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Pipeline is the most economical and efficient way of oil transportation, pipeline integrity management is an effective means to prevent pipeline accidents and ensure the safe operation of pipelines economically and reasonably, timely and accurate pipeline leakage monitoring can greatly reduce the consequences of accidents, which is a crucial part of pipeline integrity management. Pipeline leakage monitoring in China has a long history, but there is no systematic summary. The monitoring methods for oil pipelines in China are reviewed and analyzed from the levels of national and industrial, by surveying the mileage of oil pipelines, leakage accidents, laws and regulations, and pipeline working conditions in recent years. The principles and research status of oil pipeline leakage monitoring methods are introduced in detail, and various methods are divided into hardware and software by the different acquisition and processing methods of leakage signals. Finally, the development trend of pipeline leakage monitoring methods in the future is summarized and predicted: the combination of software and hardware, the efficient collection and processing of data, the intelligent analysis and diagnosis of working conditions, and the integration of information systems. [ABSTRACT FROM AUTHOR]

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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%. [ABSTRACT FROM AUTHOR]

(1) Background: In real road scenarios, various complex environmental conditions may occur, including bright lights, nighttime, rain, and snow. In such a complex environment for detecting pole-shaped obstacles, it is easy to lose the feature information. A high rate of leakage detection, false positives, and measurement errors are generated as a result. (2) Methods: The first part of this paper utilizes the improved YOLOv5 algorithm to detect and classify pole-shaped obstacles. Then, the identified target frame information is combined with binocular stereo matching to obtain more accurate distance information. (3) Results: The experimental results demonstrate that this method achieves a mean average precision (mAP) of 97.4% for detecting pole-shaped obstacles, which is 3.1% higher than the original model. The image inference time is only 1.6 ms, which is 1.8 ms faster than the original algorithm. Additionally, the model size is only 19.0 MB. Furthermore, the range error of this system is less than 7% within the range of 3–15 m. (4) Conclusions: Therefore, the algorithm not only achieves real-time and accurate identification and classification but also ensures precise measurement within a specific range. Meanwhile, the model is lightweight and better suited for deploying sensing systems. [ABSTRACT FROM AUTHOR]

Memory leaks, an important and difficult issue in software development, occur when an object is inadvertently retained longer than necessary. Programming languages provide a variety of dynamic memory management methods to support programmers in preventing the introduction of defects that cause memory leaks. However, it is not yet possible to completely free programmers from the work of memory management. Indeed, runtime leak detection is time consuming and usually done after the fact, while manual code inspection requires rich developer experience. Understanding the common patterns of memory leaks can help developers be mindful of leaks or avoid them at an earlier stage during the development process and may further inspire future research. Eight code patterns are found in our case study specifically for memory leaks caused by circular references in Python. The observed patterns can explain 91.64% of the memory leaks in the studied projects. Our work can guide important decisions about the possibility of identifying memory leaks with static code analysis. [ABSTRACT FROM AUTHOR]

Deep learning algorithm has a wide range of applications and excellent performance in the field of engineering image recognition. At present, the detection and recognition of buried metal pipeline defects still mainly rely on manual work, which is inefficient. In order to realize the intelligent and efficient recognition of pipeline magnetic flux leakage (MFL) inspection images, based on the actual demand of MFL inspection, this paper proposes a new object detection framework based on YOLOv5 and CNN models in deep learning. The framework first uses object detection to classify the targets in MFL images and then inputs the features containing defects into a regression model based on CNN according to the classification results. The framework integrates object detection and image regression model to realize the target classification of MFL pseudo color map and the synchronous recognition of metal loss depth. The results show that the target recognition ability of the model is good, its precision reaches 0.96, and the mean absolute error of the metal loss depth recognition result is 1.14. The framework has more efficient identification ability and adaptability and makes up for the quantification of damage depth, which can be used for further monitoring and maintenance strategies. [ABSTRACT FROM AUTHOR]

In the future, the world is likely to face water and therefore food shortages due to reasons such as global warming, population growth, the melting of glaciers, the destruction of agricultural lands over time or their use for different purposes, and environmental pollution. Although technological developments are important for people to live a more comfortable and safer life, it is also possible to reduce and even repair the damage to nature and protect nature itself thanks to new technologies. There is a requirement to detect abnormal water usage in agriculture to avert water scarcity, and an electronic system can help achieve this objective. In this research, an experimental study was carried out to detect water leaks in the field in order to prevent water losses that can occur in agriculture, where water consumption is the highest. Therefore, in this study, low-cost embedded electronic hardware was developed to detect over-watering by means of normal and thermal camera sensors and to collect the required data, which can be installed on a mobile agricultural robot. For image processing and the diagnosis of abnormal conditions, the collected data were transferred to a personal computer server. Then, software was developed for both the low-cost embedded system and the personal computer to provide a faster detection and decision-making process. The physical and software system developed in this study was designed to provide a water leak detection process that has a minimum response time. For this purpose, mathematical and image processing algorithms were applied to obtain efficient water detection for the conversion of the thermal sensor data into an image, the image size enhancement using interpolation, the combination of normal and thermal images, and the calculation of the image area where water leakage occurs. The field experiments for this developed system were performed manually to observe the good functioning of the system. [ABSTRACT FROM AUTHOR]

The escalating global water usage and the increasing strain on major cities due to water shortages highlights the critical need for efficient water management practices. In water-stressed regions worldwide, significant water wastage is primarily attributed to leakages, inefficient use, and aging infrastructure. Undetected water leakages in buildings' pipelines contribute to the water waste problem. To address this issue, an effective water leak detection method is required. In this paper, we explore the application of edge computing in smart buildings to enhance water management. By integrating sensors and embedded Machine Learning models, known as TinyML, smart water management systems can collect real-time data, analyze it, and make accurate decisions for efficient water utilization. The transition to TinyML enables faster and more cost-effective local decision-making, reducing the dependence on centralized entities. In this work, we propose a solution that can be adapted for effective leakage detection in real-world scenarios with minimum human intervention using TinyML. We follow an approach that is similar to a typical machine learning lifecycle in production, spanning stages including data collection, training, hyperparameter tuning, offline evaluation and model optimization for on-device resource efficiency before deployment. In this work, we considered an existing water leakage acoustic dataset for polyvinyl chloride pipelines. To prepare the acoustic data for analysis, we performed preprocessing to transform it into scalograms. We devised a water leak detection method by applying transfer learning to five distinct Convolutional Neural Network (CNN) variants, which are namely EfficientNet, ResNet, AlexNet, MobileNet V1, and MobileNet V2. The CNN models were found to be able to detect leakages where a maximum testing accuracy, recall, precision, and F1 score of 97.45%, 98.57%, 96.70%, and 97.63%, respectively, were observed using the EfficientNet model. To enable seamless deployment on the Arduino Nano 33 BLE edge device, the EfficientNet model is compressed using quantization resulting in a low inference time of 1932 ms, a peak RAM usage of 255.3 kilobytes, and a flash usage requirement of merely 48.7 kilobytes. [ABSTRACT FROM AUTHOR]

In this paper, an approach to perform leak state detection and size identification for industrial fluid pipelines with an acoustic emission (AE) activity intensity index curve (AIIC), using b-value and a random forest (RF), is proposed. Initially, the b-value was calculated from pre-processed AE data, which was then utilized to construct AIICs. The AIIC presents a robust description of AE intensity, especially for detecting the leaking state, even with the complication of the multi-source problem of AE events (AEEs), in which there are other sources, rather than just leaking, contributing to the AE activity. In addition, it shows the capability to not just discriminate between normal and leaking states, but also to distinguish different leak sizes. To calculate the probability of a state change from normal condition to leakage, a changepoint detection method, using a Bayesian ensemble, was utilized. After the leak is detected, size identification is performed by feeding the AIIC to the RF. The experimental results were compared with two cutting-edge methods under different scenarios with various pressure levels and leak sizes, and the proposed method outperformed both the earlier algorithms in terms of accuracy. [ABSTRACT FROM AUTHOR]

Water distribution networks are often susceptible to pipeline leaks caused by mechanical damages, natural hazards, corrosion, and other factors. This paper focuses on the detection of leaks in water distribution networks (WDN) using a data-driven approach based on machine learning. A hybrid autoencoder neural network (AE) is developed, which utilizes unsupervised learning to address the issue of unbalanced data (as anomalies are rare events). The AE consists of a 3DCNN encoder, a ConvLSTM decoder, and a ConvLSTM future predictor, making the anomaly detection robust. Additionally, spatial and temporal attention mechanisms are employed to enhance leak localization. The AE first learns the expected behavior and subsequently detects leaks by identifying deviations from this expected behavior. To evaluate the performance of the proposed method, the Water Network Tool for Resilience (WNTR) simulator is utilized to generate water pressure and flow rate data in a water supply network. Various conditions, such as fluctuating water demands, data noise, and the presence of leaks, are considered using the pressure-driven demand (PDD) method. Datasets with and without pipe leaks are obtained, where the AE is trained using the dataset without leaks and tested using the dataset with simulated pipe leaks. The results, based on a benchmark WDN and a confusion matrix analysis, demonstrate that the proposed method successfully identifies leaks in 96% of cases and a false positive rate of 4% compared to two baselines: a multichannel CNN encoder with LSTM decoder (MC-CNN-LSTM) and a random forest and model based on supervised learning with a false positive rate of 8% and 15%, respectively. Furthermore, a real case study demonstrates the applicability of the developed model for leak detection in the operational conditions of water supply networks using inline sensor data. [ABSTRACT FROM AUTHOR]

Spontaneous intracranial hypotension (SIH) may lead to cerebral venous thrombosis (CVT). This case report describes the diagnostic and treatment processes used for a patient with CVT caused by SIH due to spontaneous spinal cerebrospinal fluid (CSF) leakage in the high cervical region. Clinical data were collected from a 37-year-old man with an initial symptom of spontaneous posterior cervical pain. The diagnostic and treatment processes of SIH-induced CVT were described. A magnetic resonance imaging (MRI) study showed superior sagittal sinus thrombosis, and a lumbar puncture revealed a low initial CSF pressure of less than 60 mmH2O. The patient underwent anticoagulation and fluid rehydration therapies. No abnormalities were observed in the thoracic MRI scan, but a cervical MRI scan revealed a spontaneous CSF leak. An epidural blood patch with autologous blood was performed, and symptoms completely resolved 3 days after the procedure. This report proposes a diagnostic procedure for detecting rare cases of SIH-induced CVT, thereby preventing future misdiagnoses and delayed treatment. When a patient presenting with CVT in conjunction with intracranial hypotension has no history of trauma or piercing, SIH caused by spontaneous spinal CSF leakage should be considered as a potential cause of secondary low intracranial pressure. For detection of CSF leaks at rare sites, an MRI of the whole spine rather than a localized MRI of the spine needs to be performed to avoid misdiagnosis. An epidural blood patch should be performed as soon as possible as it may shorten the length of hospitalization and improve prognosis. [ABSTRACT FROM AUTHOR]

Hydrogen has emerged as a promising carbon-neutral fuel source, spurring research and development efforts to facilitate its widespread adoption. However, the safe handling of hydrogen requires precise leak detection sensors due to its low activation energy and explosive potential. Various detection methods exist, with thermal conductivity measurement being a prominent technique for quantifying hydrogen concentrations. However, challenges remain in achieving high measurement sensitivity at low hydrogen concentrations below 1% for thermal-conductivity-based hydrogen sensors. Recent research explores the 3ω method's application for measuring hydrogen concentrations in ambient air, offering high spatial and temporal resolutions. This study aims to enhance hydrogen leak detection sensitivity using the 3ω method by conducting thermal analyses on sensor design variables. Factors including substrate material, type, and sensor geometry significantly impact the measurement sensitivity. Comparative evaluations consider the minimum detectable hydrogen concentration while accounting for the uncertainty of the 3ω signal. The proposed suspended-type 3ω sensor is capable of detecting hydrogen leaks in ambient air and provides real-time measurements that are ideal for monitoring hydrogen diffusion. This research serves to bridge the gap between precision and real-time monitoring of hydrogen leak detection, promising significant advancements in the related safety applications. [ABSTRACT FROM AUTHOR]

Water leakage from aging water and wastewater pipes is a persistent problem, necessitating the improvement of existing leak detection and response methods. In this study, we conducted an analysis of essential features based on data collected from leak detection sensors installed at water meter boxes and water outlets of pipelines. The water pipeline data collected through the vibration sensor were preprocessed by converting it into a tabular form by frequency band and applied to various machine learning models. The characteristics of each model were analyzed, and XGBoost was selected as the most suitable leak detection model with a high accuracy of 99.79%. These systems can effectively reduce leak detection and response time, minimize water waste, and minimize economic losses. Additionally, this technology can be applied to various fields that utilize water pipes, making it widely applicable. [ABSTRACT FROM AUTHOR]

Many jurisdictions are introducing methane reduction policy as part of climate commitments, with a focus on the oil and gas supply chain. Periodic comprehensive leak detection and repair (LDAR) surveys or screening leak detection and repair surveys are required in the British Columbia, Canada oil and gas sector to reduce unintentional methane emissions caused by leaking infrastructure. By finding and fixing leaks quickly, emissions of methane, a potent greenhouse gas, are reduced. In this study, we explored how effectively British Columbia's regulation, deposited in December 2018, made progress towards meeting the policy objective of a 45% decrease in methane emissions by 2025 from a 2014 baseline. We evaluated the performance and cost effectiveness of regulatory-prescribed LDAR programmes using data collected by the BC Energy Regulator (formerly BC Oil and Gas Commission) for the 2020 year, and survey cost data submitted by service providers. We found that the new regulation was only partially effective due to low compliance rates. We also observed heavy tail leak distributions in LDAR data collected by service companies, but comparatively narrow leak distributions in data from permit holders who internalized LDAR operations, suggesting a difference in work practice or the use of equipment. Comprehensive leak detection surveys were found to be more cost efficient ($23 CAD/tCO2e reduced) compared to screening surveys ($1,787 CAD/tCO2e reduced) because more methane leaks are detected in comprehensive assessments. To meet methane reduction targets, we recommend that: 1) jurisdictions work to improve compliance rates by introducing minimum administrative penalties specific to noncompliance; 2) that all future LDAR surveys are instrument-based; 3) limiting turnaround time limits so that repairs are not delayed indefinitely; and 4) that LDAR completed internally by permit holders undergo independent third-party verification. Compliance is a key determinant of regulatory effectiveness. Data availability, transparency, and quality is critical to assessing regulatory effectiveness. Workers conducting LDAR surveys and measuring leaks must be trained to minimize errors. Instrument-based LDAR surveys are more effective and efficient at reducing leaked methane emissions than non-instrument-based methods. Instrument-based LDAR surveys can cost effectively reduce leaked methane emissions. [ABSTRACT FROM AUTHOR]

Early detection of leaks in natural gas gathering pipelines makes proactive maintenance and corrective measures take place more effective, thus enhancing the safe and reliable operation of pipelines and mitigating serious threats to the environment and human life. The data-driven leak detection methods have become a natural choice owing to the extensive deployment of sensors in the pipeline network. However, the supervised and semi-supervised leak detection methods need to utilize a certain amount of leak data to train reliable classifiers, and labeled data usually difficult to obtain in real-world applications especially for the gas industry. Besides, most of data-driven methods today only consider the long-term or short-term patterns hidden in the multivariate time series rather than both, which may reduce their effectiveness since temporal data generated in the real-world applications often relates to a mixture of these two patterns. To settle this challenge, we propose a novel unsupervised leak detection method by utilizing a one-dimensional convolutional autoencoder (1D-CAE) to learn short-term dependency patterns from local time series and a long short-term memory (LSTM) to discover evolving trends about long-term patterns from entire temporal data. The proposed method not only calculates the difference between actual samples and their predictions but also compares the inputs and their reconstructions, where comparisons of reconstruction are further extended from input space to hidden spaces. The obtained deviations are then integrated through the developed integration strategy and employed to calculate their global leak scores. Specially, considering that the magnitudes of deviations integrated together are diverse or there may exist correlation among these deviations caused by correlated neurons across layers, the minimum covariance determinant (MCD) method is employed to scale deviations and eliminate correlations along the projection pathway. The effectiveness of the proposed method is verified on real datasets of natural gas gathering pipeline. In addition, analysis of experimental results demonstrates that the proposed method has good accuracy, and is conducive to operating in the context of uncontrolled long-term monitoring. [ABSTRACT FROM AUTHOR]

Detecting and localizing leakages in underground water pipelines continue to be challenging in large-scale water distribution networks (WDNs) having a low density of acoustic sensors per unit pipeline. Many previous acoustic research analyses have been restricted to either (1) lab-scale WDN setups having simulated leakage conditions, or (2) field-scale WDNs with high densities of acoustic sensors. To address the current challenges, this study develops a generic and practical acoustic data analysis framework to analyze acoustics signals for leakage detection and localization in large-scale WDNs, where the proposed framework encompasses multi-stage systematic analyses, namely: (1) data quality assessment; (2) acoustic features generation and analysis; (3) leakage event detection; and (4) leakage event localization. In collaboration with Public Utility Board (PUB), Singapore's National Water Agency, our proposed framework has been verified in large-scale WDNs in Singapore having more than 1,100 km of underground water pipelines and 82 permanently installed monitoring stations, each of which is instrumented to measure acoustics, via hydrophone equipment, pressure, and water quality parameters, and by using historical data collected between August 1, 2019 and August 31, 2020, where numerous leakage events were reported to within 1,000 m, or less, connected pipelines from independent hydrophones. [ABSTRACT FROM AUTHOR]

Various water distribution leak detection and localization methods have been proposed for supervisory control and data acquisition (SCADA) data collection systems. However, because their available numbers of measurements are limited, the SCADA systems are often insufficient to identify realistic sized leaks. A clear next step is to develop detection and localization methods for smart systems that collect advanced metering infrastructure (AMI) data (i.e., AMI systems); however, only the authors have proposed tools for the AMI data collection systems. To encourage the usage of AMI data, this study tested five types of data collection systems for leak detection and localization that measure (1) only source flows, (2) source flows and a few nodal pressures, (3) source flows and AMI demands, (4) AMI demands and a few nodal pressures, and (5) AMI pressures and AMI demands. An appropriate leak detection and localization tool for each data collection system is applied and tested for two water distribution networks: one located in Austin, TX, and the other in Tucson, AZ. Each system's performance was evaluated using metrics of detection probability, false alarm rate, time to detect, and localization pipe distance. Overall, based on the obtained results, the SCADA systems were poor in detecting realistic-sized leaks, while the AMI systems successfully identified those small failures. Thus, the AMI systems were required to improve detection, and a pressure-supplemented AMI system was necessary to obtain high localization performance, particularly for a large network, such as Austin, Texas. [ABSTRACT FROM AUTHOR]

In response to the frequent occurrence of leakage accidents in heating pipelines, timely detection of leakage points in such pipelines is of great significance to ensure the safe operation of heating systems. This article proposes a method for detecting leakage points in heating pipelines using drones equipped with infrared thermal imagers, employing a combination of the improved R3Det algorithm and the adaptive threshold method. Firstly, the algorithm identifies the area of the heating pipeline and then employs the adaptive threshold method to detect the presence of leakage points in the identified pipeline area. Additionally, taking into account the morphological characteristics of heating pipelines, the R3Det network is enhanced by introducing variable convolution, enabling more precise extraction of pipeline features. To reduce model overfitting and enhance network expression capabilities, the H-swish activation function is employed to replace the original activation function. Furthermore, candidate anchor boxes are clustered using the K-means++ clustering algorithm to obtain better position regression results and improve training efficiency. The improved algorithm demonstrates significantly better positioning precision compared to the original network. Moreover, an adaptive threshold algorithm is proposed for leak detection and labelling, utilising the original temperature information contained in infrared images. The experimental results demonstrate that this method achieves higher accuracy in detecting leaks in heating pipelines. [ABSTRACT FROM AUTHOR]

In order to solve the problem of inconspicuous leakage signal characteristics under external noise interference, a leakage detection method based on the combination of variational modal decomposition (VMD) and support vector machine (SVM) is proposed. The method first calculates the spearman correlation coefficients (SCC) of multiple intrinsic modal components (IMFs) obtained by VMD with the source signal, then extracts the energy and central frequency features of IMFs with larger SCC, and finally performs leak detection using the SVM classifier. The experimental results show that the VMD-SVM method can effectively perform leak detection with an accuracy of 98.27%. The accuracy of the VMD-SVM method proposed in this paper is improved by 6.5%, 5.63% and 10.39% compared to the time-frequency (TF) feature SVM, empirical modal decomposition (EMD) feature SVM and wavelet (DWT) feature SVM, methods, respectively. In addition, feature sensitivities are analyzed to reduce model complexity while ensuring accuracy. [ABSTRACT FROM AUTHOR]

Water supply pipeline leakage is a major issue around the world. Leak detection and remediation can prevent water scarcity and some other problems. As a result, investigating pipeline leak-detecting technology has a high practical value. This study employs a promising technique for detecting pipeline leaks using Acoustic Emission (AE) signals. A dytran acceleration sensor was used to collect leakage signals in the time domain. The time-domain signal is transformed into a frequency domain by employing Fast Fourier Transform (FFT). The produced frequency signal has many dimensions which can be reduced to 17 by Principal Component Analysis (PCA). Intelligent leakage diagnosis techniques should eliminate time, and human intervention, and increase effectiveness. Machine learning (ML) models come into play at this point. To detect leakage, the hybrid ML model is proposed and it is compared with the conventional ML models. The best model for detecting water leakage is identified using the classification metrics. [ABSTRACT FROM AUTHOR]

Purpose: Lubricating oil leakage is a common issue in thermal power plant operation sites, requiring prompt equipment maintenance. The real-time detection of leakage occurrences could avoid disruptive consequences caused by the lack of timely maintenance. Currently, inspection operations are mostly carried out manually, resulting in time-consuming processes prone to health and safety hazards. To overcome such issues, this paper proposes a machine vision-based inspection system aimed at automating the oil leakage detection for improving the maintenance procedures. Design/methodology/approach: The approach aims at developing a novel modular-structured automatic inspection system. The image acquisition module collects digital images along a predefined inspection path using a dual-light (i.e. ultraviolet and blue light) illumination system, deploying the fluorescence of the lubricating oil while suppressing unwanted background noise. The image processing module is designed to detect the oil leakage within the digital images minimizing detection errors. A case study is reported to validate the industrial suitability of the proposed inspection system. Findings: On-site experimental results demonstrate the capabilities to complete the automatic inspection procedures of the tested industrial equipment by achieving an oil leakage detection accuracy up to 99.13%. Practical implications: The proposed inspection system can be adopted in industrial context to detect lubricant leakage ensuring the equipment and the operators safety. Originality/value: The proposed inspection system adopts a computer vision approach, which deploys the combination of two separate sources of light, to boost the detection capabilities, enabling the application for a variety of particularly hard-to-inspect industrial contexts. [ABSTRACT FROM AUTHOR]

A pipeline is the most efficient way to transport water from one place to another. Due to aging, corrosion, and external factors, the pipeline is prone to damage, which causes leaks. Many machine learning (ML) and deep learning (DL) methods are available to address this issue. This paper does an experimental study on available methods in ML and DL for leak detection for the collected data using an acousto-optic sensor. The experimental setup comprises of an acousto-optic sensor made of an erbium-doped fiber laser (EDFL), galvanized iron pipeline, a tank, a pump, and a data acquisition unit. The dimensions of the galvanized pipeline looped with the water tank are a length of 40 m, an inner diameter of 89 mm, and an outer diameter of 90 mm. The diameter of the simulated leak aperture is 5 mm. The methods analyzed in this study are k-means, k-medoids, Naive Bayes (NB), support vector machines (SVM), k-nearest neighbor (KNN), decision tree (DT), categorical boosting (CatBoost), random forest (RF), XGBoost, AdaBoost, and one-dimensional convolutional neural network (1DCNN). ML algorithms need a feature extraction technique because the data collected from the experiment is too large and contains redundant information. Feature extraction reduces the data size by extracting essential information. This paper extracts ten features from raw data. Among the ML algorithms, AdaBoost gives the highest prediction accuracy of 98.02%. This paper also implements eight models of 1DCNN, and Model 1 shows the best prediction accuracy of 98.16%, which is the highest compared with all the other classifiers in ML and DL for one-dimensional time series acousto-optic sensor data. [ABSTRACT FROM AUTHOR]

Key Takeaways: Hamilton, Ont., has an older, elaborate water distribution system, with high levels of nonrevenue water (NRW). The city was determined to reduce its NRW, so it conducted a leak detection pilot study to explore advanced technologies that could improve detection efforts. With a 95.6% accuracy rate from the pilot test results, the city implemented a full‐scale, proactive leak detection program. [ABSTRACT FROM AUTHOR]

This paper deals with the state fault detection scheme for distribution flow networks subject to continuously varying conditions at boundaries. A robust PDE detection observer for transport flow systems is designed. Directly built on the nonlinear hyperbolic systems of balance laws model with anti-collocated setup, the PDE observer based on backstepping theory provide the on-line estimation of signals that are not measured. The stability of the error equation is proved. The estimation and the observability time are used for fault detection; an adaptive threshold is defined for the purpose. The performances of the observer and the fault detection method are validated on actual flow data collected from a real water distribution system (WDS) for leakage detection. The leak detection time corresponds to the first alarm activation, confirms the effectiveness of proposed approach. [Display omitted] • Robust persistent state fault detection PDE-based observer for transport flow systems is built. • The nonlinearity derives from a class of hyperbolic systems of balance laws is kept. • Backstepping boundary observer with anti-collocated setup is designed. • The online estimation of unmeasured signals and the observability time are used for fault detection. • An adaptive threshold based on disturbances bound, performance index and input signal is set. • Data are collected from a realwater distribution network. • The performances of the observer and the fault detection scheme are validated on actual flow data. [ABSTRACT FROM AUTHOR]

Two deep learning algorithms, namely, Convolutional Neural Network (CNN) and Recurrent Neural Network- Long Short Term Memory (LSTM), were used to classify the water distribution networks (WDN) as leaky or non-leaky. LeakDB dataset was employed to generate different leakage scenarios for Net 1 and Hanoi benchmark WDN. Three cases, (a) incipient leaks, (b) abrupt leaks, and (c) mixed leak situations, are employed for pressure and flow conditions. A total of 1000 scenarios have been employed, 80% for training and 20% for testing. Seven metrics for analyzing the performance of CNN and LSTM are training accuracy, testing accuracy, total accuracy, true positive rate, false positive rate, false negative rate & area under curve. The results obtained are compared with those of Kammoun, etal. (2021). CNN is performing slightly better than LSTM in several metrics for most scenarios. However, both CNN and LSTM performed most of the time with better accuracy than those used by Kammoun et al. (2021). Leak detection accuracy is in the range of 90.56–98.23 % for Net1 WDN, whereas it is 49–96.55 % for Hanoi WDN. [ABSTRACT FROM AUTHOR]

Ensuring early leak detection and implementing effective secondary containment systems are critical for preventing the dispersion of hazardous liquids and minimizing the casualties of a chemical accident. This study reviews the standards for leak detectors and their placement in various regions, as well as the key parameters involved in bund design such as bund capacity, bund wall height, and separation distance from the bund wall. In this study, Computational Fluid Dynamics (CFD) simulations are conducted considering storage tanks with heights (H) of 1.75 m, 3.5 m, and 7.0 m. The detection times using a level transmitter (LT) and a leak sensor are compared at eight monitoring points. Exclusively employing the leak sensor led to a significant detection time delay of up to 16 times, ranging from 0.5 s to 8.1 s depending on the placement of the leak sensors. However, the average rate of liquid level change was 3.0 mm/s, which demonstrates that the LT consistently detects leaks faster than the leak sensor at all monitoring points. Hence, the integration of an LT alongside a leak detector offers a valuable approach to expedite leak detection, regardless of the direction or location of the leak. Furthermore, the optimized separation distance between the storage tank and the bund wall is suggested. The analytical solution and numerical solution for a separation distance matches up to 95% for H = 1.75 m. The separation distance increases as both the tank height and the height of the leak source increase. The relationship between the separation distance and the tank height exhibits a square root dependence on the liquid level from the leak point. The proposed detection method and optimized separation distance hold the potential to facilitate a revision of liquid storage and handling standards grounded in robust scientific and quantitative evidence. [ABSTRACT FROM AUTHOR]

Accurate hydrogen leakage detection is a major requirement for the safe and widespread integration of this fuel in modern energy production devices, such as fuel cells. Quasi-1D nanowires of seven different metal oxides (CuO, WO3, Nb-added WO3, SnO2, ZnO, α-Bi2O3, NiO) were integrated into a conductometric sensor array to evaluate the hydrogen-sensing performances in the presence of interfering gaseous compounds, namely carbon monoxide, nitrogen dioxide, methane, acetone, and ethanol, at different operating temperatures (200–400 °C). Principal component analysis (PCA) was applied to data extracted from the array, demonstrating the ability to discriminate hydrogen over other interferent compounds. Moreover, a reduced array formed by only five sensors is proposed. This compact array may be easily implementable into artificial olfaction systems used in real hydrogen detection applications. [ABSTRACT FROM AUTHOR]

Water scarcity as well as social and economic damages caused by the increasing amounts of non-revenue water in the water distribution networks (WDNs) have been prompting innovative solutions. A great deal of potable water is wasted due to leakage in the WDNs all over the world. Hence, various leak detection approaches have been explored, including the promising application of acoustic devices. Exploiting the benefits of technological advances in acoustic devices, signal processing, and machine learning (ML), this study aimed to develop a sophisticated system for leak detection in WDNs. Different from laboratory-based studies, this study was conducted on real WDNs in Hong Kong and lasted for about two years. Utilizing acoustic emissions acquired using wireless noise loggers, various ML algorithms were explored to develop inspection models for in-service and buried WDNs. ML classification algorithms can identify patterns in the acquired signals for leak and no-leak statuses. Thus, a combination of features describing acoustic signals in time and frequency domains was utilized to facilitate the development of ML models. Separately for metal and non-metal WDNs, ten well-known ML algorithms were used to develop leak detection models. The validation results demonstrate the promising application of noise loggers and ML for leak detection in real WDNs. Support Vector Machine (SVM), Artificial Neural Network (ANN), and Deep Learning (DL) leak detection models demonstrated a largely stable performance and a very good accuracy, particularly for new unlabelled cases. [ABSTRACT FROM AUTHOR]

Aiming at the problem of insufficient label data in the pipeline leak detection field, this paper proposes a pseudolabel (PL) adaptive learning method based on multiscale convolutional neural network (MSCNN) with the idea of transfer learning for pipeline leak aperture identification. First, the convolutional and pooling layers for transfer learning feature extraction are improved by using a dual-channel MSCNN. Second, the KL divergence function after dimensionality reduction is used to calculate the distribution distance between the source domain and the target domain to improve the robustness of distribution alignment in high-noise environments. In addition, considering the interference of PL noise, this paper develops a pseudolabel (PL) dynamic threshold to achieve the purpose of PL adaptive updating. Compared with the fixed threshold, the improved PL learning (PLL) can effectively improve the prediction accuracy of the model. The effectiveness of the method proposed in this paper is verified by predicting pipeline leakage conditions at different distances and under different pressures. The comparative analysis results show that the method in this paper is superior to other transfer learning methods in terms of prediction accuracy, stability, and convergence speed. [ABSTRACT FROM AUTHOR]

A Pennsylvania water utility was facing nonrevenue water loss of 20%. By implementing a remote acoustic monitoring system, the utility significantly decreased water loss, reduced costs and employee overtime, and improved customer service. [ABSTRACT FROM AUTHOR]

Pipe leakage in water distribution networks (WDNs) has been an emerging concern for water utilities worldwide due to its public health and economic significance. Not only does it cause significant water losses, but it also deteriorates the quality of the treated water in WDNs. Hence, a prompt response is required to avoid or minimize the eventual consequences. This raises the necessity of exploring the possible approaches for detecting and locating leaks in WDNs promptly. Currently, various leak detection methods exist, but they are not accurate and reliable in detecting leaks. This paper presents a novel GIS-based spatial machine learning technique that utilizes currently installed pressure, flow, and water quality monitoring sensors in WDNs, specifically employing the Geographically Weighted Regression (GWR) and Local Outlier Factor (LOF) models, based on a WDN dataset provided by our partner utility authority. In addition to its ability as a regression model for predicting a dependent variable based on input variables, GWR was selected to help identify locations on the WDN where coefficients deviate the most from the overall coefficients. To corroborate the GWR results, the Local Outlier Factor (LOF) is used as an unsupervised machine learning model to predict leak locations based on spatial local density, where locality is given by k-nearest neighbours. The sample WDN dataset provided by our utility partner was split into 70:30 for training and testing of the GWR model. The GWR model was able to predict leaks (detection and location) with a coefficient of determination (R2) of 0.909. The LOF model was able to predict the leaks with a matching of 80% with the GWR results. Then, a customized GIS interface was developed to automate the detection process in real-time as the sensor’s readings were recorded and spatial machine learning was used to process the readings. The results obtained demonstrate the ability of the proposed method to robustly detect and locate leaks in WDNs.

Aiming at the complex characteristics of negative pressure waves in low-pressure pipelines inside of buildings, we proposed an estimation method of pressure fluctuation trends based on the robust Kalman filter and the improved VMD, which can be used for leakage detection. The reconstructed baseline signal can accurately describe the fluctuation trend of the negative pressure wave after the pressure drop, and quantitatively express the characteristic difference between the leakage condition and the gas usage condition. The robust Kalman filter was used to estimate the pressure fluctuations. The parameters of VMD were adaptively calculated based on the WAA and discrete scale space. The trend components contained in the IMFs were separated by a reconstruction based on the Fourier series. Based on the simulation signal, the method can accurately restore the trend component contained in the complex pressure signal. Based on the actual signals, the accuracy of small leakage detection is 96.7% and the accuracy of large leakage detection is 73.3%.

There is an extensive need for surface sensors for applications such as tactile sensing for robotics, damage and strain detection for structural health monitoring and leak detection for buried structures. One type of surface sensor is electrical impedance tomography (EIT)-based sensing skins, which use electrically conductive coatings applied on the object’s surface to monitor physical or chemical phenomena on the surface. In this article, we propose a sensing skin with two electrically coupled layers separated by an insulator. Based on electrical measurements, the spatial distribution of the electrical coupling between the layers is estimated. This coupling is sensitive to both the pressure distribution on the surface and water entering between the layers through a leak. We present simulations and experimental studies to evaluate the feasibility of the proposed method for pressure sensing and leak detection. The results support the feasibility of the proposed method for both of these applications.

This paper proposes a leak detection and size identification technique in fluid pipelines based on a new leak-sensitive feature called the vulnerability index (VI) and 1-D convolutional neural network (1D-CNN). The acoustic emission hit (AEH) features can differentiate between normal and leak operating conditions of the pipeline. However, the multiple sources of acoustic emission hits, such as fluid pressure on the joints, interference noises, flange vibrations, and leaks in the pipeline, make the features less sensitive toward leak size identification in the pipeline. To address this issue, acoustic emission hit features are first extracted from the acoustic emission (AE) signal using a sliding window with an adaptive threshold. Since the distribution of the acoustic emission hit features changes according to the pipeline working conditions, a newly developed multiscale Mann–Whitney test (MMU-Test) is applied to the acoustic emission hit features to obtain the new vulnerability index feature, which shows the pipeline's susceptibility to leak and changes according to the pipeline working conditions. Finally, the vulnerability index is provided as input to a 1-D-CNN for leak detection and size identification, whose experimental results show a higher accuracy as compared to the reference state-of-the-art methods under variable fluid pressure conditions.

Effective water resource management is essential in large metropolitan cities. Digital Twins (DT), supported by IIoT and machine learning technologies, provide opportunities for real-time prediction and optimization for effective decision-making in water distribution systems. A framework for the digital twin of the Water Distribution Network (WDN) is developed in this paper to achieve higher operational efficiency using ‘WNTR’, the Python-based library of EPANET. All computational experiments and methods were validated on the benchmark hydraulic C-TOWN network (Ostfeld et al., 2011). The hydraulic parameters and quality parameters of the DT model for the water network were calibrated using the Differential Evolution (DE) algorithm. The calibrated DT served as a real-time proxy to generate simulation data, which is used for two different applications in large-scale water networks: (i) Disinfectant dosage regulation task using booster stations and (ii) pipe leakage localization task. The calibrated DT was utilized to estimate the optimal disinfectant dosing rates, ensuring water quality control within an acceptable range using optimization. The results highlight the effectiveness of the neural network and real-time optimization strategy to achieve the optimal dosing rate. For the leakage localization task, the Graph Convolution Networks (GCN) based neural network trained on the DT was found to predict leakage location very accurately.

Water is an invaluable resource quickly becoming scarce in many parts of the world. Therefore, the importance of efficiency in water supply and distribution has greatly increased. Some of the main tools for limiting losses in supply and distribution networks are leakage sensors that enable real-time monitoring. With fiber optics recently becoming a commodity, along with the sound advances in computing power and its miniaturization, multipurpose sensors relying on these technologies have gradually become common. In this study, we explore the development and testing of a multimode optic-fiber-based pipe monitoring and leakage detector based on statistical and machine learning analyses of speckle patterns captured from the fiber's outlet by a defocused camera. The sensor was placed inside or over a PVC pipe with covered and exposed core configurations, while 2 to 8 mm diameter pipe leaks were simulated under varied water flow and pressure. We found an overall leak size determination accuracy of 75.8% for a 400 µm covered fiber and of 68.3% for a 400 µm exposed fiber and demonstrated that our sensor detected pipe bursts, outside interventions, and shocks. This result was consistent for the sensors fixed inside and outside the pipe with both covered and exposed fibers. [ABSTRACT FROM AUTHOR]

Natural gas (NG) leaks from below-ground pipelines pose safety, economic, and environmental hazards. Despite walking surveys using handheld methane (CH4) detectors to locate leaks, accurately triaging the severity of a leak remains challenging. It is currently unclear whether CH4 detectors used in walking surveys could be used to identify large leaks that require an immediate response. To explore this, we used above-ground downwind CH4 concentration measurements made during controlled emission experiments over a range of environmental conditions. These data were then used as the input to a novel modeling framework, the ESCAPE−1 model, to estimate the below-ground leak rates. Using 10-minute averaged CH4 mixing/meteorological data and filtering out wind speed < 2 m s−1/unstable atmospheric data, the ESCAPE−1 model estimates small leaks (0.2 kg CH4 h−1) and medium leaks (0.8 kg CH4 h−1) with a bias of −85%/+100% and −50%/+64%, respectively. Longer averaging (≥3 h) results in a 55% overestimation for small leaks and a 6% underestimation for medium leaks. These results suggest that as the wind speed increases or the atmosphere becomes more stable, the accuracy and precision of the leak rate calculated by the ESCAPE−1 model decrease. With an uncertainty of ±55%, our results show that CH4 mixing ratios measured using industry-standard detectors could be used to prioritize leak repairs. [ABSTRACT FROM AUTHOR]

Detecting and mapping subsurface utilities in urban areas is crucial for identifying defects or damages in drinking and sewage pipes that can cause leaks. These leaks make it difficult to accurately characterize the pipes due to changes in their reflective properties. This study focused on detecting leaks originating from underground pipes and distinguishing between these various types of pipes. It also aimed to create a visual fingerprint model that displays the reflection characteristics of these pipes during different leak conditions, enabling efficient maintenance and handling procedures on the pipes. To achieve this, a finite-difference time-domain (FDTD) method was used to simulate two types of pipe materials with and without leak areas to construct different scenarios. Additionally, a ground-penetrating radar (GPR) field survey was conducted using a 600 MHz antenna in a part of the El Hammam area on Egypt's northwest coast. The simulated images produced with numerical modeling were compared with the radar profiles obtained using GPR at particular locations. The numerical simulations and radar profiles demonstrated the noticeable influence of water leaks from the different pipes, wherein the reflection of saturated soil waves was interrupted due to the presence of saturated soil. Envelope and migration techniques were employed in a new application to accurately distinguish between different pipe types, specifically focusing on leak areas. The strong correlation between the real radar profile and the specific signal of a water pipe leak in the simulated models suggests that GPR is a reliable non-destructive geophysical method for detecting water pipe leaks and distinguishing between the different pipe materials in various field conditions. The simulated models, which serve as image-matching fingerprints to identify and map water pipe leaks, help us to comprehend reality better. [ABSTRACT FROM AUTHOR]