  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.
