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Data-Driven Fault Diagnosis of Once-through Benson Boilers
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). (ERES;DISA-SIG)
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). (ERES;DISA-SIG)ORCID iD: 0000-0002-2833-7196
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). (ERES;DISA-SIG)ORCID iD: 0000-0001-6981-0966
University of Naples Federico II, Italy.
2019 (English)In: 2019 4th International Conference on System Reliability and Safety (ICSRS), IEEE Press, 2019, p. 345-354Conference paper, Published paper (Refereed)
Abstract [en]

Fault diagnosis (FD) of once-through Benson boilers, as a crucial equipment of many thermal power plants, is of paramount importance to guarantee continuous performance. In this study, a new fault diagnosis methodology based on data-driven methods is presented to diagnose faults in once-through Benson boilers. The present study tackles this issue by adopting a combination of data-driven methods to improve the robustness of FD blocks. For this purpose, one-class versions of minimum spanning tree and K-means algorithms are employed to handle the strong interaction between measurements and part load operation and also to reduce computation time and system training error. Furthermore, an adaptive neuro-fuzzy inference system algorithm is adopted to improve accuracy and robustness of the proposed fault diagnosing system by fusion of the output of minimum spanning tree (MST) and K-means algorithms. Performance of the presented scheme against six major faults is then assessed by analyzing several test scenario.

Place, publisher, year, edition, pages
IEEE Press, 2019. p. 345-354
Keywords [en]
fault diagnosis;once-through boiler;data-driven;data fusion;MST;K-means;ANFIS
National Category
Computer Sciences
Research subject
Computer and Information Sciences Computer Science
Identifiers
URN: urn:nbn:se:lnu:diva-92153DOI: 10.1109/ICSRS48664.2019.8987699ISI: 000545634000053Scopus ID: 2-s2.0-85081092030ISBN: 978-1-7281-4781-9 (print)ISBN: 978-1-7281-4780-2 (print)OAI: oai:DiVA.org:lnu-92153DiVA, id: diva2:1393903
Conference
2019 4th International Conference on System Reliability and Safety (ICSRS), Rome, November 20-22, 2019
Available from: 2020-02-17 Created: 2020-02-17 Last updated: 2022-04-12Bibliographically approved

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Saman Azari, MehdiFlammini, FrancescoCaporuscio, Mauro

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