Örn Garðarsson, G;
Boem, F;
Toni, L;
(2022)
Graph-Based Learning for Leak Detection and Localisation in Water Distribution Networks∗.
In:
IFAC-PapersOnLine.
(pp. pp. 661-666).
Elssevier
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Abstract
We propose the application of geometric deep learning techniques to the challenging leak detection and isolation problem in water distribution networks (WDNs). Specifically, we train two Chebyshev polynomial kernel Graph Convolutional Networks for the task of prediction, and reconstruction of nodal pressures in a WDN. Comparing the two network outputs (a predicted healthy model state with a reconstructed observation) a residual signal is obtained and analysed to detect leakages. By exploiting topological properties in the proposed approach, leakage isolation is also performed. We benchmark our method on the BattLeDIM 2020 dataset.
| Type: | Proceedings paper |
|---|---|
| Title: | Graph-Based Learning for Leak Detection and Localisation in Water Distribution Networks∗ |
| Event: | 11th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes SAFEPROCESS 2022 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.ifacol.2022.07.203 |
| Publisher version: | https://doi.org/10.1016/j.ifacol.2022.07.203 |
| Language: | English |
| Additional information: | © The Authors 2022. Original content in this paper is licensed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) Licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
| Keywords: | Fault detection, diagnosis, water distribution systems, geometric deep learning |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10155676 |
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