Razvarz, S, Jafari, R orcid.org/0000-0001-7298-2363, Alexander, G et al. (1 more author) (2020) Leakage Detection in Pipeline Based on Second Order Extended Kalman Filter Observer. IFAC-PapersOnLine, 52 (29). pp. 116-121. ISSN 2405-8963
Abstract
In this paper, a new technique is proposed in order to detect, locate, as well as approximate the fluid leaks in a straight pipeline (without branching) by taking into consideration the pressure and flow evaluations at the ends of pipeline on the basis of data fusion from two methods: a steady-state approximation and Second-order Extended Kalman Filter (SEKF). The SEKF is on the basis of the second-order Taylor expansion of a nonlinear system unlike to the more popular First-order Extended Kalman Filter (FEKF). The suggested technique in this paper deals with just pressure head and flow rate evaluations at the ends of pipeline that has intrinsic sensor as well as process noise. A simulation example is given for demonstrating the validity of the proposed technique. It shows that the extended Kalman particle filter algorithm on the basis of the second-order Taylor expansion is effective and performs well in decreasing systematic deviations as well as running time.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. This is an open access article under the terms of the Creative Commons CC-BY-NC-ND license. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/. |
Keywords: | Second-order Extended Kalman Filter; Leakage; Leakage detection; Pipeline |
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: | 27 Jan 2020 11:16 |
Last Modified: | 27 Jan 2020 11:16 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.ifacol.2019.12.631 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:156065 |