Water Pipeline Leakage Detection Based on Machine Learning and Wireless Sensor Networks

The detection of water pipeline leakage is important to ensure that water supply networks can operate safely and conserve water resources. To address the lack of intelligent and the low efficiency of conventional leakage detection methods, this paper designs a leakage detection method based on machine learning and wireless sensor networks (WSNs). The system employs wireless sensors installed on pipelines to collect data and utilizes the 4G network to perform remote data transmission. A leakage triggered networking method is proposed to reduce the wireless sensor network’s energy consumption and prolong the system life cycle effectively. To enhance the precision and intelligence of leakage detection, we propose a leakage identification method that employs the intrinsic mode function, approximate entropy, and principal component analysis to construct a signal feature set and that uses a support vector machine (SVM) as a classifier to perform leakage detection. Simulation analysis and experimental results indicate that the proposed leakage identification method can effectively identify the water pipeline leakage and has lower energy consumption than the networking methods used in conventional wireless sensor networks

Marine baseline and monitoring strategies for Carbon Dioxide Capture and Storage (CCS)

The QICS controlled release experiment demonstrates that leaks of carbon dioxide (CO2) gas can be detected by monitoring acoustic, geochemical and biological parameters within a given marine system. However the natural complexity and variability of marine system responses to (artificial) leakage strongly suggests that there are no absolute indicators of leakage or impact that can unequivocally and universally be used for all potential future storage sites. We suggest a multivariate, hierarchical approach to monitoring, escalating from anomaly detection to attribution, quantification and then impact assessment, as required. Given the spatial heterogeneity of many marine ecosystems it is essential that environmental monitoring programmes are supported by a temporally (tidal, seasonal and annual) and spatially resolved baseline of data from which changes can be accurately identified. In this paper we outline and discuss the options for monitoring methodologies and identify the components of an appropriate baseline survey

Localization Techniques for Water Pipeline Leakages: A Review

Pipeline leakages in water distribution network (WDN) is one of the prominent issues that has gain an interest among researchers in the past few years. Time and accuracy play an important role in leak localization as it has huge impact to the human population and economic point of view. The complexity of WDN has prompt numerous techniques and methods been introduced focusing on the accuracy and efficacy. In general, localization techniques can be divided into two broad categories; external and internal systems. This paper reviews some of the techniques that has been explored and proposed including the limitations of each techniques. Â

Development of the Next Generation of Water Distribution Network Modelling Tools Using Inverse Methods

The application of optimisation to Water Distribution Network (WDN) Modelling involves the use of computer-based techniques to many different problems, such as leakage detection and localisation. The success in the application of any model-based methodology for finding leaks highly depends on the availability of a well-calibrated model. Both leak detection and localisation, as well as model calibration are procedures that constitute the field of inverse problems in WDN modelling. The procedures are interlinked and dependent as when a leak is found and the model is updated its quality improves, while when a model is calibrated its ability to detect and localise leaks also improves. This is because both inverse problems are solved with the aim to mimic the behaviour of the real system as closely as possible using field measurements. In this research, both inverse problems are formulated as constrained optimisation problems. Evolutionary Optimisation techniques, of which Genetic Algorithms are the best-known examples, are search methods that are increasingly applied in WDN modelling with the aim to improve the quality of a solution for a given problem. This, ultimately, aids practitioners in these facets of management and operation of WDNs. Evolutionary Optimisation employs processes that mimic the biological process of natural selection and “survival of the fittest” in an artificial framework. Based on this philosophy a population of individual solutions to the problem is manipulated and, over time, “evolves” towards optimal solutions. However, such algorithms are characterised by large numbers of function evaluations. This, coupled with the computational complexity associated with the hydraulic simulation of WDNs incurs significant computational burden, can limit the applicability and scalability of this technology across the Water Industry. In addition, the inverse problem is often “ill-posed”. In practice, the ill-posed condition is typically manifested by the non-uniqueness of the problem solution and it is usually a consequence of inadequate quantity and/or quality of field observations. Accordingly, this thesis presents a methodology for applying Genetic Algorithms to solve leakage related inverse problems in WDN Modelling. A number of new procedures are presented for improving the performance of such algorithms when applied to the complex inverse problems of leak detection and localisation, as well as model calibration. A novel reformulation of the inverse problem is developed as part of a decision support framework that minimizes the impact of the inherent computational complexity and dimensionality of these problems. A search space reduction technique is proposed, i.e., a reduction in the number of possible solution combinations to the inverse problem, to improve its condition considering the accuracy of the available measurements. Eventually, this corresponds to a targeted starting point for initiating the search process and therefore more robust stochastic optimisations. The ultimate purpose is to increase the reliability of the WDN hydraulic model in localising leaks in real District Metered Areas, i.e., to reduce the number false positives. In addition, to speed up the leak search process (both computationally and physically) and, improve the overall model accuracy. A calibrated model of the WDN is not always available for supporting work at distribution mains level. Consequently, two separate problem-specific methods are proposed to meet the abovementioned purpose: (a) a Leak Inspection Method used for the detection and localisation of leaks and; (b) a Calibration Method for producing an accurate average day model that is fit for the purpose of leak detection and localisation. Both methods integrate a three-step Search Space Reduction stage, which is implemented before solving the inverse problem. The aim is to minimize the number of decision variables and the range of possible values, while trying to preserve the optimum solution, i.e., reduce the inverse problem dimensionality. The search space reduction technique is established to generate a reduced set of highly sensitive decision variables. Eventually this is done to provide a viable, scalable technique for accelerating evolutionary optimisation applications in inverse problems being worthwhile on both academic and practical grounds. The novel methodologies presented here for leak detection and localisation, as well as for model calibration are verified successfully on four case studies. The case studies include two real WDN examples with artificially generated data, which investigate the limits of each method separately. The other two case studies implement both methods on real District Metered Areas in the United Kingdom, firstly to calibrate the hydraulic network model and, then, to detect and localise a single leak event that has actually happened. The research results suggest that leaks and unknown closed or open throttle valves that cause a hydraulic impact larger than the sensor data error can be detected and localised with the proposed framework which solves the inverse problem after search space reduction. Moreover, the quality of solutions can dramatically improve for given runtime of the algorithm, as 99.99% of infeasible solution combinations are removed, compared to the case where no search space reduction is performed. The outcomes of the real cases show that the presented search space reduction technique can reduce the search area for finding the leak to within 10% of the WDN (by length). The framework can also contribute to more timely detection and localisation of leakage hotspots, thus reducing economic and environmental impacts. The optimisation model for predicting leakage hotspots can be effective despite the recognized challenges of model calibration and the physical measurement limitations from the pressure and flow field tests

Internet of Things Based Monitoring System of Leaks in Water Supply Networks Using Pressure-Based Model

Leaks in water distribution networks impose several impacts on economy, freshwater resources, water quality, health and safety. Fast leak detection and reparation is a key for lowering its negative impacts and associated costs with conventional detection techniques. This study has been used a pressure-based model to detect leaks events and its coordinates based on pressure and flow measurements. Pressure and flow data for systems that having leaks in their structure were analyzed and compared with data generated from non-leaking systems using EPANET software packages. An extension package of EPANET software (EpanetWaterGen) has been used as it has the advantage of its ability to better simulate leaks. The results show the ability of the model to detect leaks in a small and large water distribution networks with uncertainty level associated with low pressure change. The developed leak detection model utilizes pressure and flow sensors and enables the network managers and administrators to optimally place the sensors in a manner to increase efficiency and optimize cost. The system allows operators to detect leak location and volume of lost water, thus enabling a better and more efficient response to leaks, such that the network managers can address and respond to most urgent leaks and optimize the time end efforts of technical and maintenance personnel

Multiple Surface Pipeline Leak Detection Using Real-Time Sensor Data Analysis

Pipelines enable the largest volume of both intra and international transportation of oil and gas and play critical roles in the energy sufficiency of countries. The biggest drawback with the use of pipelines for oil and gas transportation is the problem of oil spills whenever the pipelines lose containment. The severity of the oil spill on the environment is a function of the volume of the spill and this is a function of the time taken to detect the leak and contain the spill from the pipeline. A single leak on the Enbridge pipeline spilled 3.3 million liters into the Kalamazoo river while a pipeline rupture in North Dakota which went undetected for 143 days spilled 29 million gallons into the environment.Several leak detection systems (LDS) have been developed with the capacity for rapid detection and localization of pipeline leaks, but the characteristics of these LDS limit their leak detection capability. Machine learning provides an opportunity to develop faster LDS, but it requires access to pipeline leak datasets that are proprietary in nature and not readily available. Current LDS have difficulty in detecting low-volume/low-pressure spills located far away from the inlet and outlet pressure sensors. Some reasons for this include the following, leak induced pressure variation generated by these leaks is dissipated before it gets to the inlet and outlet pressure sensors, another reason is that the LDS are designed for specific minimum detection levels which is a percentage of the flow volume of the pipeline, so when the leak falls below the LDS minimum detection value, the leak will not be detected. Perturbations generated by small volume leaks are often within the threshold values of the pipeline\u27s normal operational envelop as such the LDS disregards these perturbations. These challenges have been responsible for pipeline leaks going on for weeks only to be detected by third-party persons in the vicinity of the leaks. This research has been able to develop a framework for the generation of pipeline datasets using the PIPESIM software and the RAND function in Python. The topological data of the pipeline right of way, the pipeline network design specification, and the fluid flow properties are the required information for this framework. With this information, leaks can be simulated at any point on the pipeline and the datasets generated. This framework will facilitate the generation of the One-class dataset for the pipeline which can be used for the development of LDS using machine learning. The research also developed a leak detection topology for detecting low-volume leaks. This topology comprises of the installation of a pressure sensor with remote data transmission capacity at the midpoint of the line. The sensor utilizes the exception-based transmission scheme where it only transmits when the new data differs from the existing data value. This will extend the battery life of the sensor. The installation of the sensor at the midpoint of the line was found to increase the sensitivity of the LDS to leak-induced pressure variations which were traditionally dissipated before getting to the Inlet/outlet sensors. The research also proposed the development of a Leak Detection as a Service (LDaaS) platform where the pressure data from the inlet and the midpoint sensors are collated and subjected to a specially developed leak detection algorithm for the detection of pipeline leaks. This leak detection topology will enable operators to detect low-volume/low-pressure leaks that would have been missed by the existing leak detection system and deploy the oil spill response plans quicker thus reducing the volume of oil spilled into the environment. It will also provide a platform for regulators to monitor the leak alerts as they are generated and enable them to evaluate the oil spill response plans of the operators

Internet of Things Based Monitoring System of Leaks in Water Supply Networks Using Pressure-Based Model

Leaks in water distribution networks impose several impacts on economy, freshwater resources, water quality, health and safety. Fast leak detection and reparation is a key for lowering its negative impacts and associated costs with conventional detection techniques. This study has been used a pressure-based model to detect leaks events and its coordinates based on pressure and flow measurements. Pressure and flow data for systems that having leaks in their structure were analyzed and compared with data generated from non-leaking systems using EPANET software packages. An extension package of EPANET software (EpanetWaterGen) has been used as it has the advantage of its ability to better simulate leaks. The results show the ability of the model to detect leaks in a small and large water distribution networks with uncertainty level associated with low pressure change. The developed leak detection model utilizes pressure and flow sensors and enables the network managers and administrators to optimally place the sensors in a manner to increase efficiency and optimize cost. The system allows operators to detect leak location and volume of lost water, thus enabling a better and more efficient response to leaks, such that the network managers can address and respond to most urgent leaks and optimize the time end efforts of technical and maintenance personnel