Intelligent identification method for pipeline defect on basis of RBF (Radical Basis Function) neural network

A neural network and intelligent identification technology, which is applied in the field of intelligent identification of oil and gas pipeline defects, can solve problems such as difficult to meet requirements, complex oil and gas pipeline defects, and difficult to find the corresponding relationship of magnetic flux leakage signals.

Inactive Publication Date: 2011-07-13
HARBIN ENG UNIV
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Problems solved by technology

Quantify the geometric size of defects by using empirical formulas. This method is available when the requirements for quantification accuracy are not very high, but for high-precision defect quantification, this method is difficult to meet the requirements.
Defect reconstruction based on the inverse equation of the magnetic dipole, this method has a great advantage in identifying defects with simple shapes, but the

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  • Intelligent identification method for pipeline defect on basis of RBF (Radical Basis Function) neural network
  • Intelligent identification method for pipeline defect on basis of RBF (Radical Basis Function) neural network
  • Intelligent identification method for pipeline defect on basis of RBF (Radical Basis Function) neural network

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[0014] The RBF neural network-based intelligent identification method for pipeline defects of the present invention will be described in detail below in conjunction with the embodiments. The pipeline defect intelligent identification method based on RBF neural network of the present invention comprises the following steps:

[0015] (1) Obtain the pipeline defect magnetic flux leakage and pipeline defect contour data.

[0016] The pipeline defect size obtained in the embodiment of the present invention is the length, width and depth of the pipeline. The process of predicting the geometric parameters of defects based on the magnetic flux leakage signals generated by defects is essentially a process of establishing the mapping relationship between magnetic flux leakage signals and geometric parameters of defects. Divide all samples into training sample set and test sample set. The length, width and depth of the sample are shown in Table 6.1.

[0017] The MFL values ​​obtained ...

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Abstract

The invention provides an intelligent identification method for a pipeline defect on the basis of an RBF (Radical Basis Function) neural network, comprising the following steps of: (1) obtaining a pipeline defect flux leakage signal and a pipeline defect outline as detection data; (2) building an RBF neural network; (3) training and testing the neural network; and (4) predicting the pipeline defect outline by the tested neural network. The pipeline defect outline comprises the length, the width and the depth of the pipeline. With the intelligent identification method, finite tests are carried out, thus a pipeline defect outline prediction model is built. A computer simulation test is carried out for scientific prediction, and the pipeline defect outline can be accurately and quickly predicted.

Description

technical field [0001] The invention relates to an intelligent identification method for oil and gas pipeline defects, in particular to an RBF neural network-based intelligent identification method for oil and gas pipeline defects that can accurately and quickly identify pipeline defects. Background technique [0002] It is a difficult point in the research of magnetic flux leakage detection to reflect the characteristic parameters and contours of defects through the measured magnetic flux leakage signals. The general solution is to analyze the relationship between the defect parameters and the magnetic flux leakage signal, and calculate the relevant information of the defect according to the characteristics of the signal. The method of empirical formula is used to quantify the geometric size of defects. This method is available when the requirements for quantification accuracy are not very high, but for high-precision defect quantification, this method is difficult to meet ...

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Application Information

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IPC IPC(8): G06K9/62G06N3/02
Inventor 刘胜刘杨李冰
Owner HARBIN ENG UNIV
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