Microwave‐based system for non‐destructive monitoring water pipe networks using support vector machine

Becari, Wesley; Oliveira, Arthur M. de; Peres, Henrique E. M.; Correra, Fatima Salete

doi:10.1049/iet-smt.2016.0131

Cited by 1 publication

(1 citation statement)

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“…As a result, it is widely applied in speech and biological affect identification [38], physiological signal detection [39], body movement identification [40], signal feature detection and identification, etc. [41,42,43,44]. The proposed system employs ZigBee nodes serving as signal collection nodes and uses the 4G network to transmit the signals adopted by the sensors to the data processing center for processing.…”

Section: Introductionmentioning

confidence: 99%

Water Pipeline Leakage Detection Based on Machine Learning and Wireless Sensor Networks

Liu

et al. 2019

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%

Water Pipeline Leakage Detection Based on Machine Learning and Wireless Sensor Networks

Liu

et al. 2019

Sensors

View full text Add to dashboard Cite

show abstract

Microwave‐based system for non‐destructive monitoring water pipe networks using support vector machine

Cited by 1 publication

References 20 publications

Water Pipeline Leakage Detection Based on Machine Learning and Wireless Sensor Networks

Water Pipeline Leakage Detection Based on Machine Learning and Wireless Sensor Networks

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