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Intelligently Informed Control Over the Process Variables of Oil and Gas Equipment Maintenance

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This article details a new technique that uses intelligent methods in order to identify non-standard errors when controlling the technological process of maintenance petroleum equipment. First, a new formulation of the technological process control problem for the maintenance of petroleum equipment is presented. This is stated in terms of classifying errors introduced by measuring the parameters of the technological process. Intelligent methods have been proven as a tool to solve the classification problem. Various machine-learning methods have been considered: decision trees, artificial neural networks (ANN), and fuzzy logic. In this study, an effectiveness comparison of the proposed methods has been conducted using experimental data of petroleum equipment maintenance. Results indicate that ANN is the most efficient method to classify measurement errors. The proposed method will primarily improve repair quality of certain equipment components such as the pipeline system for transferring raw hydrocarbon materials. Moreover, it will improve the quality of maintenance work and durability of the pipeline system, which in turn can increase the hydrocarbon production efficiency.
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Equipment Maintenance; Induction Brazing; Measurement Errors; Intelligent Analysis; Petroleum Equipment

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C. M. Piovesan, J. B. Kozman, Method, System and Apparatus for Intelligent Management of Oil and Gas Platform Surface Equipment. Patent 8676721 USA, 2014.

L. S. Moiseeva, Carbon Dioxide Corrosion of Oil and Gas Field Equipment, Protection of Metals, Vol. 41(Issue 1):76-83, January 2005.

M. Rahimi, M. Rausand, S. Wu, Reliability prediction of offshore oil and gas equipment for use in an arctic environment, International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, pp. 81–86, Xi'an, China, June 2011.

M. Zemenkova, V. Shalay, Yu. Zemenkov, E. Kurushina, Improving the Efficiency of Administrative Decision-Making when Monitoring Reliability and Safety of Oil and Gas Equipment, MATEC Web of Conferences, Vol. 73, August 2016.

A. D. Kersey, Optical Fiber Sensors for Permanent Downwell Monitoring Applications in the Oil and Gas Industry, IEICE Transactions on Electronics, Vol. 83(Issue 3):400-404, March 2000.

Q. Queitsch, Welding Problems in Pipelines, Nominal Dia, 1420, Steel Quality X 65, Energietechnik, Vol. 25(Issue 1):18-21, 1975.

D. S. Fominykh, A. F. Rezchikov, V. A. Kushnikov, V. A. Ivashchenko, A. S. Bogomolov, L. Y. Filimonyuk, O. N. Dolinina, O. V. Kushnikov, T. E. Shulga, V. A. Tverdokhlebov, Problem of Quality Assurance during Metal Constructions Welding via Robotic Technological Complexes, Journal of Physics: Conference Series, Vol. 1015(Issue 3):032169, May 2018.

P. Gierth, L. Rebenklau, A. Michaelis, Evaluation of soldering processes for high efficiency solar cells, 35th International Spring Seminar in Electronics Technology, pp. 133–137, New York, NY, May 2012.

E. E. Mazón-Valadez, A. Hernández-Sámano, J. C. Estrada-Gutiérrez, J. Ávila-Paz, M. E. Cano-González, Developing a Fast Cordless Soldering Iron via Induction Heating, Dyna, Vol. 81(Issue 188):166-173, December 2014.

F. Nishimura, H. Nakamura, H. Takahashi, T. Takamoto, Development of a New Investment for High-Frequency Induction Soldering, Dental Materials Journal, Vol. 11(Issue 1):59-69, June 1992.

A. V. Murygin, V. S. Tynchenko, V. D. Laptenok, O. A. Emilova, A. N. Bocharov, Complex of Automated Equipment and Technologies for Waveguides Soldering Using Induction Heating, IOP Conference Series: Materials Science and Engineering, Vol. 173(Issue 1):012023, February 2017.

V. S. Tynchenko, A. V. Murygin, O. A. Emilova, A. N. Bocharov, V. D. Laptenok, The Automated System for Technological Process of Spacecraft's Waveguide Paths Soldering, IOP Conference Series: Materials Science and Engineering, Vol. 155(Issue 1):012007, November 2016.

A. V. Murygin, V. S. Tynchenko, V. D. Laptenok, O. A. Emilova, Yu. N. Seregin, Modeling of Thermal Processes in Waveguide Tracts Induction Soldering, IOP Conference Series: Materials Science and Engineering, 173(Issue 1):012026, February 2017.

Y. Baskin, Brazing of Carbide Mining Tools, Coal Age, Vol. 117(Issue 2):46-47, February 2012.

D. Ernens, H. Hariharan, W. M. Van Haaften, H. R. Pasaribu, M. Jabs, R. N. McKim, Improving Casing Integrity with Induction Brazing of Casing Connections, SPE Drilling and Completion, Vol. 33(Issue 3):241-251, September 2018.

J. Friedman, T. Hastie, R. Tibshirani, The Elements of Statistical Learning (Springer, 2001, pp. 337-387).

I. Witten, E. Frank, M. Hall, Ch. Pal, Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann, 2016).

T. Mitchell, Machine Learning (McGraw-Hill Science/ Engineering/Math, 1997).

D. T. Larose, C. D. Larose, Discovering Knowledge in Data: An Introduction to Data Mining (IEEE Computer Society, Wiley, 2005, pp. 90-106).

L. Breiman, Classification and Regression Trees (Routledge, 2017).

Hanumanthakari, S., Kodad, S., Botlaguduru, S., Sensorless Direct Torque Control of Induction Motor Using AI Based Duty Ratio Controllers, (2016) International Review on Modelling and Simulations (IREMOS), 9 (5), pp. 339-347.

B. D. Ripley, Pattern Recognition and Neural Networks (Cambridge University Press, 1996).

M. I. Jordan, D. E. Rumelhart, Forward Models: Supervised Learning with a Distal Teacher, Cognitive Science, Vol. 16(Issue 3):307-354, July 1992.

N. M. Nasrabadi, Pattern Recognition and Machine Learning, Journal of Electronic Imaging, Vol. 16(Issue 4):049901, October 2007.

R. Lippmann, An Introduction to Computing with Neural Nets, IEEE ASSP Magazine, Vol. 4(Issue 2):4-22, May 1987.

L. Fausett, Fundamentals of Neural Networks: Architectures, Algorithms, and Applications (Prentice-Hall, 1994).

M. M. Nelson, W. T. Illingworth, A Practical Guide to Neural Nets (Addison-Wesley Publishing Company, Inc., 1991).

W. Y. Loh, Classification and Regression Trees, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Vol. 1(Issue 1):14-23, January 2011.

W. S. McCulloch, W. H. Pitts, A Logical Calculus of the Ideas Immanent in Nervous Activity, Bulletin of Mathematical Biophysics, Vol. 5(Issue 4):115-133, December 1943.

W. S. McCulloch, W. H. Pitts, A Heterarchy of Values Determined by the Topology of Nervous Nets, Bulletin of Mathematical Biophysics, Vol. 7(Issue 2):89-93, June 1945.

Hannane, A., Fizazi, H., Metaheuristics and Neural Network for Satellite Images Classification, (2016) International Review of Aerospace Engineering (IREASE), 9 (4), pp. 107-113.

Benyoucef, M., Bounaama, F., Belkacem, D., Optimization of ANN Adaptation Time for the Modeling of Greenhouse Climate Using Wavelet Transform, (2016) International Review on Modelling and Simulations (IREMOS), 9 (1), pp. 37-43.

Mishra, M., Rout, P., Time-Frequency Analysis based Approach to Islanding Detection in Micro-grid System, (2016) International Review of Electrical Engineering (IREE), 11 (1), pp. 116-129.

Chartsuk, N., Marungsri, B., Supervision Strategy to Mitigate the Effect of Electric Vehicles (EVs) Charging Load on Power Distribution System Operations, (2018) International Journal on Energy Conversion (IRECON), 6 (6), pp. 184-195.

B. Widrow, R. G. Winter, R. A. Baxter, Layered Neural Nets for Pattern Recognition, IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. 36(Issue 7):1109-1118, July 1988.

G. Klir, B. Yuan, Fuzzy Sets and Fuzzy Logic (Prentice Hall, 1995).

J. Yen, R. Langari, Fuzzy Logic: Intelligence, Control, and Information (Prentice Hall, 1999).

E. H. Mamdani, S. Assilian, An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller, International Journal of Man-Machine Studies, Vol. 7(Issue 1):1-13, January 1975.

Basjaruddin, N., Margana, D., Kuspriyanto, K., Rinaldi, R., Suhendar, S., Hardware Simulation of Advanced Driver Assistance Systems Based on Fuzzy Logic, (2018) International Review on Modelling and Simulations (IREMOS), 11 (1), pp. 24-31.

Monadjemi, S., Moallem, P., Automatic Diagnosis of Particular Diseases Using a Fuzzy-Neural Approach, (2018) International Journal on Engineering Applications (IREA), 6 (1), pp. 29-34.


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