Fault detection system for long-distance gas mixture pipelines using statistical features

Integrity management of gas pipelines can be enhanced by incorporating state-of-the-art failure detection, diagnostics and prediction tools. A plethora of methods are available for real-time monitoring and leak detection, the majority of which reported for liquid pipelines that can be quickly introd...

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Main Authors: Mujtaba, S.M., Lemma, T.A., Gebremariam, M.A.
Format: Article
Published: Springer Science and Business Media Deutschland GmbH 2020
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091318912&doi=10.1007%2f978-981-15-5753-8_27&partnerID=40&md5=b815ee7bf9ffa79be3e1f03cf9c2c127
http://eprints.utp.edu.my/24687/
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spelling my.utp.eprints.246872021-08-27T05:52:56Z Fault detection system for long-distance gas mixture pipelines using statistical features Mujtaba, S.M. Lemma, T.A. Gebremariam, M.A. Integrity management of gas pipelines can be enhanced by incorporating state-of-the-art failure detection, diagnostics and prediction tools. A plethora of methods are available for real-time monitoring and leak detection, the majority of which reported for liquid pipelines that can be quickly introduced for real-life applications. The present paper, however, proposes dynamic principal component analysis (DPCA) for it has not been tested for gas pipelines under transient conditions. Mass flow rate, temperature and pressure values are used separately and in combined form to establish the reference models. Measured data are projected into the new dimension based on selected principal components. For leak detection, Hotelling�s T2-statistics and Q-statistics are monitored in real time. The validation tests for simple as well as dynamic PCA show that both methods successfully detect a leakage that has an opening of 10 of pipeline diameter. DPCA significantly magnified the information on leak in terms of T2-statistics, thus reducing the probability of missed faults. T2-statistics is found to be more sensitive to small leaks than Q-statistics. Overall, it can be said that the proposed technique has the potential to accurately identify small leaks under transient conditions. © Springer Nature Singapore Pte Ltd 2020. Springer Science and Business Media Deutschland GmbH 2020 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091318912&doi=10.1007%2f978-981-15-5753-8_27&partnerID=40&md5=b815ee7bf9ffa79be3e1f03cf9c2c127 Mujtaba, S.M. and Lemma, T.A. and Gebremariam, M.A. (2020) Fault detection system for long-distance gas mixture pipelines using statistical features. Lecture Notes in Mechanical Engineering . pp. 287-303. http://eprints.utp.edu.my/24687/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description Integrity management of gas pipelines can be enhanced by incorporating state-of-the-art failure detection, diagnostics and prediction tools. A plethora of methods are available for real-time monitoring and leak detection, the majority of which reported for liquid pipelines that can be quickly introduced for real-life applications. The present paper, however, proposes dynamic principal component analysis (DPCA) for it has not been tested for gas pipelines under transient conditions. Mass flow rate, temperature and pressure values are used separately and in combined form to establish the reference models. Measured data are projected into the new dimension based on selected principal components. For leak detection, Hotelling�s T2-statistics and Q-statistics are monitored in real time. The validation tests for simple as well as dynamic PCA show that both methods successfully detect a leakage that has an opening of 10 of pipeline diameter. DPCA significantly magnified the information on leak in terms of T2-statistics, thus reducing the probability of missed faults. T2-statistics is found to be more sensitive to small leaks than Q-statistics. Overall, it can be said that the proposed technique has the potential to accurately identify small leaks under transient conditions. © Springer Nature Singapore Pte Ltd 2020.
format Article
author Mujtaba, S.M.
Lemma, T.A.
Gebremariam, M.A.
spellingShingle Mujtaba, S.M.
Lemma, T.A.
Gebremariam, M.A.
Fault detection system for long-distance gas mixture pipelines using statistical features
author_facet Mujtaba, S.M.
Lemma, T.A.
Gebremariam, M.A.
author_sort Mujtaba, S.M.
title Fault detection system for long-distance gas mixture pipelines using statistical features
title_short Fault detection system for long-distance gas mixture pipelines using statistical features
title_full Fault detection system for long-distance gas mixture pipelines using statistical features
title_fullStr Fault detection system for long-distance gas mixture pipelines using statistical features
title_full_unstemmed Fault detection system for long-distance gas mixture pipelines using statistical features
title_sort fault detection system for long-distance gas mixture pipelines using statistical features
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2020
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091318912&doi=10.1007%2f978-981-15-5753-8_27&partnerID=40&md5=b815ee7bf9ffa79be3e1f03cf9c2c127
http://eprints.utp.edu.my/24687/
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