Failure classification in natural gas pipe-lines using artificial intelligence: A case study

Gas pipelines are often subjected to various kinds of damages such as corrosion, welding failure, and excavation damages, due to harsh environmental conditions. The failure in gas pipelines may lead to catastrophic damages like human life loss, economic loss, etc. Predicting pipeline health is of cr...

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Autores principales:	Abdul Manan, Khurram Kamal, Tahir Abdul Hussain Ratlamwala, Muhammad Fahad Sheikh, Abdul Ghani Abro, Tayyab Zafar
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Artificial intelligence Artificial neural network Failure prediction Gas pipeline Pattern recognition Supervised learning Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/1c6ea21235f3415a8c05cf8e0f9e457c
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id	oai:doaj.org-article:1c6ea21235f3415a8c05cf8e0f9e457c
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spelling	oai:doaj.org-article:1c6ea21235f3415a8c05cf8e0f9e457c2021-11-22T04:27:05ZFailure classification in natural gas pipe-lines using artificial intelligence: A case study2352-484710.1016/j.egyr.2021.10.093https://doaj.org/article/1c6ea21235f3415a8c05cf8e0f9e457c2021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352484721011112https://doaj.org/toc/2352-4847Gas pipelines are often subjected to various kinds of damages such as corrosion, welding failure, and excavation damages, due to harsh environmental conditions. The failure in gas pipelines may lead to catastrophic damages like human life loss, economic loss, etc. Predicting pipeline health is of critical importance to avoid these damages. In this study, 875 incidents are extracted from US DOT PHMSA from 2002 to 2020. For each of the incident, different parameters such as Age, NPS, Wall Thickness, Material, Operating Pressure, Location, and Area is analyzed. Two supervised learning techniques Artificial Neural Networks and Support Vector Machine are used to predict and classify different natural gas pipeline failures i.e. Corrosion, Pipeline Material, or Weld Failure and Excavation Damage by using actual pipeline incident data. One-Way ANOVA F-test is used to select the important features of the input dataset. The supervised models (Backpropagation Neural Network and SVM) are trained and tested on the input data. The performance of the models is assessed based on accurate predictions made by the trained models on the testing dataset. It is observed that Medium Gaussian SVM integrated with ANOVA (and Holdout cross-validation) performs better than other algorithms and yields 74.8% accuracy.Abdul MananKhurram KamalTahir Abdul Hussain RatlamwalaMuhammad Fahad SheikhAbdul Ghani AbroTayyab ZafarElsevierarticleArtificial intelligenceArtificial neural networkFailure predictionGas pipelinePattern recognitionSupervised learningElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENEnergy Reports, Vol 7, Iss , Pp 7640-7647 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Artificial intelligence Artificial neural network Failure prediction Gas pipeline Pattern recognition Supervised learning Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Artificial intelligence Artificial neural network Failure prediction Gas pipeline Pattern recognition Supervised learning Electrical engineering. Electronics. Nuclear engineering TK1-9971 Abdul Manan Khurram Kamal Tahir Abdul Hussain Ratlamwala Muhammad Fahad Sheikh Abdul Ghani Abro Tayyab Zafar Failure classification in natural gas pipe-lines using artificial intelligence: A case study
description	Gas pipelines are often subjected to various kinds of damages such as corrosion, welding failure, and excavation damages, due to harsh environmental conditions. The failure in gas pipelines may lead to catastrophic damages like human life loss, economic loss, etc. Predicting pipeline health is of critical importance to avoid these damages. In this study, 875 incidents are extracted from US DOT PHMSA from 2002 to 2020. For each of the incident, different parameters such as Age, NPS, Wall Thickness, Material, Operating Pressure, Location, and Area is analyzed. Two supervised learning techniques Artificial Neural Networks and Support Vector Machine are used to predict and classify different natural gas pipeline failures i.e. Corrosion, Pipeline Material, or Weld Failure and Excavation Damage by using actual pipeline incident data. One-Way ANOVA F-test is used to select the important features of the input dataset. The supervised models (Backpropagation Neural Network and SVM) are trained and tested on the input data. The performance of the models is assessed based on accurate predictions made by the trained models on the testing dataset. It is observed that Medium Gaussian SVM integrated with ANOVA (and Holdout cross-validation) performs better than other algorithms and yields 74.8% accuracy.
format	article
author	Abdul Manan Khurram Kamal Tahir Abdul Hussain Ratlamwala Muhammad Fahad Sheikh Abdul Ghani Abro Tayyab Zafar
author_facet	Abdul Manan Khurram Kamal Tahir Abdul Hussain Ratlamwala Muhammad Fahad Sheikh Abdul Ghani Abro Tayyab Zafar
author_sort	Abdul Manan
title	Failure classification in natural gas pipe-lines using artificial intelligence: A case study
title_short	Failure classification in natural gas pipe-lines using artificial intelligence: A case study
title_full	Failure classification in natural gas pipe-lines using artificial intelligence: A case study
title_fullStr	Failure classification in natural gas pipe-lines using artificial intelligence: A case study
title_full_unstemmed	Failure classification in natural gas pipe-lines using artificial intelligence: A case study
title_sort	failure classification in natural gas pipe-lines using artificial intelligence: a case study
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/1c6ea21235f3415a8c05cf8e0f9e457c
work_keys_str_mv	AT abdulmanan failureclassificationinnaturalgaspipelinesusingartificialintelligenceacasestudy AT khurramkamal failureclassificationinnaturalgaspipelinesusingartificialintelligenceacasestudy AT tahirabdulhussainratlamwala failureclassificationinnaturalgaspipelinesusingartificialintelligenceacasestudy AT muhammadfahadsheikh failureclassificationinnaturalgaspipelinesusingartificialintelligenceacasestudy AT abdulghaniabro failureclassificationinnaturalgaspipelinesusingartificialintelligenceacasestudy AT tayyabzafar failureclassificationinnaturalgaspipelinesusingartificialintelligenceacasestudy
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Failure classification in natural gas pipe-lines using artificial intelligence: A case study

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