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Supervised machine learning based system for automatic fault-detection in water-quality sensors | IEEE Conference Publication | IEEE Xplore

Supervised machine learning based system for automatic fault-detection in water-quality sensors


Abstract:

Online water-quality sensors installed in Wastewater Treatment Plants (WWTPs) are prone to process disturbances that generate erroneous data. Faulty sensor data can disru...Show More

Abstract:

Online water-quality sensors installed in Wastewater Treatment Plants (WWTPs) are prone to process disturbances that generate erroneous data. Faulty sensor data can disrupt automation systems and result in sub-optimal performance of WWTPs. This paper presents a machine-learning-based system for real-time detection and the subsequent correction of faulty sensor data installed in a full-scale municipal WWTP. The fault detection system is developed by training a k-nearest neighbour (kNN) classifier with labelled historical data. The trained kNN classifier is then deployed in the WWTP's web-based Supervisory Control And Data Acquisition (SCADA) system to assess the performance in real-time. A qualitative comparison between raw and corrected sensor data demonstrates the system's potential to detect sensor faults and provide stable and reliable surrogate values.
Date of Conference: 19-21 October 2022
Date Added to IEEE Xplore: 07 November 2022
ISBN Information:
Print on Demand(PoD) ISSN: 2372-1618
Conference Location: Sinaia, Romania

References

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