Water Pipeline Leakage Detection Based on Machine Learning and Wireless Sensor Networks
The detection of water pipeline leakage is important to ensure that water supply networks can operate safely and conserve water resources. To address the lack of intelligent and the low efficiency of conventional leakage detection methods, this paper designs a leakage detection method based on machi...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-11-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/19/23/5086 |
| _version_ | 1811262496549371904 |
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| author | Yang Liu Xuehui Ma Yuting Li Yong Tie Yinghui Zhang Jing Gao |
| author_facet | Yang Liu Xuehui Ma Yuting Li Yong Tie Yinghui Zhang Jing Gao |
| author_sort | Yang Liu |
| collection | DOAJ |
| description | The detection of water pipeline leakage is important to ensure that water supply networks can operate safely and conserve water resources. To address the lack of intelligent and the low efficiency of conventional leakage detection methods, this paper designs a leakage detection method based on machine learning and wireless sensor networks (WSNs). The system employs wireless sensors installed on pipelines to collect data and utilizes the 4G network to perform remote data transmission. A leakage triggered networking method is proposed to reduce the wireless sensor network’s energy consumption and prolong the system life cycle effectively. To enhance the precision and intelligence of leakage detection, we propose a leakage identification method that employs the intrinsic mode function, approximate entropy, and principal component analysis to construct a signal feature set and that uses a support vector machine (SVM) as a classifier to perform leakage detection. Simulation analysis and experimental results indicate that the proposed leakage identification method can effectively identify the water pipeline leakage and has lower energy consumption than the networking methods used in conventional wireless sensor networks. |
| first_indexed | 2024-04-12T19:26:31Z |
| format | Article |
| id | doaj.art-ab73db77a0864fbea2fc858a01593469 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-12T19:26:31Z |
| publishDate | 2019-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ab73db77a0864fbea2fc858a015934692022-12-22T03:19:28ZengMDPI AGSensors1424-82202019-11-011923508610.3390/s19235086s19235086Water Pipeline Leakage Detection Based on Machine Learning and Wireless Sensor NetworksYang Liu0Xuehui Ma1Yuting Li2Yong Tie3Yinghui Zhang4Jing Gao5College of Electronic Information Engineering, Inner Mongolia University, Hohhot 010021, ChinaCollege of Electronic Information Engineering, Inner Mongolia University, Hohhot 010021, ChinaCollege of Electronic Information Engineering, Inner Mongolia University, Hohhot 010021, ChinaCollege of Electronic Information Engineering, Inner Mongolia University, Hohhot 010021, ChinaCollege of Electronic Information Engineering, Inner Mongolia University, Hohhot 010021, ChinaTianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin 300387, ChinaThe detection of water pipeline leakage is important to ensure that water supply networks can operate safely and conserve water resources. To address the lack of intelligent and the low efficiency of conventional leakage detection methods, this paper designs a leakage detection method based on machine learning and wireless sensor networks (WSNs). The system employs wireless sensors installed on pipelines to collect data and utilizes the 4G network to perform remote data transmission. A leakage triggered networking method is proposed to reduce the wireless sensor network’s energy consumption and prolong the system life cycle effectively. To enhance the precision and intelligence of leakage detection, we propose a leakage identification method that employs the intrinsic mode function, approximate entropy, and principal component analysis to construct a signal feature set and that uses a support vector machine (SVM) as a classifier to perform leakage detection. Simulation analysis and experimental results indicate that the proposed leakage identification method can effectively identify the water pipeline leakage and has lower energy consumption than the networking methods used in conventional wireless sensor networks.https://www.mdpi.com/1424-8220/19/23/5086leakage detectionwireless sensor networksmachine learningleakage triggered networking |
| spellingShingle | Yang Liu Xuehui Ma Yuting Li Yong Tie Yinghui Zhang Jing Gao Water Pipeline Leakage Detection Based on Machine Learning and Wireless Sensor Networks Sensors leakage detection wireless sensor networks machine learning leakage triggered networking |
| title | Water Pipeline Leakage Detection Based on Machine Learning and Wireless Sensor Networks |
| title_full | Water Pipeline Leakage Detection Based on Machine Learning and Wireless Sensor Networks |
| title_fullStr | Water Pipeline Leakage Detection Based on Machine Learning and Wireless Sensor Networks |
| title_full_unstemmed | Water Pipeline Leakage Detection Based on Machine Learning and Wireless Sensor Networks |
| title_short | Water Pipeline Leakage Detection Based on Machine Learning and Wireless Sensor Networks |
| title_sort | water pipeline leakage detection based on machine learning and wireless sensor networks |
| topic | leakage detection wireless sensor networks machine learning leakage triggered networking |
| url | https://www.mdpi.com/1424-8220/19/23/5086 |
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