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Pipeline corrosion defect prediction method based on optimized neural network

A neural network and prediction method technology, applied in the field of oil and gas pipelines, can solve the problems of neural network model instability, reduce algorithm convergence speed, reduce training efficiency, etc., achieve the goals of increasing learning rate and accuracy, improving stability, and reducing detection frequency Effect

Active Publication Date: 2021-08-10
SOUTHWEST PETROLEUM UNIV
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Problems solved by technology

For the BP neural network, the setting of its structure has not yet formed a complete theoretical guidance system, especially for the number of neurons in the hidden layer, most of them rely on empirical settings. If the number is set too much, the training efficiency will be reduced and over-fitting will occur. convergence phenomenon, and too few settings may cause the algorithm not to converge
In addition, the traditional BP neural network model generally uses the gradient descent method to update the weight value and threshold of the network. Its iterative calculation process often needs to store a large number of high-dimensional matrices, which reduces the convergence speed of the algorithm; and the learning rate is used to adjust the convergence speed of the algorithm. One of the important factors, if the value is too small, it will also reduce the convergence speed of the algorithm, but if the value is too large, it will cause the instability of the neural network model

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  • Pipeline corrosion defect prediction method based on optimized neural network
  • Pipeline corrosion defect prediction method based on optimized neural network
  • Pipeline corrosion defect prediction method based on optimized neural network

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Embodiment Construction

[0041] In order to enable those skilled in the art to better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described The embodiments are only some of the embodiments of the present application, but not all of them. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without creative efforts shall fall within the scope of protection of this application.

[0042] refer to figure 1 , a method for predicting pipeline corrosion defects based on an optimized neural network provided by an embodiment of the present invention includes the following steps:

[0043] Step 1. Collect the corrosion defect information in the pipeline and the transport medium conditions of the pipeline to which it belo...

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Abstract

The invention discloses a pipeline corrosion defect prediction method based on an optimized neural network. The method comprises the following steps: collecting corrosion defect information in a pipeline, conveying medium conditions, a pipeline body and operation parameters; integrating, cleaning and converting the data to form a corrosion defect data training and testing sample; initializing a BP neural network model; adaptively and dynamically adjusting and optimizing the neural network model based on hidden layer neuron value optimization, a finite storage BFGS algorithm and a learning rate, and obtaining an optimal neural network prediction model through sample training and testing; conveying medium conditions, a pipeline body, operation parameters and the like of a to-be-predicted pipeline are input into the prediction model, and the annular distribution and the size of the corrosion defects in the pipeline are obtained through prediction. According to the method, corrosion influence factors in the pipeline are fully considered, the neural network structure, the storage space, the iteration speed and the stability are optimized, and a reliable reference basis is provided for residual life prediction, corrosion failure risk assessment, repair and the like of the in-service natural gas pipeline.

Description

technical field [0001] The invention belongs to the technical field of oil and gas pipelines, in particular to a pipeline corrosion defect prediction method based on an optimized neural network. Background technique [0002] In the oil and natural gas industry, corrosion usually causes a large number of defects such as thinning and cracks on the surface of the pipeline, which not only reduces the pressure bearing capacity of the pipeline, but also easily causes major safety accidents such as gas leakage or explosion, which seriously threatens the safety of the natural gas pipeline itself. Accurately grasping the location distribution and size characteristics of corrosion defects in natural gas pipelines in service has important guiding significance for the risk assessment of pipeline corrosion failure and the adoption of reasonable repair measures. [0003] At present, the corrosion defects of in-service pipelines are mainly obtained by means of magnetic flux leakage testi...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/18G06F30/27G06N3/08G06F113/14
CPCG06F30/18G06F30/27G06N3/084G06F2113/14
Inventor 贾文龙杨帆李长俊吴瑕宋硕硕张员瑞林友志李晓宇
Owner SOUTHWEST PETROLEUM UNIV
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