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

Collective thinking approach for improving leak detection systems

Water mains, especially old pipelines, are consistently threatened by the formation of leaks. Leaks inherit increased direct and indirect costs and impacts on various levels such as the economic field and the environmental level. Recently, financially capable municipalities are testing acoustic early detection systems that utilize wireless noise loggers. Noise loggers would be distributed throughout the water network to detect any anomalies in the network. Loggers provide early detection via recording and analyzing acoustic signals within the network. The city of Montreal adopted one of the leak detection projects in this domain and had reported that the main issue that hinders the installed system is false alarms. False alarms consume municipality resources and funds inefficiently. Therefore, this paper aims to present a novel approach to utilize more than one data analysis and classification technique to ameliorate the leak identification process. In this research, acoustic leak signals were analyzed using Fourier Transform, and the multiple frequency bandwidths were determined.Three models were developed to identify the state of the leak using Naïve Bayes (NB), Deep Learning (DL), and Decision Tree (DT) Algorithms. Each of the developed models has an accuracy ranging between 84% to 89%. An aggregator approach was developed to cultivate the collective approaches developed into one single answer. Through aggregation, the accuracy of leak detection improved from 89% at its best to 100%.The design, implementation approach and results are displayed in this paper. Using this method helps municipalities minimize and alleviate the costs of uncertain leak verifications and efficiently allocate their resource

Enabling Resilience in Cyber-Physical-Human Water Infrastructures

Rapid urbanization and growth in urban populations have forced community-scale infrastructures (e.g., water, power and natural gas distribution systems, and transportation networks) to operate at their limits. Aging (and failing) infrastructures around the world are becoming increasingly vulnerable to operational degradation, extreme weather, natural disasters and cyber attacks/failures. These trends have wide-ranging socioeconomic consequences and raise public safety concerns. In this thesis, we introduce the notion of cyber-physical-human infrastructures (CPHIs) - smart community-scale infrastructures that bridge technologies with physical infrastructures and people. CPHIs are highly dynamic stochastic systems characterized by complex physical models that exhibit regionwide variability and uncertainty under disruptions. Failures in these distributed settings tend to be difficult to predict and estimate, and expensive to repair. Real-time fault identification is crucial to ensure continuity of lifeline services to customers at adequate levels of quality. Emerging smart community technologies have the potential to transform our failing infrastructures into robust and resilient future CPHIs.In this thesis, we explore one such CPHI - community water infrastructures. Current urban water infrastructures, that are decades (sometimes over a 100 years) old, encompass diverse geophysical regimes. Water stress concerns include the scarcity of supply and an increase in demand due to urbanization. Deterioration and damage to the infrastructure can disrupt water service; contamination events can result in economic and public health consequences. Unfortunately, little investment has gone into modernizing this key lifeline.To enhance the resilience of water systems, we propose an integrated middleware framework for quick and accurate identification of failures in complex water networks that exhibit uncertain behavior. Our proposed approach integrates IoT-based sensing, domain-specific models and simulations with machine learning methods to identify failures (pipe breaks, contamination events). The composition of techniques results in cost-accuracy-latency tradeoffs in fault identification, inherent in CPHIs due to the constraints imposed by cyber components, physical mechanics and human operators. Three key resilience problems are addressed in this thesis; isolation of multiple faults under a small number of failures, state estimation of the water systems under extreme events such as earthquakes, and contaminant source identification in water networks using human-in-the-loop based sensing. By working with real world water agencies (WSSC, DC and LADWP, LA), we first develop an understanding of operations of water CPHI systems. We design and implement a sensor-simulation-data integration framework AquaSCALE, and apply it to localize multiple concurrent pipe failures. We use a mixture of infrastructure measurements (i.e., historical and live water pressure/flow), environmental data (i.e., weather) and human inputs (i.e., twitter feeds), combined and enhanced with the domain model and supervised learning techniques to locate multiple failures at fine levels of granularity (individual pipeline level) with detection time reduced by orders of magnitude (from hours/days to minutes). We next consider the resilience of water infrastructures under extreme events (i.e., earthquakes) - the challenge here is the lack of apriori knowledge and the increased number and severity of damages to infrastructures. We present a graphical model based approach for efficient online state estimation, where the offline graph factorization partitions a given network into disjoint subgraphs, and the belief propagation based inference is executed on-the-fly in a distributed manner on those subgraphs. Our proposed approach can isolate 80% broken pipes and 99% loss-of-service to end-users during an earthquake.Finally, we address issues of water quality - today this is a human-in-the-loop process where operators need to gather water samples for lab tests. We incorporate the necessary abstractions with event processing methods into a workflow, which iteratively selects and refines the set of potential failure points via human-driven grab sampling. Our approach utilizes Hidden Markov Model based representations for event inference, along with reinforcement learning methods for further refining event locations and reducing the cost of human efforts.The proposed techniques are integrated into a middleware architecture, which enables components to communicate/collaborate with one another. We validate our approaches through a prototype implementation with multiple real-world water networks, supply-demand patterns from water utilities and policies set by the U.S. EPA. While our focus here is on water infrastructures in a community, the developed end-to-end solution is applicable to other infrastructures and community services which operate in disruptive and resource-constrained environments

Gestión inteligente de sistemas de distribución de agua

The United Nations predicts that the world's population in 2050 will reach 9.7 billion people. This exponential growth will mean an increase in the global demand for water available for human consumption. In addition, the advance of climate change is causing the occurrence of more frequent droughts, especially in arid and semi-arid areas. Indirectly, this means an increase in the costs associated with water transport and purification, as water must be drawn from sources that are increasingly distant from the points of consumption and the quality is getting worse. The traditional management of urban water supply is changing towards a more sustainable model aimed at an efficient use of resources (water, energy, labour) that not only reduces management costs but is also more environmentally friendly. This transformation is taking place due to the development of other transversal disciplines (cloud computing, communication systems, Big Data, electronics, etc.) applied to many fields of science, which applied to water management, can bring considerable benefits. Furthermore, to achieve intelligent management of a water supply network, it is necessary to rely on current tools that provide objective knowledge of the system. For example, geographic information systems (GIS) together with hydraulic models serve as a georeferenced database where the behaviour of any hydraulic network in different scenarios can be simulated. The Internet of Things (IoT) allows the connection of a network of sensors to know the main hydraulic variables at any time, providing key information for hydraulic models to faithfully reproduce the behaviour of modelled systems in real time. Digitalisation itself favours the use of information and communication technologies (ICT) to convert traditional management into smart management. For these reasons, new studies are needed to assess the potential and applicability of these new tools. This thesis is organised in 6 chapters focused on the development and application of a decision support system that allow the manager of a water supply network to make decisions based on data recorded on real-time. All the tools developed throughout this thesis have been tested in a real water supply network located in the south of Spain, managed by the Provincial Water Company of Cordoba (EMPROACSA). Chapter 1 shows the trajectory of urban supply management: explaining the starting point and where it is expected to achieve. Then, Chapter 2 describes the main objective and the specific objectives of this thesis, as well as the structure of this document. Chapter 3 presents a methodology that serves as a basis for starting the digitisation process in water supply networks. The system developed is based on three pillars: the geographic information system, the hydraulic model, and the application for mobile devices. The geographic information system provides a georeferenced database of the infrastructures that compose the hydraulic network; the hydraulic model simulates the response of the network to different operation scenarios; and finally, the mobile application facilitates the feedback of the system to keep it always up to date with changes in the systems. One of the distinguishing features of this work is the use of free software (Qgis, Epanet and Google My Maps) in all stages, which fosters digitisation in supply companies with a low budget. Chapter 4 develops an early warning system based on water pressure monitoring. The communication node developed ad-hoc for this work, sends water pressure data to the cloud, where users can visualise them with a device with an internet connection. Among its advantages are its low cost, it allows the use of different communication systems and has a high autonomy powered by batteries, which makes it well adapted to supply systems. The proposed monitoring system detects failures in the network due to pressure drops, alerting managers of the affected zone. Chapter 5 explains the decision support tool developed to deal with failures in water transmission networks. The web platform that supports this tool is divided into 3 independent modules: fault detection, alerts, and fault repair. The first module is responsible for detecting, geolocating and classifying faults in the hydraulic network using the information recorded in real time by the pressure monitoring system described in the previous chapter. The second module is responsible for sending alerts selectively to the workers in the area of the failure. Finally, the third module estimates, applying the hydraulic model, the maximum time that the manager has to fix failures, avoiding supply cuts using the water stored in regulation tanks when the failure occurs. The fault detection and classification module has demonstrated a 95% accuracy when applied to a real case. Chapter 6 contains the general conclusions of the thesis, as well as possible lines of future work. In summarise, water management is experiencing a paradigm shift. This transformation requires sufficiently mature technologies to ensure good results. Therefore, studies are needed that not only advance towards smart management, but also evaluate the tools available now and their integration into the current management model. This thesis presents a decision support system applied to supply networks, which help managers to make decisions based on objective information, not on intuition or experience. The use of open-source software and hardware in all the developments of this thesis must be emphasised. This specific feature allows the adoption of the methodologies proposed by water companies, regardless of size or financial resources, enabling the whole system or only part of it to be adapted to the operation of the company.Las Naciones Unidas prevén que la población mundial en 2050 alcanzará los 9.700 millones de personas. Este crecimiento exponencial supondrá un aumento de la demanda global de agua disponible para el consumo humano. Además, el avance del cambio climático está provocando la aparición de sequías más frecuentes, especialmente en las zonas áridas y semiáridas. Indirectamente, esto supone un aumento de los costes asociados al transporte y la depuración del agua, ya que hay que extraerla de fuentes cada vez más alejadas de los puntos de consumo y la calidad es cada vez peor. La gestión tradicional del abastecimiento de agua en las ciudades está cambiando hacia un modelo más sostenible orientado a un uso eficiente de los recursos (agua, energía, mano de obra) que además de reducir los costes de gestión, es más respetuoso con el medio ambiente. Esta transformación se está produciendo gracias al desarrollo de otras disciplinas transversales (computación en la nube, sistemas de comunicación, Big Data, electrónica, etc.) aplicadas a diversos campos de la ciencia, que aplicadas a la gestión del agua, pueden aportar considerables beneficios. Además, para conseguir una gestión inteligente de una red de abastecimiento de agua, es necesario apoyarse en herramientas actuales que proporcionen un conocimiento objetivo del sistema. Por ejemplo, los sistemas de información geográfica (SIG) junto con los modelos hidráulicos sirven como base de datos georreferenciada donde se puede simular el comportamiento de cualquier red hidráulica en diferentes escenarios. El Internet de las Cosas (IoT) permite la conexión de una red de sensores para conocer las principales variables hidráulicas en cada momento, aportando información clave para que los modelos hidráulicos reproduzcan fielmente el comportamiento de los sistemas modelizados en tiempo real. La propia digitalización favorece el uso de las tecnologías de la información y la comunicación (TIC) para convertir la gestión tradicional en una gestión inteligente. Por estas razones, son necesarios nuevos estudios para evaluar el potencial y la aplicabilidad de estas nuevas herramientas. Esta tesis se organiza en 6 capítulos centrados en el desarrollo y aplicación de un sistema de apoyo a la decisión que permita al gestor de una red de abastecimiento de agua tomar decisiones basadas en datos registrados en tiempo real. Todas las herramientas desarrolladas a lo largo de esta tesis han sido probadas en una red real de abastecimiento de agua situada en el sur de España, gestionada por la Empresa Provincial de Aguas de Córdoba (EMPROACSA). El capítulo 1 muestra la trayectoria de la gestión del abastecimiento urbano: explicando el punto de partida y hacia dónde se espera llegar. A continuación, el capítulo 2 describe el objetivo principal y los objetivos específicos de esta tesis, así como la estructura de este documento. El capítulo 3 presenta una metodología que sirve de base para iniciar el proceso de digitalización de las redes de abastecimiento de agua. El sistema desarrollado se basa en tres pilares: el sistema de información geográfica, el modelo hidráulico y la aplicación para dispositivos móviles. El sistema de información geográfica proporciona una base de datos georreferenciada de las infraestructuras que componen la red hidráulica; el modelo hidráulico simula la respuesta de la red ante diferentes escenarios de operación; y, por último, la aplicación móvil facilita la retroalimentación del sistema para mantenerlo siempre actualizado con los cambios en los sistemas. Uno de los rasgos distintivos de este trabajo es el uso de software libre (Qgis, Epanet y Google My Maps) en todas las etapas, lo que favorece la digitalización en empresas de abastecimiento con bajo presupuesto. El capítulo 4 desarrolla un sistema de alerta temprana basado en la monitorización de la presión del agua. El nodo de comunicación desarrollado ad-hoc para este trabajo, envía los datos de la presión del agua a la nube, donde los usuarios pueden visualizarlos con un dispositivo con conexión a internet. Entre sus ventajas están su bajo coste, permite el uso de diferentes sistemas de comunicación y tiene una gran autonomía alimentada por baterías, lo que hace que se adapte bien a los sistemas de abastecimiento. El sistema de monitorización propuesto detecta fallos en la red por caídas de presión, alertando a los gestores de la zona afectada. El capítulo 5 explica la herramienta de apoyo a la toma de decisiones desarrollada para hacer frente a las averías en las redes de abastecimiento en alta. La plataforma web, que soporta esta herramienta, se divide en 3 módulos independientes: detección de averías, alertas y reparación de averías. El primer módulo se encarga de detectar, geolocalizar y clasificar las averías en la red hidráulica a partir de la información registrada en tiempo real por el sistema de monitorización de presiones descrito en el capítulo anterior. El segundo módulo se encarga de enviar alertas de forma selectiva a los trabajadores de la zona de la avería. Por último, el tercer módulo estima, aplicando el modelo hidráulico, el tiempo máximo del que dispone el gestor para solucionar las averías, evitando los cortes de suministro con el agua almacenada en los depósitos de regulación cuando se produce la avería. El módulo de detección y clasificación de averías ha demostrado una precisión del 95% cuando se aplica a un caso real. El capítulo 6 contiene las conclusiones generales de la tesis, así como posibles líneas de trabajo futuras. En resumen, la gestión del agua está experimentando un cambio de paradigma. Esta transformación requiere tecnologías suficientemente maduras para garantizar buenos resultados. Por ello, son necesarios estudios que no sólo avancen hacia una gestión inteligente, sino que evalúen las herramientas disponibles en la actualidad y su integración en el modelo de gestión actual. Esta tesis presenta un sistema de apoyo a la decisión aplicado a las redes de suministro de agua, que ayuda a los gestores a tomar decisiones basadas en información objetiva y no en la intuición o la experiencia. Cabe destacar el uso de software y hardware de código abierto en todos los desarrollos de esta tesis. Esta particularidad permite la adopción de las metodologías propuestas por las empresas de agua, independientemente de su tamaño o recursos financieros, permitiendo adaptar todo el sistema o sólo una parte de él al funcionamiento de la empresa

Bibliographical review on cyber attacks from a control oriented perspective

This paper presents a bibliographical review of definitions, classifications and applications concerning cyber attacks in networked control systems (NCSs) and cyber-physical systems (CPSs). This review tackles the topic from a control-oriented perspective, which is complementary to information or communication ones. After motivating the importance of developing new methods for attack detection and secure control, this review presents security objectives, attack modeling, and a characterization of considered attacks and threats presenting the detection mechanisms and remedial actions. In order to show the properties of each attack, as well as to provide some deeper insight into possible defense mechanisms, examples available in the literature are discussed. Finally, open research issues and paths are presented.Peer ReviewedPostprint (author's final draft

An Unsupervised Cluster: Learning Water Customer Behavior Using Variation of Information on a Reconstructed Phase Space

The unsupervised clustering algorithm described in this dissertation addresses the need to divide a population of water utility customers into groups based on their similarities and differences, using only the measured flow data collected by water meters. After clustering, the groups represent customers with similar consumption behavior patterns and provide insight into ‘normal’ and ‘unusual’ customer behavior patterns. This research focuses upon individually metered water utility customers and includes both residential and commercial customer accounts serviced by utilities within North America. The contributions of this dissertation not only represent a novel academic work, but also solve a practical problem for the utility industry. This dissertation introduces a method of agglomerative clustering using information theoretic distance measures on Gaussian mixture models within a reconstructed phase space. The clustering method accommodates a utility’s limited human, financial, computational, and environmental resources. The proposed weighted variation of information distance measure for comparing Gaussian mixture models places emphasis upon those behaviors whose statistical distributions are more compact over those behaviors with large variation and contributes a novel addition to existing comparison options

Battle of the Attack Detection Algorithms:Disclosing cyber attacks on water distribution networks

The BATtle of the Attack Detection ALgorithms (BATADAL) is the most recent competition on planning and management of water networks undertaken within the Water Distribution Systems Analysis Symposium. The goal of the battle was to compare the performance of algorithms for the detection of cyber-physical attacks, whose frequency increased in the past few years along with the adoption of smart water technologies. The design challenge was set for C-Town network, a real-world, medium-sized water distribution system operated through Programmable Logic Controllers and a Supervisory Control And Data Acquisition (SCADA) system. Participants were provided with datasets containing (simulated) SCADA observations, and challenged with the design of an attack detection algorithm. The effectiveness of all submitted algorithms was evaluated in terms of time-to-detection and classification accuracy. Seven teams participated in the battle and proposed a variety of successful approaches leveraging data analysis, model-based detection mechanisms, and rule checking. Results were presented at the Water Distribution Systems Analysis Symposium (World Environmental & Water Resources Congress), in Sacramento, on May 21-25, 2017. This paper summarizes the BATADAL problem, proposed algorithms, results, and future research directions

Smart Urban Water Networks

This book presents the paper form of the Special Issue (SI) on Smart Urban Water Networks. The number and topics of the papers in the SI confirm the growing interest of operators and researchers for the new paradigm of smart networks, as part of the more general smart city. The SI showed that digital information and communication technology (ICT), with the implementation of smart meters and other digital devices, can significantly improve the modelling and the management of urban water networks, contributing to a radical transformation of the traditional paradigm of water utilities. The paper collection in this SI includes different crucial topics such as the reliability, resilience, and performance of water networks, innovative demand management, and the novel challenge of real-time control and operation, along with their implications for cyber-security. The SI collected fourteen papers that provide a wide perspective of solutions, trends, and challenges in the contest of smart urban water networks. Some solutions have already been implemented in pilot sites (i.e., for water network partitioning, cyber-security, and water demand disaggregation and forecasting), while further investigations are required for other methods, e.g., the data-driven approaches for real time control. In all cases, a new deal between academia, industry, and governments must be embraced to start the new era of smart urban water systems

Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems

Over the last few decades, the field of fault diagnostics and structural health management has been experiencing rapid developments. The reliability, availability, and safety of engineering systems can be significantly improved by implementing multifaceted strategies of in situ diagnostics and prognostics. With the development of intelligence algorithms, smart sensors, and advanced data collection and modeling techniques, this challenging research area has been receiving ever-increasing attention in both fundamental research and engineering applications. This has been strongly supported by the extensive applications ranging from aerospace, automotive, transport, manufacturing, and processing industries to defense and infrastructure industries