A large number of oil and gas pipelines in the Russian Federation have been in operation for over 20 years. For these pipelines, the issue of assessing the residual resource is relevant. Today, much attention is paid to the problem of long-term durability of pipelines. Trunk pipelines are under the influence of cyclic loads and influences arising during operation. The acting stresses in the pipe wall do not exceed the allowable ones, however, they cause micro-damage to the metal structure. When assessing the cyclic fatigue of a metal, the main criterion is the relative damage to the metal. The use of non-destructive testing methods (ultrasonic and magnetic), as well as the establishment of a relationship between the number of cycles and diagnostic parameters, will improve the accuracy of the residual life assessment. When analyzing several diagnostic parameters, the question of data interconnection becomes relevant. Since establishing an empirical or semi-empirical relationship between ultrasonic and magnetic properties is a complex task, artificial neural networks (ANNs) can be used to solve this problem. The use of ANN in the diagnostics of trunk pipelines will increase the accuracy of the assessment and eliminate the subjectivity of data interpretation.
Today, the assessment of the technical condition of objects taking into account the effects of variable loads is gaining popularity in the world. As a rule, such loads do not lead to simultaneous destruction of structures. Such facilities include trunk pipelines. The variable loads arising in them are determined by the technological mode of operation. For main pipelines, a low-cycle load is characteristic, for gas pipelines, a multi-cycle load. During cyclic impact on the metal, its slight damage occurs at the micro level, which is almost impossible to detect without special diagnostic methods. Microdamage in the metal accumulates in the form of microcracks and, with an increase in the number of loading cycles, the number of microcracks increases several times, which can ultimately cause macrocracks and lead to design failure. When analyzing accidents that occurred on pipelines, the fact that failure occurred without the presence of characteristic crack-like defects in the pipe wall is often noted. Thus, fatigue damage to the metal is a dangerous factor that can lead to the destruction of pipelines. Given the new trends in diagnostics, one of the key issues is the assessment of damage to prevent accidents on main pipelines. The article considers the study of the dependence of the acoustic and magnetic properties of specimens from 09G2S steel loaded in the equivalent mode of the main gas pipeline, depending on the level of accumulated damage.
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