Fault Handling in Large Water Networks with Online Dictionary Learning
This addresses fault handling for water distribution systems, offering a simplified, scalable solution that is incremental in applying existing online learning techniques to this domain.
The paper tackles fault detection in water distribution networks by proposing a data-driven approach that uses network topology for sensor placement and online dictionary learning to model the network from sensor data. The method demonstrates good performance on both small and large-scale networks.
Fault detection and isolation in water distribution networks is an active topic due to its model's mathematical complexity and increased data availability through sensor placement. Here we simplify the model by offering a data driven alternative that takes the network topology into account when performing sensor placement and then proceeds to build a network model through online dictionary learning based on the incoming sensor data. Online learning is fast and allows tackling large networks as it processes small batches of signals at a time and has the benefit of continuous integration of new data into the existing network model, be it in the beginning for training or in production when new data samples are encountered. The algorithms show good performance when tested on both small and large-scale networks.