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Remaining useful life prediction of a piping system using artificial neural networks: A case study

Oil producers or operators such as Shell, Petronas, Petron, Chevron, and Lukoil have always placed their equipment as the highest priority for operations. Still, the study shows that many failures in the facility associated with piping systems lead to billions of dollars� loss. In the oil and gas...

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Main Authors: Shaik, N.B., Pedapati, S.R., B A Dzubir, F.A.
Format: Article
Published: Ain Shams University 2021
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110458516&doi=10.1016%2fj.asej.2021.06.021&partnerID=40&md5=10669608ce663175c15308ffd8b3ff58
http://eprints.utp.edu.my/23714/
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spelling my.utp.eprints.237142021-08-19T09:40:42Z Remaining useful life prediction of a piping system using artificial neural networks: A case study Shaik, N.B. Pedapati, S.R. B A Dzubir, F.A. Oil producers or operators such as Shell, Petronas, Petron, Chevron, and Lukoil have always placed their equipment as the highest priority for operations. Still, the study shows that many failures in the facility associated with piping systems lead to billions of dollars� loss. In the oil and gas industry, these piping systems are subjected to various failure mechanisms since it has been operated in various processes and harsh geographical environment. Most of the piping systems are susceptible to corrosion caused by several factors, as reported in the literature. Corrosions of the piping system weakened the piping strength as well as its fittings, thus reducing its ability to withstand the fluctuation of temperature and pressure generated towards the piping system. This work focussed on the factors that contribute to the life of the piping system based on the real-time risk inspection data that were obtained from PETRONAS facilities. The parameters considered were pressure, corrosion, wall thinning, age, nominal thickness, outer radius, and product type. The neural network model has been developed to predict the remaining useful life of piping based on the selected parameters. The proposed model showed promising results of R2 value 0.99, which is close to 1.0, and the validation accuracy of a model was found 97.51 when compared with the actual data. The deterioration trends of individual factors considered in this study are generated to know the effect on pipe life conditions. This work may help oil and gas companies in determining the Fitness For service (FFS) of the piping system by estimating the life of the piping system affected by various corrosion phenomena. © 2021 THE AUTHORS Ain Shams University 2021 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110458516&doi=10.1016%2fj.asej.2021.06.021&partnerID=40&md5=10669608ce663175c15308ffd8b3ff58 Shaik, N.B. and Pedapati, S.R. and B A Dzubir, F.A. (2021) Remaining useful life prediction of a piping system using artificial neural networks: A case study. Ain Shams Engineering Journal . http://eprints.utp.edu.my/23714/
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 Oil producers or operators such as Shell, Petronas, Petron, Chevron, and Lukoil have always placed their equipment as the highest priority for operations. Still, the study shows that many failures in the facility associated with piping systems lead to billions of dollars� loss. In the oil and gas industry, these piping systems are subjected to various failure mechanisms since it has been operated in various processes and harsh geographical environment. Most of the piping systems are susceptible to corrosion caused by several factors, as reported in the literature. Corrosions of the piping system weakened the piping strength as well as its fittings, thus reducing its ability to withstand the fluctuation of temperature and pressure generated towards the piping system. This work focussed on the factors that contribute to the life of the piping system based on the real-time risk inspection data that were obtained from PETRONAS facilities. The parameters considered were pressure, corrosion, wall thinning, age, nominal thickness, outer radius, and product type. The neural network model has been developed to predict the remaining useful life of piping based on the selected parameters. The proposed model showed promising results of R2 value 0.99, which is close to 1.0, and the validation accuracy of a model was found 97.51 when compared with the actual data. The deterioration trends of individual factors considered in this study are generated to know the effect on pipe life conditions. This work may help oil and gas companies in determining the Fitness For service (FFS) of the piping system by estimating the life of the piping system affected by various corrosion phenomena. © 2021 THE AUTHORS
format Article
author Shaik, N.B.
Pedapati, S.R.
B A Dzubir, F.A.
spellingShingle Shaik, N.B.
Pedapati, S.R.
B A Dzubir, F.A.
Remaining useful life prediction of a piping system using artificial neural networks: A case study
author_facet Shaik, N.B.
Pedapati, S.R.
B A Dzubir, F.A.
author_sort Shaik, N.B.
title Remaining useful life prediction of a piping system using artificial neural networks: A case study
title_short Remaining useful life prediction of a piping system using artificial neural networks: A case study
title_full Remaining useful life prediction of a piping system using artificial neural networks: A case study
title_fullStr Remaining useful life prediction of a piping system using artificial neural networks: A case study
title_full_unstemmed Remaining useful life prediction of a piping system using artificial neural networks: A case study
title_sort remaining useful life prediction of a piping system using artificial neural networks: a case study
publisher Ain Shams University
publishDate 2021
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110458516&doi=10.1016%2fj.asej.2021.06.021&partnerID=40&md5=10669608ce663175c15308ffd8b3ff58
http://eprints.utp.edu.my/23714/
_version_ 1738656511246204928
score 13.223943

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