Search Results (35)

Accurate localization of buried water pipelines in rural areas is crucial for maintenance and leak management but is often hindered by outdated maps and the limitations of traditional geophysical methods. This study aimed to develop and validate a multi-source remote-sensing workflow, integrating UAV (unmanned aerial vehicle)-borne near-infrared (NIR) surveys, multi-temporal Sentinel-2 imagery, and historical Google Earth orthophotos to precisely map pipeline locations and establish a surface baseline for future monitoring. Each dataset was processed within a unified least-squares framework to delineate pipeline axes from surface anomalies (vegetation stress, soil discoloration, and proxies) and rigorously quantify positional uncertainty, with findings validated against RTK-GNSS (Real-Time Kinematic—Global Navigation Satellite System) surveys of an excavated trench. The combined approach yielded sub-meter accuracy (±0.3 m) with UAV data, meter-scale precision (≈±1 m) with Google Earth, and precision up to several meters (±13.0 m) with Sentinel-2, significantly improving upon inaccurate legacy maps (up to a 300 m divergence) and successfully guiding excavation to locate a pipeline segment. The methodology demonstrated seasonal variability in detection capabilities, with optimal UAV-based identification occurring during early-vegetation growth phases (NDVI, Normalized Difference Vegetation Index ≈ 0.30–0.45) and post-harvest periods. A Sentinel-2 analysis of 221 cloud-free scenes revealed persistent soil discoloration patterns spanning 15–30 m in width, while Google Earth historical imagery provided crucial bridging data with intermediate spatial and temporal resolution. Ground-truth validation confirmed the pipeline location within 0.4 m of the Google Earth-derived position. This integrated, cost-effective workflow provides a transferable methodology for enhanced pipeline mapping and establishes a vital baseline of surface signatures, enabling more effective future monitoring and proactive maintenance to detect leaks or structural failures. This methodology is particularly valuable for water utility companies, municipal infrastructure managers, consulting engineers specializing in buried utilities, and remote-sensing practitioners working in pipeline detection and monitoring applications. Full article

The importance of maintenance activities for improving the quality of water sources and guaranteeing a steady supply of water has increased significantly because of current social concerns. Water supply pipe corrosion is an issue that can cause leaks and lower water quality. The identification of hydraulic anomalies in water pumping systems is the subject of this project. A database was created of data acquired from a water supply network with pipes of various lengths and sizes. In hydraulic systems, sensor meters are mounted at various sites with distinct physical features, pipe sizes, and vital supply points. The input parameters used for a model are the sensor parameters, and the model analyzes the correlation between the input parameters (sensors) and determines which parameters are the most important, deciding on the output of the model, and thereby building the simplest model, which requires the least input parameters and gives the most accurate prediction results. In this project, using on the input signal from the sensors, the k-nearest neighbors machine learning algorithm was used to correlate/predict whether the pump was shut down (broken) for a certain period of time. Full article

Oil pollution in water bodies is a substantial environmental concern that poses severe risks to human health, aquatic ecosystems, and economic activities. Rising energy consumption and industrial activity have resulted in more oil spills, damaging long-term ecology. The aim of the review is to discuss problems, effects, and methods of monitoring and sensing oil pollution in water. Oil can destroy the aquatic habitat. Once oil gets into aquatic habitats, it changes both physically and chemically, depending on temperature, wind, and wave currents. If not promptly addressed, these processes have severe repercussions on the spread, persistence, and toxicity of oil. Effective monitoring and early identification of oil pollution are vital to limit environmental harm and permit timely reaction and cleanup activities. Three main categories define the three main methodologies of oil spill detection. Remote sensing utilizes satellite imaging and airborne surveillance to monitor large-scale oil spills and trace their migration across aquatic bodies. Accurate real-time detection is made possible by optical sensing, which uses fluorescence and infrared methods to identify and measure oil contamination based on its particular optical characteristics. Using sensor networks and Internet of Things (IoT) technologies, wireless sensing improves early detection and response capacity by the continuous automated monitoring of oil pollution in aquatic settings. In addition, the effectiveness of advanced artificial intelligence (AI) techniques, such as deep learning (DL) and machine learning (ML), in enhancing detection accuracy, predicting leak patterns, and optimizing response strategies, is investigated. This review assesses the advantages and limits of these detection technologies and offers future research directions to advance oil spill monitoring. The results help create more sustainable and efficient plans for controlling oil pollution and safeguarding aquatic habitats. Full article

The water supply pipeline system is responsible for providing clean drinking water to residents, but pipeline leaks can lead to water resource wastage, increased operational costs, and safety hazards. To effectively detect the leakage level in the water supply pipelines and address the difficulty of accurately distinguishing fine-grained leakage levels using traditional methods, this paper proposes a fine-grained leakage identification method based on Convolutional Neural Networks (CNN) and the Selective State Space Model (Mamba). An experimental platform was built to simulate different leakage conditions, and multi-axis sensors were used to collect data, resulting in the creation of a high-quality dataset. The signals were converted into frequency-domain images using Short-Time Fourier Transform (STFT), and CNN was employed to extract image features. Mamba was integrated to capture the one-dimensional time dynamic characteristics of the leakage signal, and the CosFace loss function was introduced to increase the inter-class distance, thereby improving the fine-grained classification ability. Experimental results show that the proposed method achieves optimal performance across various evaluation metrics. Compared to SVM, BP neural networks, and CNN methods, the accuracy was improved by 17.9%, 15.9%, and 3.0%, respectively. Compared to Support Vector Machine (SVM), Backpropagation neural network (BP), attention mechanism with the LSTM network (LSTM-AM), CNN, and inverted transformers network (iTransformer) methods, the accuracy improved by 17.9%, 15.9%, 7.8%, 3.0%, and 2.3%, respectively. Additionally, the method enhanced intra-class consistency and increased inter-class differences, showing outstanding performance at different leakage levels, which could contribute to improved intelligent management for water pipeline leakage detection. Full article

Low-density lipoprotein (LDL) chemically modified by reactive oxygen species (ROS), for example, leaking from red blood cells in the vascular compartment, more readily crosses the vascular endothelium than does nonoxidatively modified LDL to enter tissue fluid. Oxidatively modified LDL (oxLDL) may also be created in the tissue fluid by ROS leaking from cells by design, for example, by inflammatory white cells, or simply leaking from other cells as a consequence of oxygen metabolism. As well as oxLDL, glycatively modified LDL (glycLDL) is formed in the circulation. High-density lipoprotein (HDL) appears capable of decreasing the burden of lipid peroxides formed on LDL exposed to ROS or to glucose and its metabolites. The mechanism for this that has received the most attention is the antioxidant activity of HDL, which is due in large part to the presence of paraoxonase 1 (PON1). PON1 is intimately associated with its apolipoprotein A1 component and with HDL’s lipid domains into which lipid peroxides from LDL or cell membranes can be transferred. It is frequently overlooked that for PON1 to hydrolyze lipid substrates, it is essential that it remain by virtue of its hydrophobic amino acid sequences within a lipid micellar environment, for example, during its isolation from serum or genetically modified cells in tissue culture. Otherwise, it may retain its capacity to hydrolyze water-soluble substrates, such as phenyl acetate, whilst failing to hydrolyze more lipid-soluble molecules. OxLDL and probably glycLDL, once they have crossed the arterial endothelium by receptor-mediated transcytosis, are rapidly taken up by monocytes in a process that also involves scavenger receptors, leading to subendothelial foam cell formation. These are the precursors of atheroma, inducing more monocytes to cross the endothelium into the lesion and the proliferation and migration of myocytes present in the arterial wall into the developing lesion, where they transform into foam cells and fibroblasts. The atheroma progresses to have a central extracellular lake of cholesteryl ester following necrosis and apoptosis of foam cells with an overlying fibrous cap whilst continuing to grow concentrically around the arterial wall by a process involving oxLDL and glycLDL. Within the arterial wall, additional oxLDL is generated by ROS secreted by inflammatory cells and leakage from cells generally when couplet oxygen is reduced. PON1 is important for the mechanism by which HDL opposes atherogenesis, which may provide a better avenue of inquiry in the identification of vulnerable individuals and the provision of new therapies than have emerged from the emphasis placed on its role in RCT. Full article

To improve the accuracy of leak identification and location of water supply pipelines, a novel convolution gated recurrent unit method based on the attention mechanism is proposed in this paper. Firstly, a convolutional neural network is used to capture the localspatio-temporal characteristics of the signal. Secondly, a gated recurrent unit is used to extract the signal’s long dependence relationship. Finally, an attention mechanism is combined to highlight the influence of key features in the learning process, so as to achieve accurate recognition of the pipeline pressure state. The accurate identification of leakage faults is expected to further improve the location accuracy of pipeline leakage points, which is very important for the practical application of the algorithm in engineering. In order to verify the effectiveness of the proposed method, a simulated leakage test platform is set up for the leakage simulation test. The test results of different leakage conditions show that the recognition accuracy of the proposed network structure is 98.75% for test samples, which is higher than other network structures of the same type. According to the identification results of leakage characteristics, the VMD method is used to extract the high-frequency components of the negative pressure wave signal, so as to obtain the inflection point of the negative pressure wave, so as to determine the arrival time difference of the signal, and the arrival time method based on the negative pressure wave is used to locate the leakage point. Across 12 leak locations, the maximum relative error is 7.67%, the minimum relative error is 0.86%, and the average relative error is only 2.97%, achieving the best performance among the various methods. The positioning accuracy meets the requirement of practical application and the algorithm has good robustness. Full article

Water distribution networks (WDNs), which are responsible for delivering water of adequate quantity and quality, are vulnerable to threats such as leaks, pipe breaks, and contaminant intrusions. Hence, it is important to identify critical network elements to develop more assertive maintenance strategies for water systems. This paper aims to perform a risk assessment on leaks and pipe breaks to support the identification of critical elements in water supply systems. To this end, complex network theory (CNT) is applied as an alternative to conventional approaches that rely on multiple hydraulic simulations. Metrics such as robustness, redundancy, centrality, and connectivity are used to analyze graphs representing WDNs. Failures are modeled using hydraulic simulations to evaluate their impact on parameters such as pressure and flow. CNT metrics are then applied, including shortest path calculations between water sources and demand vertices to assess pipe importance, and vertex centrality metrics to evaluate node influence on the network. The results of the hydraulic simulations are compared with the outcomes of CNT-based analyses. Multi-criteria analysis is then employed to determine the asset maintenance priority, considering multiple failures and the associated impacts on the system. The results highlight a novel approach that shifts the focus from hydraulic state-based assessments to topology-driven analysis, reducing the influence of uncertainties inherent in water distribution network models. Full article

In today’s expanding cities, pipeline networks are becoming an essential part of the industrial infrastructure. Monitoring these pipelines autonomously is becoming increasingly important. Inspecting pipelines for cracks is one specific task that poses a huge burden on humans. Undetected cracks may pose multi-dimensional risks. In this paper, we introduce the Pipeline Leak Identification Emergency Robot Swarm (PLIERS) system, an industrial system that deploys Internet-of-Things (IoT), robotics, and neural network technologies to detect cracks in emptied water and sewage pipelines. In PLIERS, a swarm of robots inspect emptied pipelines from the inside to detect cracks, collect images of them, and register their locations. When the images are taken, they are fed into a cloud-based module for analysis by a convolutional neural network (CNN). The CNN is used to detect cracks and identify their severity. Through extensive training and testing, the CNN model performance showed promising scores for accuracy (between 80% and 90%), recall (at least 95%), precision (at least 95%), and F1 (at least 96%). Additionally, through the careful design of a prototype for a water/sewage pipeline structure with several types of cracks, the robots used managed to exchange information among themselves and convey crack images to the cloud-based server for further analysis. PLIERS is a system that deploys modern technologies to detect and recognize cracks in pipeline grids. It adds to the efforts of improving instrumentation and measurement approaches by using robots, sensory, IoT principles, and the efficient analysis of CNNs. Full article

Pipeline leakage, which leads to water wastage, financial losses, and contamination, is a significant challenge in urban water supply networks. Leak detection and prediction is urgent to secure the safety of the water supply system. Relaying on deep learning artificial neural networks and a specific optimization algorithm, an intelligential detection approach in identifying the pipeline leaks is proposed. A hydraulic model is initially constructed on the simplified Net2 benchmark pipe network. The District Metering Area (DMA) algorithm and the Cuckoo Search (CS) algorithm are integrated as the DMA-CS algorithm, which is employed for the hydraulic model optimization. Attributing to the suspected leak area identification and the exact leak location, the DMA-CS algorithm possess higher accuracy for pipeline leakage (97.43%) than that of the DMA algorithm (92.67%). The identification pattern of leakage nodes is correlated to the maximum number of leakage points set with the participation of the DMA-CS algorithm, which provide a more accurate pathway for identifying and predicting the specific pipeline leaks. Full article

This paper promotes water distribution networks’ (WDNs) sustainability and efficiency by integrating intelligent data analysis with ground-penetrating radar (GPR) to better interpret GPR images for detecting water leaks, favouring their asset assessment. This work uses GPR data from a laboratory setting to investigates the effects of various parameters on image interpretability across pipes. This methodology aims to advance the automation of leak and pipe identification, improving data interpretation and reducing dependency on human experts for leakage detection purposes. The findings suggest the possibility of uncovering new features enhancing GPR image interpretability, presented in 3D models. Full article

A This paper proposes a modified real-time transient modelling (MRTTM) framework to address the critical challenge of leak detection and localization in pipeline transmission systems. Pipelines are essential infrastructure for transporting liquids and gases, but they are susceptible to leaks, with severe environmental and economic impacts. MRTTM tackles this challenge with a three-stage operational process. First, “Data Collection” gathers sensor data from designated observation points. Second, the “Detection” stage identifies leaks. Finally, “Decision-Making” utilizes MRTTM to pinpoint the exact leak magnitude and location. This paper introduces an innovative method designed to significantly enhance pipeline leak detection and localization through the application of artificial intelligence and advanced signal processing techniques. The improved MRTTM framework integrates AI for pattern recognition, state space modelling for leak segment identification, and an extended Kalman filter (EKF) for precise leak location estimation, addressing the limitations of traditional methods. This paper showcases the application of MRTTM through a case study using the K-nearest neighbors (KNN) method on a water transmission pipeline for leak detection. KNN aids in classifying leak patterns and identifying the most likely leak location. Additionally, MRTTM incorporates the EKF, enabling real-time updates during transient events for faster leak identification. Preprocessing sensor data before comparison with the leakage pattern bank (LPB) minimizes false alarms and enhances detection reliability. Overall, the AI-powered MRTTM framework offers a powerful solution for swift and precise leak detection and localization in pipeline systems. The functionality of the framework is examined, and the results effectively approve the effectiveness of this methodology. The experimental results validate the practical utility of the MRTTM framework in real-world applications, demonstrating up to 90% detection accuracy and an F1 score of 0.92. Full article

With the development of the marine oil industry, leakage accidents are one of the most serious problems threatening maritime and national security. The spilt crude oil can float and sink in the water column, posing a serious long-term threat to the marine environment. High-frequency sonar detection is currently the most efficient method for identifying sunken oil. However, due to the complicated environment of the deep seabed and the interference of the sunken oil signals with homogeneous information, sonar detection data are usually difficult to interpret, resulting in low efficiency and a high failure rate. Previous works have focused on features designed by experts according to the detection environments and the identification of sunken oil targets regardless of the feature extraction step. To automatically identify sunken oil targets without a prior knowledge of the complex seabed conditions during the image acquisition process for sonar detection, a systematic framework is contrived for identifying sunken oil targets that combines image enhancement with a convolutional neural network (CNN) classifier for the final decision on sunken oil targets examined in this work. Case studies are conducted using datasets obtained from a sunken oil release experiment in an outdoor water basin. The experimental results show that (i) the method can effectively distinguish between the sunken oil target, the background, and the interference target; (ii) it achieved an identification accuracy of 83.33%, outperforming feature-based recognition systems, including SVM; and (iii) it provides important information about sunken oil such as the location of the leak, which is useful for decision-making in emergency response to oil spills at sea. This line of research offers a more robust and, above all, more objective option for the difficult task of automatically identifying sunken oils under complex seabed conditions. Full article

Urban water systems worldwide are confronted with the dual challenges of dwindling water resources and deteriorating infrastructure, emphasising the critical need to minimise water losses from leakage. Conventional methods for leak and burst detection often prove inadequate, leading to prolonged leak durations and heightened maintenance costs. This study investigates the efficacy of logic- and machine learning-based approaches in early leak detection and precise location identification within water distribution networks. By integrating hardware and software technologies, including sensor technology, data analysis, and study on the logic-based and machine learning algorithms, innovative solutions are proposed to optimise water distribution efficiency and minimise losses. In this research, we focus on a case study area in the Sunbury region of Victoria, Australia, evaluating a pumping main equipped with Supervisory Control and Data Acquisition (SCADA) sensor technology. We extract hydraulic characteristics from SCADA data and develop logic-based algorithms for leak and burst detection, alongside state-of-the-art machine learning techniques. These methodologies are applied to historical data initially and will be subsequently extended to live data, enabling the real-time detection of leaks and bursts. The findings underscore the complementary nature of logic-based and machine learning approaches. While logic-based algorithms excel in capturing straightforward anomalies based on predefined conditions, they may struggle with complex or evolving patterns. Machine learning algorithms enhance detection by learning from historical data, adapting to changing conditions, and capturing intricate patterns and outliers. The comparative analysis of machine learning models highlights the superiority of the local outlier factor (LOF) in anomaly detection, leading to its selection as the final model. Furthermore, a web-based platform has been developed for leak and burst detection using a selected machine learning model. The success of machine learning models over traditional logic-based approaches underscores the effectiveness of data-driven, probabilistic methods in handling complex data patterns and variations. Leveraging statistical and probabilistic techniques, machine learning models offer adaptability and superior performance in scenarios with intricate or dynamic relationships between variables. The findings demonstrate that the proposed methodology can significantly enhance the early detection of leaks and bursts, thereby minimising water loss and associated economic costs. The implications of this study are profound for the scientific community and stakeholders, as it provides a scalable and efficient solution for water pipeline monitoring. Implementing this approach can lead to more proactive maintenance strategies, ultimately contributing to the sustainability and resilience of urban water infrastructure systems. Full article

Train wheels are crucial components for ensuring the safety of trains. The accurate and fast identification of wheel tread defects is necessary for the timely maintenance of wheels, which is essential for achieving the premise of conditional repair. Image-based detection methods are commonly used for detecting tread defects, but they still have issues with the misdetection of water stains and the leaking of small defects. In this paper, we address the challenges posed by the detection of wheel tread defects by proposing improvements to the YOLOv8 model. Firstly, the impact of water stains on tread defect detection is avoided by optimising the structure of the detection layer. Secondly, an improved SPPCSPC module is introduced to enhance the detection of small targets. Finally, the SIoU loss function is used to accelerate the convergence speed of the network, which ensures defect recognition accuracy with high operational efficiency. Validation was performed on the constructed tread defect dataset. The results demonstrate that the enhanced YOLOv8 model in this paper outperforms the original network and significantly improves the tread defect detection indexes. The average precision, accuracy, and recall reached 96.95%, 96.30%, and 95.31%. Full article

Leakage in water supply pipelines remains a significant challenge. It leads to resource and economic waste. Researchers have developed several leak detection methods, including the use of embedded sensors and pressure prediction. The former approach involves pre-installing detectors inside pipelines to detect leaks. This method allows for the precise localization of leak points. The stability is compromised because of the wireless signal strength. The latter approach, which relies on pressure measurements to predict leak events, does not achieve precise leak point localization. To address these challenges, in this paper, a coherent optical time-domain reflectometry (φ-OTDR) system is employed to capture vibration signal phase information. Subsequently, two pre-trained neural network models based on CNN and Resnet18 are responsible for processing this information to accurately identify vibration events. In an experimental setup simulating water pipelines, phase information from both leaking and non-leaking pipe segments is collected. Using this dataset, classical CNN and ResNet18 models are trained, achieving accuracy rates of 99.7% and 99.5%, respectively. The multi-leakage point experiment results indicate that the Resnet18 model has better generalization compared to the CNN model. The proposed solution enables long-distance water-pipeline precise leak point localization and accurate vibration event identification. Full article