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Web-based Machine Learning Platform for Condition-Monitoring

: Bernard, Thomas; Kühnert, Christian; Campbell, Enrique

Volltext urn:nbn:de:0011-n-5320096 (1023 KByte PDF)
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Erstellt am: 8.2.2019

Beyerer, Jürgen (Ed.); Kühnert, Christian (Ed.); Niggemann, Oliver (Ed.):
Machine Learning for Cyber Physical Systems. Selected papers from the International Conference ML4CPS 2018 : Selected papers from the International Conference ML4CPS 2018, Karlsruhe, October 23rd and 24th, 2018
Berlin: Springer Vieweg, 2019 (Technologies for Intelligent Automation 9)
ISBN: 978-3-662-58484-2 (Print)
ISBN: 978-3-662-58485-9 (Online)
Conference on Machine Learning for Cyber-Physical-Systems and Industry 4.0 (ML4CPS) <4, 2018, Karlsruhe>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()
Machine-learning; water quality monitoring; anomaly detection; plugin architecture; data fusion

Modern water system infrastructures are equipped with a large amount of sensors. In recent years machine-learning (ML) algorithms became a promising option for data analysis. However, currently ML algorithms are not frequently used in real-world applications. One reason is the costly and time-consuming integration and maintenance of ML algorithms by data scientists. To overcome this challenge, this paper proposes a generic, adaptable platform for real-time data analysis in water distribution networks. The architecture of the platform allows to connect to different types of data sources, to process its measurements in realtime with and without ML algorithms and finally pushing the results to different sinks, like a database or a web-interface. This is achieved by a modular, plugin based software architecture of the platform. As a use-case, a data-driven anomaly detection algorithm is used to monitor the water quality of several water treatment plants of the city of Berlin.