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

Abstract

This article presents the development of a method for identifying non-standard errors in control the technological process of maintenance petroleum equipment using intelligent methods. There is stated a new formulation of the technological process control problem for the maintenance of petroleum equipment as the task of classifying errors introduced by means of measuring the parameters of the technological process. Intellectual methods have proven themselves as a tool for solving the problem of classification. Various intellectual methods were considered for solving the classification problem: decision trees, artificial neural networks and fuzzy logic method. Effectiveness comparison of the proposed methods is carried out on the basis of experimental data on actual technological processes of petroleum equipment maintenance. The results of the study indicate that the method based on artificial neural networks is the most efficient in solving the problem of classifying the errors of measuring instruments. The proposed method of managing the technological process of petroleum equipment maintenance is intended primarily to improve the repair quality of such equipment components as the pipeline system for transferring hydrocarbon raw materials. Using the proposed method will improve the quality of maintenance work; improve the durability of the pipeline system, which in turn can increase the efficiency of hydrocarbon production.

Algorithms for selecting the operating mode of the technological process of waveguide paths induction brazing

“…In this case, we consider artificial neural networks, fuzzy regulator, and neural fuzzy regulator. The article [21] solves a rather similar problem. The difference in this paper is that another set of sets has been chosen as the descriptive characteristics of the classification object.…”

“…The studies presented in [12] show that the most significant factor influencing the quality of induction brazing process control is the initial setting of process parameters for the induction brazing process. The method of making technological decisions presented in this paper focuses primarily on the initial setting of the induction brazing process, which is due to the choice of new input parameters different from those used in the work [21]. The combined use of the methods proposed in this paper and in paper [21] will make it possible to cover most variations in the induction brazing process, which will significantly improve the quality of induction brazing process management.…”

The Use of Collections of Artificial Neural Networks to Improve the Control Quality of the Induction Soldering Process

“…In the previous works [ 15 , 16 ], the authors investigated the application of the following data analysis methods to solve the problem of controlling the technological process of induction soldering: decision trees [ 16 ], artificial neural networks [ 15 , 16 ], fuzzy logic [ 15 , 16 ], and neuro-fuzzy controller [ 15 ]. According to the results of the experiments, artificial neural networks (ANN) showed the best accuracy.…”

“…According to the results of the experiments, artificial neural networks (ANN) showed the best accuracy. This led to the choice of such a technology for solving the problems posed in this study, the datasets in which are similar to the data in works [ 15 , 16 ].…”

Автоматизация Математического Моделирования Технологического Процесса Индукционной Пайки

“…Однако применение таких высоко технологичных методов формирования неразъѐмных соединений усложняется наличием ряда внешних факторов, наибольшую сложность из которых представляют: влияние человеческого фактора [4][5]. Выше обозначенные проблемы управления современными процессами формирования неразъемных соединений могут быть решены в результате внедрения интеллектуальных технологий обработки информации и принятия решений в условиях неопределенности, что позволит проводить оценку достоверности получаемой из зоны нагрева информации, оценивать погрешности средств измерения и формировать адекватное управление технологическим процессом с целью повышения его точности и повторяемости [6][7].…”