Razvarz, S, Jafari, R orcid.org/0000-0001-7298-2363, Vargas-Jarillo, C et al. (2 more authors) (2021) Pipeline Leak Detection and Location based on Fuzzy Controller. In: Proceedings of 2020 IEEE Symposium Series on Computational Intelligence (SSCI). 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 01-04 Dec 2020, Canberra, Australia. IEEE , pp. 1944-1949. ISBN 978-1-7281-2547-3
Abstract
Pipeline systems have been taken as one of the most important tools for transmission around the world. It will be important for the industrial society that pipeline systems function appropriately by taking into consideration the growing requirement for effective interconnecting fluid networks. Typically, there are various kinds of control systems or algorithms for pipeline leak detection. In this paper, a pipeline leak detection and location method is proposed based on fuzzy control. It is concluded that the response of the fuzzy controller is fast and has no oscillations and this is appropriate for numerous high- precision usages like robotics as well as weapon systems. The numerical results demonstrate that the suggested technique is simple, efficient, and has a high level of localization precision.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | pipeline systems, fuzzy controller, pipeline leak detection |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Arts, Humanities and Cultures (Leeds) > School of Design (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 26 Oct 2020 13:24 |
Last Modified: | 17 Aug 2023 20:40 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/SSCI47803.2020.9308314 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:167160 |