Big Data Approach Application for Steel Pipelines in the Conditions of Corrosion Fatigue

This paper presents results of the use of Big Data approach and neural network for the pipelines diagnosis problem. In this case the pipeline is in the conditions of crack growth of corrosion fatigue and exposed to hydrogen. It is proposed to use graphene protective coatings. The mathematical model...

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Main Authors: Skrynkovskyy R. M., Yuzevych L. V., Ogirko O. I., Pawlowski G.
Format: Article
Language:English
Published: Sumy State University 2018-11-01
Series:Журнал інженерних наук
Subjects:
Online Access:http://jes.sumdu.edu.ua/?page_id=27291
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author Skrynkovskyy R. M.
Yuzevych L. V.
Ogirko O. I.
Pawlowski G.
author_facet Skrynkovskyy R. M.
Yuzevych L. V.
Ogirko O. I.
Pawlowski G.
author_sort Skrynkovskyy R. M.
collection DOAJ
description This paper presents results of the use of Big Data approach and neural network for the pipelines diagnosis problem. In this case the pipeline is in the conditions of crack growth of corrosion fatigue and exposed to hydrogen. It is proposed to use graphene protective coatings. The mathematical model for estimating the changes in the effective surface energy of WPL during plastic deformation, electrochemical overstrain, polarization potential and current density of the metal dissolution reaction at the top of the crack on the pipeline surface during its mechanical loading in an aqueous electrolyte solution is given. The dissolution of the metal is considered on the juvenile surface, taking into account the anode and cathode regions based on the approaches of surface physics and electrochemistry. An element of a mathematical model is a quality functional, taking into account information flows and a sensitivity coefficient. Functional quality is used to specify the feedback between the investment project methodology and risk estimates, as well as to optimize the information flows of enterprises and improve the system of protection of metallic underground pipelines that operate under conditions of corrosion fatigue. The purpose of this project is to improve the relevant regulatory and technical documents as well as software.
format Article
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institution OA Journals
issn 2312-2498
2414-9381
language English
publishDate 2018-11-01
publisher Sumy State University
record_format Article
series Журнал інженерних наук
spelling doaj-art-e269c322c01c4fb6a206d4fc2e95a5272025-08-20T02:21:14ZengSumy State UniversityЖурнал інженерних наук2312-24982414-93812018-11-0152E27E3210.21272/jes.2018.5(2).e6Big Data Approach Application for Steel Pipelines in the Conditions of Corrosion FatigueSkrynkovskyy R. M.0Yuzevych L. V.1Ogirko O. I.2Pawlowski G.3Lviv University of Business and Law, 99 Kulparkіvska St., 79021 Lviv, UkraineLviv Polytechnic National University, 12 Stepana Bandery St., 79013 Lviv, UkraineLviv State University of Internal Affairs, 26 Horodotska St., 79007 Lviv, UkraineZaklad Handlowo-Uslugowy BHP, 17 Kostrzynska St., 69-113 Gorzyca, PolandThis paper presents results of the use of Big Data approach and neural network for the pipelines diagnosis problem. In this case the pipeline is in the conditions of crack growth of corrosion fatigue and exposed to hydrogen. It is proposed to use graphene protective coatings. The mathematical model for estimating the changes in the effective surface energy of WPL during plastic deformation, electrochemical overstrain, polarization potential and current density of the metal dissolution reaction at the top of the crack on the pipeline surface during its mechanical loading in an aqueous electrolyte solution is given. The dissolution of the metal is considered on the juvenile surface, taking into account the anode and cathode regions based on the approaches of surface physics and electrochemistry. An element of a mathematical model is a quality functional, taking into account information flows and a sensitivity coefficient. Functional quality is used to specify the feedback between the investment project methodology and risk estimates, as well as to optimize the information flows of enterprises and improve the system of protection of metallic underground pipelines that operate under conditions of corrosion fatigue. The purpose of this project is to improve the relevant regulatory and technical documents as well as software.http://jes.sumdu.edu.ua/?page_id=27291gas pipelinemonitoringfatigue crackcorrosiondatabasesBig Dataneural networkintelligent softwarehardware
spellingShingle Skrynkovskyy R. M.
Yuzevych L. V.
Ogirko O. I.
Pawlowski G.
Big Data Approach Application for Steel Pipelines in the Conditions of Corrosion Fatigue
Журнал інженерних наук
gas pipeline
monitoring
fatigue crack
corrosion
databases
Big Data
neural network
intelligent software
hardware
title Big Data Approach Application for Steel Pipelines in the Conditions of Corrosion Fatigue
title_full Big Data Approach Application for Steel Pipelines in the Conditions of Corrosion Fatigue
title_fullStr Big Data Approach Application for Steel Pipelines in the Conditions of Corrosion Fatigue
title_full_unstemmed Big Data Approach Application for Steel Pipelines in the Conditions of Corrosion Fatigue
title_short Big Data Approach Application for Steel Pipelines in the Conditions of Corrosion Fatigue
title_sort big data approach application for steel pipelines in the conditions of corrosion fatigue
topic gas pipeline
monitoring
fatigue crack
corrosion
databases
Big Data
neural network
intelligent software
hardware
url http://jes.sumdu.edu.ua/?page_id=27291
work_keys_str_mv AT skrynkovskyyrm bigdataapproachapplicationforsteelpipelinesintheconditionsofcorrosionfatigue
AT yuzevychlv bigdataapproachapplicationforsteelpipelinesintheconditionsofcorrosionfatigue
AT ogirkooi bigdataapproachapplicationforsteelpipelinesintheconditionsofcorrosionfatigue
AT pawlowskig bigdataapproachapplicationforsteelpipelinesintheconditionsofcorrosionfatigue