The University of Sheffield
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CCWI2017: F115 'Online advanced uncertain reasoning architecture with binomial event discriminator system for novelty detection in smart water networks'

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journal contribution
posted on 2017-09-01, 15:02 authored by Stephen Mounce, A. Fargus, M. Weeks, J. Young, D. Ejimbe, E. Goya, M. Holburn, T. Jackson, Joseph BoxallJoseph Boxall
Minimising the loss of treated water from water supply systems due to bursts is an ongoing issue for water service providers around the world. Sensor technology and the ‘big data’ they generate combined with machine learning based analytics are providing an opportunity for automated event detection. AURA-Alert has been developed as an online (Software as a Service) system which automates the training data selection (by selecting data with acceptable Match Strength and with regular retraining). The addition of a Binomial Event Discriminator service can produce alerts based on windows of thresholded match distances. A pilot deployment on over 200 live data streams in the cloud has been deployed as part of the SmartWater4Europe project. Examples of analysis for real events are presented. For a historic subset of eight data streams over a three month period up to 58% of bursts were detected (depending on window used for evaluation). It is concluded that the system is an effective and viable tool for novelty detection for water network time series data with potential for wider applicability. Key strengths include lack of per site configuration, data-driven self-learning (from periods of normality), real-time, high scalability and full automation of model retraining.


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