Fault handling in large water networks with online dictionary learning. (October 2020) - Irofti, Paul; Stoican, Florin; Puig, Vicenç
Abstract: 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. Highlights: Fault detection and isolation in large scale networks. Sensor placement through a Gram–Schmidt graph-aware procedure. Dictionary learning for fault detection and isolation in large water networks. Lift big-data limitations by employing online dictionary learning strategies. Validation on benchmark "Hanoi network" and a generic large-scale network.