A field implementation of linear prediction for leak-monitoring in water distribution networks. (August 2020)
- Record Type:
- Journal Article
- Title:
- A field implementation of linear prediction for leak-monitoring in water distribution networks. (August 2020)
- Main Title:
- A field implementation of linear prediction for leak-monitoring in water distribution networks
- Authors:
- Cody, Roya A.
Narasimhan, Sriram - Abstract:
- Abstract: Water distribution networks (WDNs) are complex systems that are subjected to stresses due to a number of hydraulic and environmental loads. As a result, system leaks remain an unavoidable reality. Leaks which are not large enough to become visible at the street level can often go undetected for prolonged periods of time; the presence of smaller leaks can be concealed in system variability. The current paper addresses the problem of leak-detection and localization in WDNs, using a data-driven methodology which utilizes linear prediction (LP) theory. LP has a relatively simple mathematical formulation and has been shown in laboratory studies to effectively capture leak-induced signatures in fluid-filled pipes. In this paper, the performance of LP for leak-detection is verified, using field data in an operational WDN. In addition, a two-part localization approach is proposed which utilizes LP pre-processed data, in tandem with the traditional cross-correlation approach. Results of the field study show that the proposed method is able to perform both leak-detection and localization in full-scale systems using relatively short time signal lengths. This is advantageous in continuous monitoring situations as this minimizes data transmission requirements, which are one of the main impediments to full-scale implementation and deployment of leak-detection technology. In addition to the analysis results, a novel hydrant-mounted data-acquisition system is proposed, along withAbstract: Water distribution networks (WDNs) are complex systems that are subjected to stresses due to a number of hydraulic and environmental loads. As a result, system leaks remain an unavoidable reality. Leaks which are not large enough to become visible at the street level can often go undetected for prolonged periods of time; the presence of smaller leaks can be concealed in system variability. The current paper addresses the problem of leak-detection and localization in WDNs, using a data-driven methodology which utilizes linear prediction (LP) theory. LP has a relatively simple mathematical formulation and has been shown in laboratory studies to effectively capture leak-induced signatures in fluid-filled pipes. In this paper, the performance of LP for leak-detection is verified, using field data in an operational WDN. In addition, a two-part localization approach is proposed which utilizes LP pre-processed data, in tandem with the traditional cross-correlation approach. Results of the field study show that the proposed method is able to perform both leak-detection and localization in full-scale systems using relatively short time signal lengths. This is advantageous in continuous monitoring situations as this minimizes data transmission requirements, which are one of the main impediments to full-scale implementation and deployment of leak-detection technology. In addition to the analysis results, a novel hydrant-mounted data-acquisition system is proposed, along with its unique hardware and software capabilities. … (more)
- Is Part Of:
- Advanced engineering informatics. Volume 45(2020)
- Journal:
- Advanced engineering informatics
- Issue:
- Volume 45(2020)
- Issue Display:
- Volume 45, Issue 2020 (2020)
- Year:
- 2020
- Volume:
- 45
- Issue:
- 2020
- Issue Sort Value:
- 2020-0045-2020-0000
- Page Start:
- Page End:
- Publication Date:
- 2020-08
- Subjects:
- Leak detection -- Water distribution networks -- Linear prediction -- Acoustic signals
Computer-aided engineering -- Periodicals
Engineering -- Data processing -- Periodicals
620.00285 - Journal URLs:
- http://www.sciencedirect.com/science/journal/14740346 ↗
http://books.google.com/books?id=KhFVAAAAMAAJ ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.aei.2020.101103 ↗
- Languages:
- English
- ISSNs:
- 1474-0346
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 0696.851100
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 13568.xml