deep Feed Forward Neural Network

Boiler tube leaks significantly reduce the operational availability of power units, yet their early detection and prediction have not been fully realised in the industry. This paper introduces a novel approach employing deep feedforward neural networks for early detection of boiler tube leaks in pulverised coal-fired boilers. Early detection enhances repair planning, minimising downtime and production losses. It also improves monitoring and control of boiler tube failures, thereby optimising power plant operations and revenue. Diverse deep neural network models were developed and rigorously tested by leveraging 9 years of operational data (2012–2020). Exhaustive hyper-parameter optimisation, involving seven parameters, substantially improved predictive accuracy. By achieving training and testing accuracies of 82.8% to 99.3%, the study assessed their ability to detect boiler tube leaks over the same 9-year span, providing insights into leak detection capabilities. The models notably predicted all 12 identified tube leak events, averaging a 14-day lead time before boiler shutdown. In addition to leak prediction, a leak detection matrix was devised to analyse residual behaviour and reduce the likelihood of false alarms. However, the models’ predictive performance was observed to be limited to the following year, with satisfactory results for 2021 only. Incorporating the 2021 data into retraining significantly improved the predictions for 2022. The study concludes that while the models demonstrate robust short-term prediction capabilities, they require continuous retraining to maintain accuracy and relevance. This ongoing refinement is essential for keeping the models up-to-date and reliable in predicting future boiler tube leaks.

Research on gas pipeline leakage identification based on SE-2DCNN with ultra-weak fiber Bragg grating distributed acoustic sensing system

Abstract A new method for identifying gas pipeline leaks using an ultra-weak fiber Bragg grating (UWFBG) distributed acoustic sensing (DAS) system is proposed. To enhance the accuracy of gas leakage detection, an improved convolutional neural network (CNN) incorporating a two-dimensional structure based on the squeeze-and-excitation (SE) attention mechanism was introduced. The principle of acoustic leakage sensing using this technology is explained in detail, and an experimental setup simulating gas pipeline leaks is constructed. During this process, 9,340 DAS data points across varying leakage volumes and pipeline pressures were collected to create ten distinct datasets. The Mel-frequency cepstral coefficients (MFCC) are employed as the feature input for the optimized SE-2DCNN model, which performs identification tasks. The results show that this optimized model achieves an average leakage identification accuracy of 95.33%, demonstrating superior performance over other methods. This approach offers a robust reference for accurately detecting gas pipeline leakages.

Miniaturized Electrochemical Gas Sensor with a Functional Nanocomposite and Thin Ionic Liquid Interface for Highly Sensitive and Rapid Detection of Hydrogen.

Hydrogen has been widely used in industrial and commercial applications as a carbon-free, efficient energy source. Due to the high flammability and explosion risk of hydrogen-air mixtures, it is vital to develop sensors featuring fast-responding and high sensitivity for hydrogen leakage detection. This paper presents a miniaturized electrochemical gas sensor by elaborately establishing a nanocomposite and thin ionic liquid interface for highly sensitive and rapid electrochemical detection of hydrogen, in which a remarkable response time and recovery time of approximately 6 s was achieved at room temperature. A screen-printed carbon electrode was modified with a reduced graphene oxide-carbon nanotube (rGO-CNT) hybrid and platinum-palladium (Pt-Pd) bimetallic nanoparticles to realize high sensitivity. To achieve miniaturization and high stability of the sensor, a thin-film room-temperature ionic liquid (RTIL) was employed as the electrolyte with a significantly decreased response time. The fast-responding hydrogen sensor demonstrates excellent performance with high sensitivity, linearity, and repeatability at concentrations below the lower explosive limit of 4 vol %. The engineered high-performance interface and gas sensor provide a promising and effective strategy for gas sensor design and rapid hazardous gas monitoring.

Leak detection and leak localization in a smart water management system using computational fluid dynamics (CFD) and deep learning (DL)

Leak detection and leak localization in a smart water management system using computational fluid dynamics (CFD) and deep learning (DL)

A Novel Optimized Vibration-based Energy Harvester for Leak Detection in Wall-mounted Water Pipelines

A Novel Optimized Vibration-based Energy Harvester for Leak Detection in Wall-mounted Water Pipelines

Real-Time Perfusion and Leak Assessment in Bariatric Surgery: Bridging Traditional and Advanced Techniques.

This comprehensive literature review explores the efficacy of real-time perfusion and leak assessment methods in bariatric surgery, comparing traditional techniques with advanced imaging modalities. As the global incidence of obesity and related comorbidities rises, the demand for bariatric surgeries such as Roux-en-Y gastric bypass and sleeve gastrectomy has increased, along with the risk of serious complications like anastomotic and staple line leaks. Traditional intraoperative leak testing methods, including the air leak and methylene blue dye tests, are commonly employed but exhibit inconsistent sensitivity in leak detection. Intraoperative endoscopy, although underutilized, offers enhanced visualization and has been associated with reduced leak and complication rates in certain cases. Emerging technologies such as indocyanine green (ICG) fluorescence, laser speckle contrast imaging (LSCI), and hyperspectral imaging (HSI) provide real-time assessment of tissue perfusion, potentially improving surgical outcomes. ICG fluorescence enables visualization of blood flow to detect ischemia, while LSCI offers immediate, dye-free perfusion mapping, and HSI assesses tissue oxygenation without the need for contrast agents. Despite their promise, these technologies are limited by high costs, technical complexity, and varying accessibility, with current evidence insufficient to confirm their superiority over traditional methods. Future research should focus on large-scale, multicenter trials to validate these advanced techniques and refine their application in clinical practice. Integrating traditional and emerging methods may optimize intraoperative decision-making, reduce complications rates, and enhance patient outcomes in bariatric surgery.

An early warning method of pipeline leakage monitoring with limited leakage samples

Aiming at the problems of low computational efficiency of the real-time transient model (RTTM), and training difficulty of data-driven models with small leakage samples in existing pipeline leakage monitoring methods, this paper proposes a novel pipeline leakage detection model (Multiple Regression Modeling of Real-time Transients, MR-RTTM) based on the fusion of real-time transient mechanism model and data-driven inference. Firstly, the real-time transient mechanism model of pipelines is derived based on the Bernoulli equation, and the required eigenvalues and label values of the data-driven model are determined to achieve the integration of the mechanism and the data-driven model. Secondly, MR-RTTM is constructed by applying the data of pipelines in normal working conditions and combining with the multiple regression algorithm to fit the real-time transient mechanism model of pipelines. An optimal threshold interval selection method of the model is also proposed based on the probability of detection (POD) and the probability of false call (POFC). Experiments show that MR-RTTM can detect pipeline leakage by arranging common pressure and flow rate sensors in the pipeline inlets and outlets, and the minimum detectable leakage is 1 % with an acceptable interval of 80 %∼100 % for POD and 0 ∼ 10 % for POFC, and the leakage discrimination rate is up to 100 %.

Water Leak Detection: A Comprehensive Review of Methods, Challenges, and Future Directions

This paper provides a comprehensive review of the methods and techniques developed for detecting leaks in water distribution systems, with a focus on highlighting their strengths, weaknesses, and areas for future research. Given the substantial economic, social, and environmental impacts of undetected leaks, timely detection and precise location of leaks are critical concerns for water authorities. This review categorizes existing methods into traditional approaches, such as manual sounding, and modern techniques involving smart water management and sensor technologies. A multidimensional bibliometric analysis was employed to systematically identify, select, and evaluate 600 scholarly articles on water leak detection, sourced from the Scopus database over a 23-year period (2000–2023). The paper evaluates each method based on leak sensitivity, burst detection, continuous monitoring, alarm accuracy, and implementation costs. Novel insights include an analysis of emerging smart water technologies and their integration into real-world water distribution networks, offering improved efficiency in leak detection. The paper also identifies key gaps in current research and suggests future directions for advancing the accuracy and cost-effectiveness of these technologies.

Acoustic emission-based leakage detection for gas safety valves: Leveraging a multi-domain encoding learning algorithm

Safety valves in gas distribution systems are crucial for preventing excessive pressure buildups. Leakage in these valves compromises system stability, causing equipment damage and environmental pollution. Acoustic emission signal analysis from the valve body is essential for detecting leakage, but these signals are often weak and easily disrupted by noise, complicating detection. This paper proposes a multi-domain encoding learning algorithm to address these challenges. Unlike conventional single-domain methods, it combines two encoding images, the Gramian angular difference field and the Markov transition field, to enhance the comprehensive expression of leakage features. A lightweight convolutional neural network reduces computing resource dependency, and a convolutional block attention mechanism improves feature identification. Experimental results demonstrate that our method detects gas leakage with an accuracy of at least 96.77%. It maintains superior performance even under intense noise interference, offering a promising solution for detecting weak gas leakages in safety valves amidst high background noise.

Artificial intelligence based leak detection in blended hydrogen and natural gas pipelines

In the transition towards a hydrogen-based energy system, the strategic use of pipelines is crucial for efficient hydrogen distribution. Leveraging existing natural gas pipelines to carry a mix of hydrogen and natural gas offers a cost-effective alternative to building new infrastructure. This study explores the development of a leak detection system compatible with existing pipelines, specifically tailored for the blended hydrogen and natural gas mix. Given the scarcity of leak data for blended hydrogen-natural gas pipelines, the study introduces a Real-Time Transient Model (RTTM) for blended gases, simulating leak dynamics to generate necessary data. Additionally, a leak detection system (LDS) is developed using a fusion of Convolutional Neural Network (CNN) and Explainable Artificial Intelligence (XAI) through Adaptive Neuro-Fuzzy Inference Systems (ANFIS). This LDS framework overcomes the “black box” issue common in AI-driven systems, enabling reliable detection. The integration of Explainable and traditional AI techniques holds promising implications for blended hydrogen pipelines by enhancing the safety and efficiency of hydrogen transportation, thereby mitigating economic and environmental impacts, and addressing public concerns.

Border Gateway Protocol Route Leak Detection Technique Based on Graph Features and Machine Learning

In the Internet, ASs are interconnected using BGP. However, due to a lack of security considerations in the design of BGP, a series of security issues arise during the propagation of routing information, such as prefix hijacking, route leakage, and AS path tampering. Therefore, this paper conducts research on the detection of route leakage. By analyzing BGP routing information, we abstract the routing propagation relationship between ASs into a network topology graph, and extract graph features from the graph abstracted from routing data at certain time intervals. Based on the structural robustness features and centrality measurement features of the graph, we determine whether a route leakage has occurred during the current time period. To this end, we use machine learning methods and propose a weighted voting model. This model trains multiple single models and assigns weights to them, and through the weighted analysis of the results of multiple models, it can determine whether a route leakage has occurred. In addition, to determine the corresponding weights, we use genetic algorithms for identifying route leaks. The experimental results show that the method used in this paper has a high accuracy rate, and compared with a single model, it performs better on multiple datasets.

MXene anchored fabric for room-temperature operated ammonia sensing

Low-cost and fast-response fabric sensors are highly desirable for shop floor worker daily monitoring and early trace ammonia leak detection. However, the difficulty is how to establish a high bonding capacity between the active material and the fabric to meet the needs of sensing performance on practical applications. Herein, MXene anchored fabric sensors have been fabricated by spray casting technology and the impacts of the preparation process on sensor performance have been investigated. When the casting concentration of MXene is 10 mg/mL and the fabric is spandex, the ammonia sensor exhibits the best sensing performance with a response time of 0.62 s for 100 ppm ammonia and a low limit of detection of 0.4 ppm. Notably, when the sensor used for detection ammonia in wafer cleaner volatiles, the responsivity was up to 96.61 %. This research offers an efficient route for developing MXene anchored fabric for room-temperature operated ammonia sensing.