Novel Leakage Detection by Ensemble CNN-SVM and Graph-Based Localization in Water Distribution Systems
| Autor: | Jiheon Kang, Jae-Ho Lee, Doo-Seop Eom, Soo-Hyun Wang, Youn-Jong Park |
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| Rok vydání: | 2018 |
| Předmět: |
Computer science
020208 electrical & electronic engineering Real-time computing Feature extraction 02 engineering and technology Convolutional neural network Pipeline transport Support vector machine Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Water treatment Electrical and Electronic Engineering Wireless sensor network Classifier (UML) Leakage (electronics) |
| Zdroj: | IEEE Transactions on Industrial Electronics. 65:4279-4289 |
| ISSN: | 1557-9948 0278-0046 |
| DOI: | 10.1109/tie.2017.2764861 |
| Popis: | In many water distribution systems, a significant amount of water is lost because of leakage during transit from the water treatment plant to consumers. As a result, water leakage detection and localization have been a consistent focus of research. Typically, diagnosis or detection systems based on sensor signals incur significant computational and time costs, whereas the system performance depends on the features selected as input to the classifier. In this paper, to solve this problem, we propose a novel, fast, and accurate water leakage detection system with an adaptive design that fuses a one-dimensional convolutional neural network and a support vector machine. We also propose a graph-based localization algorithm to determine the leakage location. An actual water pipeline network is represented by a graph network and it is assumed that leakage events occur at virtual points on the graph. The leakage location at which costs are minimized is estimated by comparing the actual measured signals with the virtually generated signals. The performance was validated on a wireless sensor network based test bed, deployed on an actual WDS. Our proposed methods achieved 99.3% leakage detection accuracy and a localization error of less than 3 m. |
| Databáze: | OpenAIRE |
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