Method for reproducing two-dimensional defect of petroleum pipeline PSO-BP (Particle Swarm Optimization-Back-Propagation) neural network

Abstract

The invention aims at providing a method for reproducing a two-dimensional defect of a petroleum pipeline PSO-BP (Particle Swarm Optimization-Back-Propagation) neural network. Actually measured pipeline magnetic flux leakage data and pipeline defect data are used as experimental data of defect reconfiguration. The method comprises the steps of: with a magnetic flux leakage signal as input and a defect outline as outlet, setting a particle initial parameter, randomly initializing an initial position and an initial speed of each particle, calculating a particle fitness function numerical value , determining a past best value pbest of each particle and a global best value gbest of the whole particle swarm, updating the position and the speed of each particle, judging whether reaching the maximum iteration time or preset precision, if meeting the weight and the threshold of outputting a neutral network; and otherwise, re-comparing. The neutral network after the weight and the threshold are optimized by using a particle swarm algorithm is used for reproducing the two-dimensional defect of the pipeline and also reproducing a defect outline of the pipeline. According to the invention, the defect that the BP algorithm is easy to fall into a local minimum value can be effectively solved, and the convergence precision is improved, thus the defect of the pipeline is accurately reproduced.

Description

technical field [0001] The invention relates to a method for pipeline defect detection. Background technique [0002] With the rapid development of my country's oil and natural gas industry, pipeline transportation has become the main way of my country's land oil and gas transportation. However, with the increase of pipeline age, construction defects, man-made damage and corrosion, pipeline accidents occur frequently, which not only cause major economic losses, but also seriously pollute the environment and even endanger the lives of production personnel. Magnetic flux leakage detection technology is the most widely used method in pipeline defect detection. It uses ultrasonic, magnetic flux leakage, ray and other flaw detection principles. Without affecting normal production, through the intelligent detector walking in the pipeline, oil and gas Defects of the pipe wall or coating: such as deformation, damage, corrosion, perforation, weight loss of the pipe wall and thicknes...

AI Technical Summary

Problems solved by technology

The inverse problem is very complicated, and a commonly used method to solve the inverse problem is to use an iterative method, but this method is computationally intensive

[0003] BP neural network has the ability to approximate any nonlinear mapping through learning, but the standard BP neural network has the disadvantages of easy to fall into local minimum, slow convergence speed and cause oscillation effect.

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