Abstract
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.
Keywords
- Machine-learning
- water quality monitoring
- anomaly detection
- plugin architecture
- data fusion
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Bernard, T., Kühnert, C., Campbell, E. (2019). Web-based Machine Learning Platform for Condition- Monitoring. In: Beyerer, J., Kühnert, C., Niggemann, O. (eds) Machine Learning for Cyber Physical Systems. Technologien für die intelligente Automation, vol 9. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-58485-9_5
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DOI: https://doi.org/10.1007/978-3-662-58485-9_5
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