Research on Leakage Detection Based on Flow Data of Pipeline Network

Research on Leakage Detection Based on Flow Data of Pipeline Network

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Tong WANG^a^,^b, Zhong-yu LI^a^,^b, Bing-qing KANG^a^,^b, Duo-lin ZHU^a^,^b, Qing-yi WANG^a^,^b, Hong-bin ZHAO^a^,^b, De-lun XU^a^,^b, Lei HONG^a^,^b

Water Resources and Power | 2023, 41(7) : 127 - 131

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Water Resources and Power | 2023, 41(7): 127-131

• WATER CONSERVANCY AND HYDROPOWER ENGINEERING •

Research on Leakage Detection Based on Flow Data of Pipeline Network

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Tong WANG^a^,^b, Zhong-yu LI^a^,^b, Bing-qing KANG^a^,^b, Duo-lin ZHU^a^,^b, Qing-yi WANG^a^,^b, Hong-bin ZHAO^a^,^b, De-lun XU^a^,^b, Lei HONG^a^,^b

Affiliations

^a.School of Civil Engineering, Chang’an University, Xi’an 710061, China

^b.Key Laboratory of Water Supply and Sewerage, Ministry of Housing and Urban-Rural Development, Chang’an University, Xi’an 710061, China

Published: 2023-07-25 doi: 10.20040/j.cnki.1000-7709.2023.20221541

Outline

Abstract

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In order to alleviate the waste of resources caused by the leakage of urban water supply networks, taking predictive classification as the basic idea, the monitoring data after wavelet noise reduction processing was used in the prediction model of PSO seeking least squares support vector machine algorithm, and the prediction model was trained and evaluated, then combined with the model prediction error distribution law, the estimation method of threshold value and leakage volume was introduced for leakage detection. The results show that the average error between the model prediction and the actual water volume is low, the stability is high, and the combination of the prediction-threshold classification method can detect sudden leakage in a timely manner and estimate the leakage volume relatively accurately.

Key words

water supply network / flow data / leakage detection / prediction classification / error analysis

Cite this Article

Tong WANG, Zhong-yu LI, Bing-qing KANG, Duo-lin ZHU, Qing-yi WANG, Hong-bin ZHAO, De-lun XU, Lei HONG. Research on Leakage Detection Based on Flow Data of Pipeline Network[J]. Water Resources and Power, 2023 , 41 (7) : 127 -131 . DOI: 10.20040/j.cnki.1000-7709.2023.20221541

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Year 2023 volume 41 Issue 7

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Article Info

doi: 10.20040/j.cnki.1000-7709.2023.20221541

Receive Date：2022-07-27
Online Date：2026-01-28
Published：2023-07-25

Article Data

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History

Received：2022-07-27
Revised：2022-09-23

Funding

Affiliations

^a.School of Civil Engineering, Chang’an University, Xi’an 710061, China

^b.Key Laboratory of Water Supply and Sewerage, Ministry of Housing and Urban-Rural Development, Chang’an University, Xi’an 710061, China

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表12种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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