Pipeline assembly detection system and detection method

Abstract

The invention discloses a pipeline component detection system. The system comprises a distance measurement module, a suspected region generation module and a feature module which are constructed by aneural network, and a component feature sample library serving as a pipeline component category comparison and identification reference, the suspected area generation module inputs to-be-identified pipeline magnetic flux leakage data and generates a pipeline assembly suspected area; the feature module is used for extracting magnetic flux leakage data features of the pipeline assembly; the method comprises the steps of inputting sample magnetic flux leakage data of various pipeline assembly categories and generating a feature sample library, wherein the magnetic flux leakage data of a suspectedarea of the pipeline assembly is input, and suspected pipeline assembly characteristics are output; and the distance measurement module calculates the similarity between the suspected pipeline component characteristics and each component characteristic sample in the component characteristic sample library, and judges the type of the pipeline component according to the value of the similarity. Theinvention further provides a pipeline assembly detection method. According to the invention, the problem of low detection accuracy under the condition of insufficient samples is overcome, and the detection accuracy of the actual pipeline assembly is improved.

Description

technical field [0001] The invention relates to the technical field of artificial intelligence pipeline component fault diagnosis, in particular to a pipeline component detection system and detection method. Background technique [0002] At present, pipeline transportation is the main transportation mode of oil and natural gas. In-service pipelines have been in harsh working environments such as mud or seawater for a long time, and the corrosion area of ​​the pipeline needs to be repaired regularly. Locating the exact location of a corrosion area in a pipeline can be very challenging during pipeline corrosion repair operations. Especially in the case of long-distance pipelines, there is a large error in using absolute mileage to locate the corrosion zone. In actual engineering, the relative distance between the corrosion area and the pipeline components is often used to locate it. Therefore, accurate identification of components of oil and gas pipelines is crucial. [00...

AI Technical Summary

Problems solved by technology

However, for pipeline MFL signals, the amount of data is very limited

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