Jafari, R orcid.org/0000-0001-7298-2363, Razvarz, S, Vargas-Jarillo, C et al. (2 more authors) (2021) Leakage Detection for Pipe Systems with Fuzzy Monitoring Strategy. In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI). 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 05-07 Dec 2021, Orlando, FL, USA. IEEE ISBN 978-1-7281-9049-5
Abstract
A Pipe is a ubiquitous product in the industries that is used to convey liquids, gases, or solids suspended in a liquid, e.g., a slurry from one location to another. Both internal and external cracking can result in structural failure of the industrial piping system and possibly decrease the service life of the equipment. The chaos and complexity associated with the uncertain behavior inherent in pipeline systems lead to difficulty in detection and localisation of leaks in real-time. The timely detection of leakage is important in order to reduce the loss rate and serious environmental consequences. To address this issue, in this paper an auto regressive with exogenous input (ARX)-Laguerre fuzzy proportional -derivative (PD) observation system is pro-posed to detect and estimate a leak in pipelines. In this work, the ARX-Laguerre model has been used to generate better performance in the presence of uncertainty. According to the results, the proposed technique can detect leaks accurately and effectively.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 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: | Autoregressive with eXogenous Input Laguerre (ARX-Laguerre) , Fuzzy , Pipeline , PD , Controller , PD observer |
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: | 04 Mar 2022 10:44 |
Last Modified: | 04 Mar 2022 10:44 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/ssci50451.2021.9660103 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:184261 |