In this work, we present a general procedure for acoustic leak detection in water networks that satisfies multiple real-world constraints such as energy efficiency and ease of deployment. Based on recordings from seven contact microphones attached to the water supply network of a municipal suburb, we trained several shallow and deep anomaly detection models. Inspired by how human experts detect leaks using electronic sounding-sticks, we use these models to repeatedly listen for leaks over a predefined decision horizon. This way we avoid constant monitoring of the system. While we found the detection of leaks in close proximity to be a trivial task for almost all models, neural network based approaches achieve better results at the detection of distant leaks.
A Multivariate Statistical Framework for Detection, Classification and Pre-localization of Anomalies in Water Distribution Networks
Institute of Computer Science and Information Technologies, National Technical University "Kharkiv Polytechnic Institute"（计算机科学与信息科技研究所，国家技术大学"Kharkiv 工程学院"）；Informatics Institute, University of Amsterdam（信息学院，阿姆斯特丹大学）；Bernoulli Institute, University of Groningen（伯努利研究所，格罗宁根大学）
Bielefeld University（比勒费尔德大学）；University of Cyprus（塞浦路斯大学）；Honda Research Institute（本田研究院）
Water distribution networks are a key component of modern infrastructure for housing and industry. They transport and distribute water via widely branched networks from sources to consumers. In order to guarantee a working network at all times, the water supply company continuously monitors the network and takes actions when necessary -- e.g. reacting to leakages, sensor faults and drops in water quality. Since real world networks are too large and complex to be monitored by a human, algorithmic monitoring systems have been developed. A popular type of such systems are residual based anomaly d…
The detection of leaks in pipeline transportation systems is a matter of serious concern for operators, who pursue the integrity of their assets, the reduction of losses and the prevention of environmental hazards. Whenever a hole occurs in a pressurized pipeline, the corresponding fluid leakage is characterized by a turbulent flow and a peculiar acoustic noise, whose characteristics depend also on the size of the hole itself. This study shows that both the presence and the size of such a leaking hole can be successfully detected, by exploiting the acoustic noise (pressure transients) generate…
Today, detection of anomalous events in civil infrastructures (e.g. water pipe breaks and leaks) is time consuming and often takes hours or days. Pipe breakage as one of the most frequent types of failure of water networks often causes community disruptions ranging from temporary interruptions in services to extended loss of business and relocation of residents. In this project, we design and implement a two-phase approach for leak event identification, which leverages dynamic data from multiple information sources including IoT sensing data (pressure values and/or flow rates), geophysical dat…
University of Bucharest（布加勒斯特大学）；Politehnica University of Bucharest（布加勒斯特理工大学）；Universitat Politècnica de Catalunya（加泰罗尼亚理工大学）；Institut de Robòtica i Informàtica Industrial（机器人与信息技术研究所）
In this paper, we propose a data-driven leak localization method for water distribution networks (WDNs) which combines two complementary approaches: graph-based interpolation and dictionary classification. The former estimates the complete WDN hydraulic state (i.e., hydraulic heads) from real measurements at certain nodes and the network graph. Then, these actual measurements, together with a subset of valuable estimated states, are used to feed and train the dictionary learning scheme. Thus, the meshing of these two methods is explored, showing that its performance is superior to either appro…
Factor Graph Optimization for Leak Localization in Water Distribution Networks
LOS-CS-FMI, University of Bucharest（布加勒斯特大学LOS-CS-FMI）；Institut de Robòtica i Informàtica Industrial, CSIC-UPC（机器人与信息工业研究所）；Dept. of Automation Control and Systems Engineering, Politehnica University of Bucharest（布加勒斯特理工大学自动化控制与系统工程系）；Supervision, Safety and Automatic Control Research Center (CS2AC) of the Universitat Politècnica de Catalunya（加泰罗尼亚理工大学监督、安全与自动控制研究中心）
Dual Unscented Kalman Filter Architecture for Sensor Fusion in Water Networks Leak Localization
Institut de Robòtica i Informàtica Industrial, CSIC-UPC（西班牙科学研究理事会-加泰罗尼亚理工大学机器人与工业自动化研究所）；LOS-CS-FMI, University of Bucharest（布加勒斯特大学数学与计算机科学学院计算机科学系）；Supervision, Safety and Automatic Control Research Center (CS2AC) of the Universitat Politècnica de Catalunya（加泰罗尼亚理工大学监督、安全与自动控制研究中心）；Politehnica University of Bucharest（布加勒斯特理工大学）
Unsupervised Online Detection of Pipe Blockages and Leakages in Water Distribution Networks
KIOS Research and Innovation Center of Excellence, University of Cyprus（希腊塞浦路斯大学KIOS研究中心）；Department of Electrical and Computer Engineering, University of Cyprus（塞浦路斯大学电气与计算机工程系）
Nodal Hydraulic Head Estimation through Unscented Kalman Filter for Data-driven Leak Localization in Water Networks
Institut de Robòtica i Informàtica Industrial, CSIC-UPC（西班牙国家研究委员会-加泰罗尼亚理工大学工业机器人与信息技术研究所）；University of Bucharest（布加勒斯特大学）；Institute for Logic and Data Science（逻辑与数据科学研究所）；Politehnica University of Bucharest（布加勒斯特理工大学）；Supervision, Safety and Automatic Control Research Center (CS2AC) - UPC（加泰罗尼亚理工大学监督、安全与自动控制研究中心）
Learning Dictionaries from Physical-Based Interpolation for Water Network Leak Localization
University of Bucharest（布加勒斯特大学）；Institute for Logic and Data Science（逻辑与数据科学研究所）；Institut de Robòtica i Informàtica Industrial, CSIC-UPC（西班牙国家研究委员会-加泰罗尼亚理工大学工业机器人研究所）；Universitat Politècnica de Catalunya（加泰罗尼亚理工大学）；Politehnica University of Bucharest（布加勒斯特理工大学）
Singapore University of Technology and Design (SUTD)（新加坡科技设计大学）；Thales Solutions Asia Pte Ltd（泰雷兹亚洲解决方案私人有限公司）
Shallow water environments create a challenging channel for communications. In this paper, we focus on the challenges posed by the frequency-selective signal distortion called the Doppler effect. We explore the design and performance of machine learning (ML) based demodulation methods --- (1) Deep Belief Network-feed forward Neural Network (DBN-NN) and (2) Deep Belief Network-Convolutional Neural Network (DBN-CNN) in the physical layer of Shallow Water Acoustic Communication (SWAC). The proposed method comprises of a ML based feature extraction method and classification technique. First, the f…
Explainable Fuzzy GNNs for Leak Detection in Water Distribution Networks
Jheronimus Academy of Data Science, Eindhoven University of Technology, Eindhoven, The Netherlands University of Bari Aldi Moro, Bari, Italy IHE Institute for Water Education, Delft, The Netherlands University of Cartagena, Cartagena, Spain School of Industrial Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
The impact of sensor placement on graph-neural-network-based leakage detection
Control Systems Group, Dept. of Electrical Engineering, Eindhoven University of Technology（埃因霍温理工大学控制系统组）；Wetsus, Centre of Excellence for Sustainable Water Technology（可持续水技术卓越中心）；Statistical Methods – Biometris, Wageningen University（瓦格宁根大学统计方法—生物计量学）
A comparison between joint and dual UKF implementations for state estimation and leak localization in water distribution networks
Department of Systems Eng., Automation and Industrial Informatics (ESAII), Universitat Politècnica de Catalunya - BarcelonaTech (UPC)（系统工程、自动化与工业信息系（ESAII），巴塞罗那技术大学（UPC））；LOS-CS-FMI, University of Bucharest（洛斯-斯-费米（LOS-CS-FMI），布加勒斯特大学）；Deptartment of Automation Control and Systems Engineering, Politehnica University of Bucharest（自动化控制与系统工程系，布加勒斯特理工大学）；Institut de Robòtica i Informàtica Industrial, CSIC-UPC, Barcelona（机器人与信息工业研究所，CSIC-UPC，巴塞罗那）；Supervision, Safety and Automatic Control Research Center (CS2AC), Universitat Politècnica de Catalunya（监督、安全与自动控制研究中心（CS2AC），巴塞罗那技术大学）
