Real-time Leak Research Articles

Near Real-time Leak Location by Inverse Analysis Integrating Measurement Uncertainty

AbstractThis paper presents a novel model-based method for near real-time pipe burst location in water distribution networks by integrating measurement uncertainty into inverse analysis. The method accounts for expected errors between measured and computed values, providing a pipe burst location area whose size varies according to the expected error level and the burst size. The proposed method is demonstrated and compared with the traditional inverse approach using a real case study with artificial bursts of different sizes and with different pressure signal noise levels. The performance of both methods is also assessed and discussed considering the effect of seasonal water demands. The traditional inverse analysis fails to accurately locate the pipe burst events, and depending on the expected error level and pipe burst size, the obtained locations may be significantly further away from the real burst location. Conversely, the proposed method does not point to the exact burst location but provides an approximated area in which step-testing can be carried out to pinpoint the exact burst location; the size of this area can be larger or smaller depending on the burst flow rate and signal uncertainty.

Real-time leakage predictions of pneumatic controlled actuator during cycle test using machine learning

Real-time leakage predictions of pneumatic controlled actuator during cycle test using machine learning

Experimental investigation on a solar-powered oxy-hydrogen gas system for enhanced plastic pyrolysis

The escalating environmental concerns associated with plastic waste have intensified the search for sustainable waste management solutions. Plastic dispersion in the environment poses severe threats to ecosystems, human well-being, and agriculture. Consequently, addressing plastic pollution stands as a pressing ecological concern. In contrast, the adoption of sustainable solar energy is harnessed for the production of HHO (Oxy-Hydrogen) gas, offering a promising avenue for clean energy generation. Gas production is achieved through the process of electrolysis, utilizing solar panels as the primary energy source. To address storage challenges, a spiral pipe configuration is employed for the interim containment of the generated HHO gas. Given the highly flammable nature of HHO gas, stringent safety protocols are imperative throughout the production and storage phases. To ensure the safe handling of HHO gas, a sophisticated MQ-8 Sensor is employed for real-time leak detection within the gas containment system. This sensor plays a critical role in maintaining operational safety by promptly identifying and addressing any potential gas leaks that may arise within the tubing infrastructure. The HHO gas, generated through the electrolysis process, serves as the primary fuel for the pyrolysis of plastic. This optimization is crucial to maximize the efficiency of the pyrolysis process. As a result of the pyrolysis process, crude oil is produced as an intermediary product. This crude oil holds the potential for subsequent refinement, offering versatility for various applications and end uses. Hence, the integration of solar energy-driven HHO gas in the pyrolysis of plastic demonstrates a promising avenue for mitigating plastic waste and contributing to environmental cleanliness. The intricate technological aspects, ranging from gas production through electrolysis, leak detection, optimized combustion, to crude oil production and refinement, collectively establish a comprehensive framework for sustainable waste management and resource recovery.

Pipeline Leak Detection: A Comprehensive Deep Learning Model Using CWT Image Analysis and an Optimized DBN-GA-LSSVM Framework.

Detecting pipeline leaks is an essential factor in maintaining the integrity of fluid transport systems. This paper introduces an advanced deep learning framework that uses continuous wavelet transform (CWT) images for precise detection of such leaks. Transforming acoustic signals from pipelines under various conditions into CWT scalograms, followed by signal processing by non-local means and adaptive histogram equalization, results in new enhanced leak-induced scalograms (ELIS) that capture detailed energy fluctuations across time-frequency scales. The fundamental approach takes advantage of a deep belief network (DBN) fine-tuned with a genetic algorithm (GA) and unified with a least squares support vector machine (LSSVM) to improve feature extraction and classification accuracy. The DBN-GA framework precisely extracts informative features, while the LSSVM classifier precisely distinguishes between leaky and non-leak conditions. By concentrating solely on the advanced capabilities of ELIS processed through an optimized DBN-GA-LSSVM model, this research achieves high detection accuracy and reliability, making a significant contribution to pipeline monitoring and maintenance. This innovative approach to capturing complex signal patterns can be applied to real-time leak detection and critical infrastructure safety in several industrial applications.

Optimal leakage detection and classification of the water distribution network based on the machine learning approach

ABSTRACT Water distribution networks are operated to supply tap water stably, but they suffer from leakage due to various factors, resulting in poor performance. In Korea, IoT (Internet of Things)-based smart sensors are installed to reduce these leaks, and a large amount of data is collected. In this study, to solve the data uncertainty and verification of the model's field applicability, the eXtreme Gradient Boosting (XGBoost) model, which is based on boosting, was selected. This choice was made through hyperparameter optimization using leak detection data obtained from leak detection sensors, as well as data refined through on-site leak detection. The area under the curve_receiver operating characteristic value for each leak class using the XGB model was 0.9955 for class 0, 0.9956 for class 1, and 0.9985 for class 2. In addition, the partial dependence plot of 120 Hz, which has the highest variable importance, was derived from analyzing the influence of the leak detection and classification model on the amount of leak vibration change at 120 Hz. Therefore, it is expectged the suggested methods can be used to build a GIS-based monitoring system for real-time leak detection by linking the developed model and the location of the leak detection sensor.

Development of a Conservative Hamiltonian Dynamic System for the Early Detection of Leaks in Pressurized Pipelines

In this study, we propose an innovative approach for real-time leakage detection in pipelines by integrating conservative Hamiltonian equations and experimental Internet of Things (IoT) technologies. The proposed method combines a hybrid model that utilizes sensors and IoT devices to acquire real-time data and solves the coupled system of Hamiltonian equations using the ODE45 numerical integration method. Spectral frequency analysis is an essential part of this method, as it reveals specific patterns in the pressure and flow signals. The findings highlighted 95% accuracy in leak detection, which was validated through a comparison of the theoretical and experimental data. The novelty of this approach lies in its ability to maintain constant total system energy, thereby enabling continuous monitoring for early leak detection. As an improvement, the proper handling of sensor signals is emphasized, underscoring its contribution to the efficient management of water resources in potable water distribution systems. Doi: 10.28991/CEJ-2024-010-04-01 Full Text: PDF

Multi-Phase Flow Metering for Offshore Pipeline Leak Detection: Anomaly Detection Using AI Algorithms

Offshore oil and gas operations have long relied on Multi-Phase FlowMetering (MPFM) technology to accurately monitor the flow of oil, gas, and water within pipelines. As offshore pipeline networks expand and age, the risks of undetected leaks grow significantly, leading to economic losses and environmental disasters. Traditional MPFM systems focus on fluid composition and flow rate monitoring but fall short in providing real-time leak detection capabilities. The integration of Artificial Intelligence (AI), specifically anomaly detection algorithms, into MPFM systems presents a promising solution for addressing this critical gap in pipeline monitoring. Anomaly detection using AI involves training predictive models on historical pipeline data to identify irregularities in flow patterns that could signify a leak. By utilizing machine learning techniques such as random forests, k-means clustering, and autoencoders, AI models can detect subtle deviations in pipeline behavior that are often overlooked by conventional monitoring methods. This paper explores the application of AI-driven anomaly detection to offshore pipeline leak detection, offering a comprehensive review of the existing literature on MPFM technology, AI in industrial settings, and the evolving role of predictive analytics in oil and gas operations. The methodology outlines the steps taken to develop and validate a predictive model using historical MPFM data from a real-world offshore pipeline in Nigeria. Through a case study focusing on an offshore pipeline in the Niger Delta, this research demonstrates the effectiveness of AI in identifying leaks before they result in major economic or environmental damage. Results from the case study highlight significant improvements in leak detection accuracy, early identification of leaks, and cost savings through the implementation of AI-enhanced MPFM systems. By integrating real-time data, such as pressure changes, flow rates, and fluid composition, the proposed AI-driven model offers a dynamic approach to pipeline monitoring. This approach not only automates the detection of potential leaks but also reduces the time it takes to identify and mitigate leaks, ultimately leading to enhanced operational efficiency and environmental safety. Additionally, the study explores the economic and environmental impact of early leak detection, emphasizing the importance of incorporating AI into MPFM systems for long-term sustainability in offshore oil and gas operations. The findings of this research suggest that AI has the potential to revolutionize pipeline monitoring by enhancing the capabilities of MPFM systems, making them more responsive to leaks and better equipped to handle the complexities of multi-phase flow in offshore environments. As offshore pipelines become more susceptible to leaks due to age and harsh operational conditions, the integration of AI-driven anomaly detection will be key to ensuring the continued viability and safety of offshore oil and gas infrastructure. This paper concludes by outlining future research directions and recommending best practices for deploying AI-enhanced MPFM systems to optimize leak detection and pipeline integrity management in offshore settings.

High-sensitivity water leakage detection and localization in tunnels using novel ultra-weak fiber Bragg grating sensing technology

Water leakage poses a serious threat to tunnel integrity and safety, making early detection critical. However, traditional techniques struggle with high-sensitivity leak monitoring. This paper presents a novel super absorbent polymer (SAP)-coated ultra-weak fiber Bragg grating (UWFBG) strain sensing cable for enhanced water leakage detection and localization in tunnels. The cable’s response and resulting morphological/strain changes upon leakage contact are characterized. When water interacts with the SAP-coated region, the polymer rapidly absorbs it and expands, producing a significant localized strain reduction. The cable demonstrates the repeatability for leakage sensing, addressing existing challenges. Tunnel model tests establish it can discriminate single-point, multi-point and linear leakages. Analysis of strain variations under different leakage rates, down to 1.5 mL/min, confirms strong performance for early warning. Installation along tunnel joints also enables leakage detection. In summary, the distributed SAP-UWFBG cable sensing system offers high sensitivity, real-time leakage monitoring capabilities critical for ensuring tunnel structural integrity and safety through its fast response to even minor leakage events.

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.

A Hybrid Deep Learning Approach: Integrating Short-Time Fourier Transform and Continuous Wavelet Transform for Improved Pipeline Leak Detection.

A hybrid deep learning approach was designed that combines deep learning with enhanced short-time Fourier transform (STFT) spectrograms and continuous wavelet transform (CWT) scalograms for pipeline leak detection. Such detection plays a crucial role in ensuring the safety and integrity of fluid transportation systems. The proposed model leverages the power of STFT and CWT to enhance detection capabilities. The pipeline's acoustic emission signals during normal and leak operating conditions undergo transformation using STFT and CWT, creating scalograms representing energy variations across time-frequency scales. To improve the signal quality and eliminate noise, Sobel and wavelet denoising filters are applied to the scalograms. These filtered scalograms are then fed into convolutional neural networks, extracting informative features that harness the distinct characteristics captured by both STFT and CWT. For enhanced computational efficiency and discriminatory power, principal component analysis is employed to reduce the feature space dimensionality. Subsequently, pipeline leaks are accurately detected and classified by categorizing the reduced dimensional features using t-distributed stochastic neighbor embedding and artificial neural networks. The hybrid approach achieves high accuracy and reliability in leak detection, demonstrating its effectiveness in capturing both spectral and temporal details. This research significantly contributes to pipeline monitoring and maintenance and offers a promising solution for real-time leak detection in diverse industrial applications.

High-Precision Single-Leak Detection and Localization in Single-Phase Liquid Pipelines Using the Negative Pressure Wave Technique: An Application in a Real-Field Case Study

Summary This paper presents a novel workflow for high-precision leak detection in pipeline networks using the negative pressure wave (NPW) technique. The proposed workflow addresses challenges associated with noisy and convoluted pressure transducer data, rapid pressure decay, and the need for robustness in leak event detection. To overcome these challenges, the workflow incorporates data preprocessing techniques for cleansing, normalization, and denoising, as well as dynamic pressure control limit lines to differentiate between pump and leak events. Multiple transducer analysis techniques are used to minimize false positives. Synthetic leak scenarios are generated using the Water Network Tool for Resilience (WNTR) package, enabling a comprehensive assessment of the workflow’s performance. The generated scenarios are validated through pressure history matching against field inline pressure recordings. A dashboard is developed for real-time visualization and verification of leak events. The effectiveness of the workflow is demonstrated through testing on a real network, resulting in the successful detection and precise localization of a confirmed leak event. The workflow proves its capability to achieve high accuracy, with a 100-m resolution in a complex network configuration with 29 pipe sections and 1-Hz pressure signal recordings. For synthetic leak events, a 10-Hz pressure signal is utilized, achieving a remarkable 10-m accuracy. Moreover, the integration of the workflow with supervisory control and data acquisition (SCADA) systems is showcased, highlighting its potential for near real-time leak detection in practical applications. Overall, this paper presents a comprehensive and effective workflow for high-precision leak detection and localization in pipeline networks, offering valuable insights into improving the efficiency and reliability of leak detection systems.

Study on Radial Leakage Model and Law of Fractured Formation Drilling Fluid

Wellbore leakage mostly occurs in structurally developed fractured formations. Analyzing the real-time leakage rate during the drilling process plays an important role in identifying the leakage mechanism and its rules on-site. Based on the principles of fluid mechanics and using Herschel-Bulkley (H-B) drilling fluid, by reasonably simplifying the drilling fluid performance parameters, fracture roughness characteristic parameters, pressure difference between the wellbore and formation, and the radial extension length of drilling fluid, the radial leakage model is improved to improve the calculation accuracy. Using the Euler format in numerical analysis to solve the model and with the help of numerical analysis software, the radial leakage law of this flow pattern in the fractures is obtained. The results show that the deformation coefficient of the fracture index, fracture aperture, pressure difference, leakage rate, and cumulative leakage rate are positively correlated. The larger the curvature of the fracture, the rougher the fracture, and the smaller the leakage rate and cumulative leakage rate. The larger the consistency coefficient of the drilling fluid, the greater the additional resistance between the fractures, and the smaller the leakage rate and cumulative leakage rate. As the extending length of the fracture increases, the invasion of drilling fluid decreases, the leakage rate slows down, and eventually reaches zero, with the maximum cumulative leakage rate.

Leak detection and localization in water distribution networks using conditional deep convolutional generative adversarial networks

This paper explores the use of ‘conditional convolutional generative adversarial networks’ (CDCGAN) for image-based leak detection and localization (LD&L) in water distribution networks (WDNs). The method employs pressure measurements and is based on four pillars: (1) hydraulic model-based generation of leak-free training data by taking into account the demand uncertainty, (2) conversion of hydraulic model input demand-output pressure pairs into images using kriging interpolation, (3) training of a CDCGAN model for image-to-image translation, and (4) using the structural similarity (SSIM) index for LD&L. SSIM, computed over the entire pressure distribution image is used for leak detection, and a local estimate of SSIM is employed for leak localization. The CDCGAN model employed in this paper is based on the pix2pix architecture. The effectiveness of the proposed methodology is demonstrated on leakage datasets under various scenarios. Results show that the method has an accuracy of approximately 70% for real-time leak detection. The proposed method is well-suited for real-time applications due to the low computational cost of CDCGAN predictions compared to WDN hydraulic models, is robust in presence of uncertainty due to the nature of generative adversarial networks, and scales well to large and variable-sized monitoring data due to the use of an image-based approach.

Nonintrusive Sensor System Developed Using a Force-Sensing Resistor for Pipeline Integrity Monitoring

Pipelines are crucial for transporting liquid and gaseous resources at various industrial sites. As unexpected pipeline failures and improper spill responses often cause detrimental effects on the environment and public safety, real-time leakage monitoring and detection are important. This study proposes a nonintrusive sensor system to measure the pressure and integrity of a pipeline in real time by easily installing nonintrusive sensors on the exterior of the pipelines. Initially, a prototype of the nonintrusive pressure-monitoring sensor was designed and fabricated. This sensor correlated the changes in pressure inside a pipe with those in the contact force exerted between the pipe and the surrounding high-stiffness band; a force-sensing resistor was used to measure the contact force. Furthermore, parametric studies were performed on a six-inch pipe, which indicated that the developed sensor can accurately monitor the inner pressure of the pipe. We also developed a methodology to determine the optimal sensor placement in a pipeline network for detecting damages using the Euclidean distance method. This was combined with the methodology of determining the decision boundary for classifying the damage location using a support vector machine to rapidly detect the damage using a minimum number of sensors. Finally, a nonintrusive sensor system was installed on the fire main pipeline system, and the damage detection performance was experimentally validated. The developed nonintrusive sensor system can be easily installed on operating pipes to monitor the inner pressure of a pipe in real-time and accurately detect damages in a pipeline network.

Real-time rapid leakage estimation for deep space habitats using exponentially-weighted adaptively-refined search

The recent accelerated growth in space-related research and development activities makes the near-term need for long-term extraterrestrial habitats evident. Such habitats must operate under continuous disruptive conditions arising from extreme environments like meteoroid impacts, extreme temperature fluctuations, galactic cosmic rays, destructive dust, and seismic events. Loss of air or atmospheric leakage from a habitat poses safety challenges that demand proper attention. Such leakage may arise from micro-meteoroid impacts, crack growth, bolt/rivet loosening, and seal deterioration. In this paper, leakage estimation in deep space habitats is posed as an inverse problem. A forward pressure-based dynamical model is formulated for atmospheric leakage. Experiments are performed on a small-scaled pressure chamber where different leakage scenarios are emulated and corresponding pressure values are measured. An exponentially-weighted adaptively-refined search (EWARS) algorithm is developed and validated for the inverse problem of real-time leakage estimation. It is demonstrated that the proposed methodology can achieve real-time estimation and tracking of constant and variable leaks with accuracy.

Acoustic feature-based leakage event detection in near real-time for large-scale water distribution networks

Abstract Acoustic sensors are widely deployed to detect hidden leakages in water distribution networks (WDNs). However, few studies have been conducted to quantitatively understand the dominant leakage acoustic characteristics, which are usually mixed with unknown environmental noises, coupled with the constraint of sparse deployment of acoustic sensors. In this paper, a comprehensive approach, that performs acoustic data feature analysis, is developed to detect pipe leakages in near real-time via a series of systematic analyses, namely: (1) data quality assessment; (2) features identifications; (3) outlier detection and event classification; and finally (4) near real-time leakage detection. The proposed solution has been tested on two major WDNs in Singapore having around 1,000 km of water pipelines installed with 74 permanently installed hydrophone sensors. The leakage detection results obtained from our case study demonstrate that the dominant leakage acoustic characteristics can be captured in lower intrinsic mode functions (IMFs), to within the frequency range of 100–750 Hz approximately, by decomposing the original acoustic signal. Systemwide leakage event detection and classification models are subsequently trained and tested on acoustic datasets collected over 13 historical months, where more than 70% F1-scores can be obtained from the emulated near real-time leakage detection analysis.

NSTX-U Machine Core Vacuum Seals Upgrade Design

The vacuum in the National Spherical Torus Experiment Upgrade (NSTX-U) is in the range of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.66\,\times \,10 ^{-6}$ </tex-math></inline-formula> Pa ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\,\times \,10 ^{-8}$ </tex-math></inline-formula> torr). To enhance vacuum seals and lower gas permeation in the NSTX-U and therefore ensuring operation safety and quality, the machine core vacuum seal design has been upgraded using double elastomeric seals and pumped interspaces (guard channels). In NSTX-U machine core, there are six major vacuum seals, including upper and lower vacuum vessel main flanges, upper and lower bellows flanges, and two in the ceramic isolation ring assembly that are mounted to the upper vacuum vessel main flange. The elastomeric seals are commercially available and customized fluorocarbon (Viton) O-rings. To ensure appropriate O-ring compression ratios for the vacuum seal, the O-ring grooves are customized with appropriate tolerances considering specific situations for each seal. In this upgrade, the vacuum in each interspace is designed to reach 13.33 Pa (0.1 torr), thus the overall pressure difference crossing each double O-ring seal is expected to be reduced by at least three orders of magnitude, and accordingly the overall rates of leakage and permeation will be lowered by three orders of magnitude. The corresponding pump-down times for each seal, at room temperature, are estimated at about 60 s. In addition, the vacuum pumped interspaces will be able to function as a real-time leak monitor.

Real-Time Leak Diagnosis in Water Distribution Systems Based on a Bank of Observers and a Genetic Algorithm

The main contribution of this paper is to present a novel solution for the leak diagnosis problem in branched pipeline systems considering the availability of pressure head and flow rate sensors on the upstream (unobstructed) side and the downstream (constricted) side. This approach is based on a bank of Kalman filters as state observers designed on the basis of the classical water hammer equations and a related genetic algorithm (GA) which includes a fitness function based on an integral error that helps obtaining a good estimation despite the presence of noise. For solving the leak diagnosis problem, three stages are considered: (a) the leak detection is performed through a mass balance; (b) the region where the leak is occurring is identified by implementing a reduced bank of Kalman filters which localize the leak by sweeping all regions of the branching pipeline through a GA that reduces the computational effort; (c) the leak position is computed through an algebraic equation derived from the water hammer equations in steady-state. To assess this methodology, experimental results are presented by using a test bed built at the Tuxtla Gutiérrez Institute of Technology, Tecnológico Nacional de México (TecNM). The obtained results are then compared with those obtained using a classic extended Kalman filter which is widely used in solving leak diagnosis problems and it is highlighted that the GA approach outperforms the EKF in two cases whereas the EKF is better in one case.

Literature Review of Data Analytics for Leak Detection in Water Distribution Networks: A Focus on Pressure and Flow Smart Sensors

Leakage detection is one of the important aspects of water distribution management. Water companies are exploring alternative approaches to detect leaks in a timely manner with high accuracy to reduce water losses and minimize environmental and economic consequences. In this article, a literature review is presented to develop a step-by-step analytic framework for the leakage detection process based on flow and pressure data collected from water distribution networks. The main steps of the data analytic for leakage detection are: setting up the goals, data collection, preparing the gathered data, analyzing the prepared data, and method evaluation. The issues of concern for each step of the proposed leakage detection framework are analyzed and discussed. The smart sensor-based leakage detection methods can be categorized as data-driven methods and model-based methods. Data-driven methods can be further categorized as statistical process control-based methods, prediction-classification methods, and clustering methods. Hydraulic model-based methods can be further categorized as calibration-based methods, sensitivity analysis, and classifier-based methods. The advantages and disadvantages of each method are discussed, and suggestions for future research are provided. This review represents a new perspective on the subject from five aspects: (1) most of the leakage detection methods are focused on burst detection, and different types of leakage should be considered in future research, (2) it is important to consider data uncertainties, and more robust real-time leakage detection methods should be developed, (3) it is important to consider hydraulic model uncertainties, (4) unrealistic assumptions should be addressed in future research, and (5) spatial relations between sensors could provide more information and should be considered.

Evaluation of the Leak Detection Performance of Distributed Kalman Filter Algorithms in WSN-Based Water Pipeline Monitoring of Plastic Pipes

Water is a basic necessity and one of the most valuable resources for human living. Sadly, large quantities of treated water get lost daily worldwide, especially in developing countries, through leaks in the water distribution network. Wireless sensor network-based water pipeline monitoring (WWPM) systems using low-cost micro-electro-mechanical systems (MEMS) accelerometers have become popular for real-time leak detection due to their low-cost and low power consumption, but they are plagued with high false alarm rates. Recently, the distributed Kalman filter (DKF) has been shown to improve the leak detection reliability of WWPM systems using low-cost MEMS accelerometers. However, the question of which DKF is optimal in terms of leak detection reliability and energy consumption is still unanswered. This study evaluates and compares the leak detection reliability of three DKF algorithms, selected from distributed data fusion strategies based on diffusion, gossip and consensus. In this study, we used a combined approach involving simulations and laboratory experiments. The performance metrics used for the comparison include sensitivity, specificity and accuracy. The laboratory results revealed that the event-triggered diffusion-based DKF is optimal, having a sensitivity value of 61%, a specificity value of 93%, and an accuracy of 90%. It also has a lower communication burden and is less affected by packet loss, making it more responsive to real-time leak detection.