Waternomics is a European Union-funded research project aspiring to develop and introduce Information and Communication Technology (ICT) as an enabling technology to manage water as a resource, increase end-user conservation awareness, affect behavioural changes and avoid water l
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Waternomics is a European Union-funded research project aspiring to develop and introduce Information and Communication Technology (ICT) as an enabling technology to manage water as a resource, increase end-user conservation awareness, affect behavioural changes and avoid water losses through leak detection. Existing leakage detection methods are generally focused on scrutinising large diameter pipes in water supply distribution networks or transmission pipes. However, it has been estimated that the average household's leaks can be as much as 35m3 of water per year. In order to solve the problem, analysis of different types of data in the household piping system is required, including detection and identification. One conventional approach is to use flow sensors installed at several locations within the household piping system and perform a mass balance approach to detect leakage. However, this method is expensive and difficult to implement. This research proposes a novel approach to household leakage detection by means of sound signal recordings. The approach consists of recording the sound signals that are produced by water fixtures and appliances, and then use these recordings to detect any abnormal situation which may be an indication of a leak. The method comprises three major steps: recording, storing and processing of sound signals. The recording step is done by means of a non-intrusive sound sensor that sends records remotely; the storage step is made in a database of sound signals for different types of uses; finally, the processing step is made through a sound signal identification software tool that is able to search the database libraries for related sounds, in a similar way as the Shazam app for music. Tests of the leak detection method are presented for data collected in laboratory conditions. Results show that this detection method has a potential to help reducing leakages through an easy-to-install and non-intrusive sensor.
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