35 lines
2.6 KiB
Plaintext
35 lines
2.6 KiB
Plaintext
---
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title: "Acoustic Leak Detection"
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tags: [anomaly-detection, audio-processing, deep-learning, signal-processing, real-world-application, water-networks, infrastructure-monitoring]
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excerpt: "Anomaly detection models for acoustic leak detection in water networks."
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teaser: "/figures/10_water_networks_teaser.jpg"
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venue: "ICAART 2021"
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---
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Detecting leaks in vast municipal water distribution networks is critical for resource conservation and infrastructure maintenance. This study introduces and evaluates an **anomaly detection approach for acoustic leak identification**, specifically designed with **energy efficiency** and **ease of deployment** as key considerations.
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<FloatingImage
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src="/figures/10_water_networks_approach.jpg"
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alt="Diagram illustrating the anomaly detection approach for acoustic leak identification in water networks."
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width={400}
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height={300}
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float="right"
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caption="Illustration of the anomaly detection approach for acoustic leak identification in water networks."
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/>
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The methodology leverages acoustic recordings captured by microphones deployed directly on a section of a real-world **municipal water network**. Instead of requiring continuous monitoring, the proposed system mimics human inspection routines by performing **intermittent checks**, significantly reducing power consumption and data load.
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Various **anomaly detection models**, ranging from traditional "shallow" methods (e.g., GMMs, OC-SVMs) to more complex **deep learning architectures** (e.g., autoencoders, potentially CNNs on spectrograms), were trained using data representing normal network operation. These models were then evaluated on their ability to distinguish anomalous sounds indicative of leaks.
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Key findings include:
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* Detecting leaks occurring acoustically **nearby** the sensor proved relatively straightforward for most evaluated models.
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* **Neural network-based methods demonstrated superior performance** in identifying leaks originating **further away** from the sensor, showcasing their ability to capture more subtle acoustic signatures amidst background noise.
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<CenteredImage
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src="/figures/10_water_networks_mel.jpg"
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alt="Mel-spectrogram examples showing acoustic signatures of normal operation versus leak sounds"
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width={800}
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height={400}
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caption="Mel-spectrogram visualizations comparing normal sounds and leak-related acoustic patterns."
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maxWidth="100%"
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/>
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This research validates the feasibility of using anomaly detection for practical, energy-efficient acoustic leak monitoring in water networks, highlighting the advantages of deep learning techniques for detecting more challenging, distant leaks. <Cite bibtexKey="muller2021acoustic" /> |