Fault handling in large water networks with online dictionary learning
Fault detection and isolation in water distribution networks is an active topic due to the nonlinearities of flow propagation and recent increases in data availability due to sensor deployment. Here, we propose an efficient two-step data driven alternative: first, we perform sensor placement taking...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/341071 |
| Acceso en línea: | https://hdl.handle.net/2117/341071 https://dx.doi.org/10.1016/j.jprocont.2020.08.003 |
| Access Level: | acceso abierto |
| Palabra clave: | Water -- Distribution Automatic control Fault detection and isolation Sensor placement Online dictionary learning Classification Water networks Aigua -- Distribució Control automàtic Àrees temàtiques de la UPC::Informàtica::Automàtica i control |
| Sumario: | Fault detection and isolation in water distribution networks is an active topic due to the nonlinearities of flow propagation and recent increases in data availability due to sensor deployment. Here, we propose an efficient two-step data driven alternative: first, we perform sensor placement taking the network topology into account; second, we use incoming sensor data to build a network model through online dictionary learning. Online learning is fast and allows tackling large networks as it processes small batches of signals at a time. This brings the benefit of continuous integration of new data into the existing network model, either in the beginning for training or in production when new data samples are gathered. The proposed algorithms show good performance in our simulations on both small and large-scale networks. |
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