A Prediction Method of Pipeline Corrosion Defects Based on Optimal Neural Network

Abstract

The invention discloses a pipeline corrosion defect prediction method based on an optimized neural network. The steps are as follows: collecting corrosion defect information in the pipeline, conditions of the conveying medium, pipeline body and operating parameters; integrating, cleaning and converting the data to form corrosion defect data Training and testing samples; initializing the BP neural network model; optimizing the neural network model based on hidden layer neuron value optimization, limited storage BFGS algorithm and learning rate adaptive dynamic adjustment, and obtaining the optimal neural network prediction model through sample training and testing; The transmission medium conditions, pipeline body and operating parameters of the pipeline to be predicted are input into the prediction model, and the circumferential distribution and size of corrosion defects in the pipeline are predicted. The invention fully considers the influence factors of pipeline internal corrosion, optimizes the neural network structure, storage space, iteration speed and stability, and provides a reliable reference basis for the remaining life prediction, corrosion failure risk assessment and repair of natural gas pipelines in service.

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 testing...

AI Technical Summary

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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