Vibration-based water leakage detection system for public open data platforms

Water Distribution Networks are known to lose a consistent percentage of drinkable water due to the presence of leakages. In this paper it is proposed a solution to detect water leaks consisting of: i) a new sensing equipment able to acoustically monitor the external surface of a newly laid underground pipe; ii) a training of several machine learning models able to analyse the data collected by the new sensing equipment; iii) a user dashboard to give the final user the possibility to monitor the pipe’s condition. The research process included the generation of artificial leakages capable to produce a suitable dataset necessary to properly train machine learning models onto

Embedding monitoring systems for cured-in-place pipes

This paper proposes a non-intrusive electrical measurement system for monitoring some relevant parameters in pipeline systems. Temperature and flow-rate are monitored, using MEMS sensors. The flow-rate is evaluated by measuring pipe vibration, with a 3 axis accelerometer, induced by turbulence when fluid flows within the tube. The sensors are embedded, during the curing process of the cured-in-place pipes, in the pipes wall making the system suitable to be installed in renewed or new pipeline. The first experimental results show that it may be possible to obtain, at low-cost, a fully monitored distribution network