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Cloud-based event detection platform for water distribution networks using machine-learning algorithms

 
: Kühnert, Christian; Baruthio, M.; Bernard, Thomas; Steinmetz, C.; Weber, J.-M.

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Fulltext urn:nbn:de:0011-n-3605353 (915 KByte PDF)
MD5 Fingerprint: 23e987a2ca3242e58b37714de7ca103a
Created on: 6.10.2015


Procedia Engineering 119 (2015), pp.901-907
ISSN: 1877-7058
International Conference on Computing and Control for the Water Industry (CCWI) <13, 2015, Leicester>
English
Journal Article, Conference Paper, Electronic Publication
Fraunhofer IOSB ()
machine learning; time series analysis; event-detection; cloud-based service

Abstract
Modern water distribution networks are equipped with a large amount of sensors to monitor the drinking water quality. To detect anomalies, usually each sensor contains its own threshold, but machine-learning algorithms become an alternative to reduce the parametrization effort. Still, one reason why they are not used in practice is the geographical restricted data access. Data is stored at the plant, but data scientists needed for the data analysis are situated elsewhere.
To overcome this challenge, this paper proposes a cloud-based event-detection and reporting platform, which provides a possibility to use machine learning algorithms. The plants measurements are cyclically transferred into a secure cloud service where they are downloaded and analyzed from the data scientist. Results are made available as reports.

: http://publica.fraunhofer.de/documents/N-360535.html