Abstract
The water supply is part of the critical infrastructure as the accessibility of clean drinking water is essential to ensure the health of the people. To guarantee the availability of fresh water, efficient and reliable water distribution networks are crucial. Monitoring these systems is necessary to avoid deterioration in water quality, deal with leakages and prevent cyber-physical attacks. While the installation of a growing amount of sensors is increasing the possibilities to monitor the system, considering the control of the senors becomes another challenge as sensor faults negatively influence the reliability of systems dealing with leakages and monitoring water quality. In this work, we aim to overcome the negative implications induced by sensor faults by using a sensor fault monitoring system based on three steps. First, established residual based fault detection is applied. In a second step, we extend this method to a fault isolation technique and finally propose fault accommodation by standard imputation techniques and different types of virtual sensors.
We gratefully acknowledge funding in the frame of the BMBF project TiM, 05M20PBA; of the BMWi project KI-Marktplatz, 01MK20007E; from the VW-Foundation for the project IMPACT funded in the frame of the funding line AI and its Implications for Future Society.
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Notes
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The code is available at https://github.com/vvaquet/sensor-fault-detection.
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MSE: \(\frac{1}{n}\sum _{i=1}^n (y_i-\hat{y}_i)^2\), with ground truth \(y_i\) and prediciton \(\hat{y}_i\); \(R^2 = 1-\frac{\sum (y_i-\hat{y}_i)^2}{\sum (y_i-\overline{{y}_i})^2}\).
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Some \(R^2\) scores are missing as they are not defined for constant values.
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Vaquet, V., Artelt, A., Brinkrolf, J., Hammer, B. (2022). Taking Care of Our Drinking Water: Dealing with Sensor Faults in Water Distribution Networks. In: Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., Aydin, M. (eds) Artificial Neural Networks and Machine Learning – ICANN 2022. ICANN 2022. Lecture Notes in Computer Science, vol 13530. Springer, Cham. https://doi.org/10.1007/978-3-031-15931-2_56
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