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<header><h4 class="nav__title"><i class="fas fa-book"></i> Reference</h4></header>
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<ol class="bibliography"><li><span id="muller2021acoustic"><span style="font-variant: small-caps">Müller, R., Illium, S., Ritz, F., et al.</span> 2020. Acoustic leak detection in water networks. <i>arXiv preprint arXiv:2012.06280</i>.</span>
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data-bibtex-json='"@article{muller2021acoustic,\n title = {Acoustic leak detection in water networks},\n author = {Müller, Robert and Illium, Steffen and Ritz, Fabian and Schröder, Tobias and Platschek, Christian and Ochs, Jörg and Linnhoff-Popien, Claudia},\n journal = {arXiv preprint arXiv:2012.06280},\n year = {2020}\n}\n"'
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data-bibtex-json='"@article{muller2021acoustic,\n title = {Acoustic leak detection in water networks},\n author = {Müller, Robert and Illium, Steffen and Ritz, Fabian and Schröder, Tobias and Platschek, Christian and Ochs, Jörg and Linnhoff-Popien, Claudia},\n journal = {arXiv preprint arXiv:2012.06280},\n year = {2020}\n}\n"'
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<p style="display:block; width:40%" class="align-right"><img src="\assets\figures\10_water_networks_approach.jpg" alt="Diagram illustrating the anomaly detection approach for leak detection" /></p>
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<p>Detecting leaks in vast municipal water distribution networks is critical for resource conservation and infrastructure maintenance. This study introduces and evaluates an <strong>anomaly detection approach for acoustic leak identification</strong>, specifically designed with <strong>energy efficiency</strong> and <strong>ease of deployment</strong> as key considerations.</p>
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<p>The methodology leverages acoustic recordings captured by microphones deployed directly on a section of a real-world <strong>municipal water network</strong>. Instead of requiring continuous monitoring, the proposed system mimics human inspection routines by performing <strong>intermittent checks</strong>, significantly reducing power consumption and data load.</p>
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<p>Various <strong>anomaly detection models</strong>, ranging from traditional “shallow” methods (e.g., GMMs, OC-SVMs) to more complex <strong>deep learning architectures</strong> (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.</p>
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<p>Key findings include:</p>
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<ul>
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<li>Detecting leaks occurring acoustically <strong>nearby</strong> the sensor proved relatively straightforward for most evaluated models.</li>
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<li><strong>Neural network-based methods demonstrated superior performance</strong> in identifying leaks originating <strong>further away</strong> from the sensor, showcasing their ability to capture more subtle acoustic signatures amidst background noise.</li>
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<img src="/assets/figures/10_water_networks_mel.jpg" alt="Mel-spectrogram examples showing acoustic signatures of normal operation versus leak sounds" style="display:block; width:90%" />
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<figcaption>Mel-spectrogram visualizations comparing normal sounds and leak-related acoustic patterns.</figcaption>
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<p><br /></p>
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<p>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. <a class="citation" href="/publications#muller2021acoustic">[Müller et al. 2020]</a></p>
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<p class="page__date"><strong><i class="fas fa-fw fa-calendar-alt" aria-hidden="true"></i> Updated:</strong> <time class="dt-published" datetime="2021-03-03T00:00:00+01:00">March 3, 2021</time></p>
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