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

Author(s): Skrynkovskyy R. M.1, Yuzevych L. V.2, 3, Ogirko O. I.4, Pawlowski G.5*

1 Lviv University of Business and Law, 99 Kulparkіvska St., 79021 Lviv, Ukraine;
2 Lviv Polytechnic National University, 12 Stepana Bandery St., 79013 Lviv, Ukraine;
3 Karpenko Physico-Mechanical Institute of the NAS of Ukraine, 5 Naukova St., 79060 Lviv, Ukraine;
4 Lviv State University of Internal Affairs, 26 Horodotska St., 79007 Lviv, Ukraine;
5 Zaklad Handlowo-Uslugowy BHP, 17 Kostrzynska St., 69-113 Gorzyca, Poland

*Corresponding Author’s Address: [email protected]

Issue: Volume 5; Issue 2 (2018)

Paper received: May 2, 2018
The final version of the paper received: October 6, 2018
Paper accepted online: October 10, 2018

Skrynkovskyy R. M. Big Data Approach Application for Steel Pipelines in the Conditions of Corrosion Fatigue / R. M. Skrynkovskyy, L. V. Yuzevych, O. I. Ogirko, G. Pawlowski // Journal of Engineering Sciences. – Sumy : Sumy State University, 2018. – Volume 5, Issue 2. – P. E27-E32.

DOI: 10.21272/jes.2018.5(2).e6

Research Area: MECHANICAL ENGINEERING: Computational Mechanics

Abstract. 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.

Keywords: gas pipeline, monitoring, fatigue crack, corrosion, databases, Big Data, neural network, intelligent software, hardware, databases.


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