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Automated active fault detection in fouled dissolved oxygen sensors
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. IVL Swedish Environm Res Inst, Stockholm, Sweden.ORCID iD: 0000-0002-4476-8025
MathWorks AB, Knarrarnasgatan 7, SE-16440 Kista, Sweden.ORCID iD: 0000-0002-8034-4043
IVL Swedish Environm Res Inst, Stockholm, Sweden.ORCID iD: 0000-0001-5635-8472
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
2019 (English)In: Water Research, ISSN 0043-1354, E-ISSN 1879-2448, Vol. 166, article id 115029Article in journal (Refereed) Published
Abstract [en]

Biofilm formation causes bias in dissolved oxygen (DO) sensors, which hamper their usage for automatic control and thereby balancing energy- and treatment efficiency. We analysed if a dataset that was generated with deliberate perturbations, can automatically be interpreted to detect bias caused by biofilm formation. We used a challenging set-up with realistic conditions that are required for a full-scale application. This included automated training (adapting to changing normal conditions) and automated tuning (setting an alarm threshold) to assure that the fault detection (FD)-methods are accessible to the operators. The results showed that automatic usage of FD-methods is difficult, especially in terms of automatic tuning of alarm thresholds when small training datasets only represent the normal conditions, i.e. clean sensors. Despite the challenging set-up, two FD-methods successfully improved the detection limit to 0.5 mg DO/L bias caused by biofilm formation. We showed that the studied dataset could be interpreted equally well by simpler FD-methods, as by advanced machine learning algorithms. This in turn indicates that the information contained in the actively generated data was more vital than its interpretation by advanced algorithms.

Place, publisher, year, edition, pages
PERGAMON-ELSEVIER SCIENCE LTD , 2019. Vol. 166, article id 115029
Keywords [en]
Active fault detection, Monitoring, Receiver operating characteristics, Gaussian process regression, One-class classification
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:uu:diva-397583DOI: 10.1016/j.watres.2019.115029ISI: 000493221600037PubMedID: 31541793OAI: oai:DiVA.org:uu-397583DiVA, id: diva2:1372869
Available from: 2019-11-25 Created: 2019-11-25 Last updated: 2019-11-25Bibliographically approved

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Samuelsson, OscarCarlsson, Bengt

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