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| single | Detection and localization of leakages in water networks. | projects | A u | acoustic anomaly-detection |
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In cooperation with the Munich City Services (Stadtwerke München (SWM)), we researched the possibilities of leakage detection in real-world water networks. Therefore, we installed contact microphones directly on the infrastructure in one of Munich's suburbs. {% cite muller2021acoustic%}
<img src="\assets\images\projects\pipe_leak\1fe8265e-ff21-4e9c-8a2a-2ebcaec41728.jpeg" style="margin-bottom: 0em;">
<img src="\assets\images\projects\pipe_leak\8d2364f1-1b03-480d-9ed3-09d548f47383.jpeg" style="margin-bottom: 0em;">
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The feasibility study revealed the technical limitations of this undertaking. Nonetheless, we gain valuable insights. Through audio-to-mel spectrogram transformations and the power of machine learning, deep neural networks (conv. and recurrent) are more capable of extracting important features for the given task than classic ML approaches. In this context, our team was able to produce further publications. {% cite elsner2019deep illium2020surgical muller2020analysis illium2022empirical%}
<img src="\assets\images\projects\pipe_leak\5ea8f0ee-6c61-4835-944c-b77683d397ca.jpeg" style="margin-bottom: 0em;">
<img src="\assets\images\projects\pipe_leak\cc01cc58-d3f6-4220-b4f4-c7ea26b3a116.jpeg" style="margin-bottom: 0em;">
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This project ran from late 2018 until the early months of 2020.
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