Leaks in resource transmission pipelines is a growing concern for the water transmission industry. This creates a need to prevent the threat of leaks and minimize their damages through extensive research in leak detection technology. This research work provides a thorough investigation into the history of leak detection in pipelines by surveying the web of knowledge database and visualizing the outputs using visualization software VOSviewer and CiteNetExplorer. The analysis of the web of knowledge output presents a set of the ten most used keywords in the field of leak detection in pipelines that are further described and analyzed. Additionally, in-depth analysis of a randomly selected sample of papers was conducted to draw a sense of the progress in the industry over the past four decades. This article also defines a novel approach to define the leak detection phases, i.e., the identify-localize-pinpoint approach. Furthermore, two classes of leak detection systems are identified, static leak detection systems and dynamic leak detection systems. The two systems are defined as well as their differentiative capabilities. Finally, this article provides a summary of popular leak detection technologies to provide a broad sense of understanding for the leak detection field of research.
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 resources.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.