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2025-04-01 09:32:38 +02:00
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_posts/projects/2019-02-24-water-networks.md
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@ -11,6 +11,10 @@ skills: Acoustic Signal Processing, Deep Learning (CNNs), Anomaly Detection, Rea
---
![Leaking pipe icon](/assets/images/projects/pipe_leak.png){: .align-left style="padding:0.1em; width:5em"}
In collaboration with Munich's municipal utility provider, Stadtwerke München (SWM), this project explored the feasibility of using acoustic monitoring for early leak detection in water pipe infrastructure. The primary goal was to develop machine learning models capable of identifying leak-indicating sound patterns within a real-world operational environment.
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**Project:** ErLoWa (Erkennung von Leckagen in Wasserleitungsnetzen)<br>
**Partner:** [Stadtwerke München (SWM)](https://www.swm.de/)<br>
**Duration:** Late 2018 - Early 2020<br>
@ -18,8 +22,6 @@ skills: Acoustic Signal Processing, Deep Learning (CNNs), Anomaly Detection, Rea
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In collaboration with Munich's municipal utility provider, Stadtwerke München (SWM), this project explored the feasibility of using acoustic monitoring for early leak detection in water pipe infrastructure. The primary goal was to develop machine learning models capable of identifying leak-indicating sound patterns within a real-world operational environment.
**Methodology & Activities:**
* **Data Acquisition:** Sensor networks comprising contact microphones were deployed across sections of Munich's suburban water network to capture continuous acoustic data.