Irregular pipeline defect inversion method based on improved particle swarm optimization algorithm

Abstract

The invention discloses an irregular pipeline defect inversion method based on an improved particle swarm optimization algorithm. The invention belongs to the technical field of pipeline monitoring. The irregular pipeline defect inversion method adopts an improved particle swarm algorithm, introduces the concept of personalized inertia weight, respectively compares the fitness of each particle with the average fitness of the population to judge the advantages and disadvantages of the current position of each particle, on the basis of the uniform inertia weight based on the BPSO, reduces the inertia weight of the particle with the optimal position, and increases the inertia weight of the particle with the poor position, so that the inertia weight of each particle is more matched with the current position of the particle; when the speed of the particles is updated, optimal experience positions pbestc of other particles in the population are introduced for learning, and a learning factorc3 is adjusted to enable the corresponding pbestc to be linearly decreased progressively along with the number of iteration steps; and by utilizing the thought of a genetic algorithm, a particle withan optimal position is added in a manner of combining inheritance and variation to jump out of a local minimum value and accelerate an optimization process.

Description

technical field [0001] The invention relates to the technical field of in-pipeline detection, in particular to an inversion method for irregular pipeline defects based on an improved particle swarm algorithm. Background technique [0002] Oil and natural gas are important energy and chemical raw materials, which play a vital role in people's life, industrial and agricultural production and national defense construction. The basic requirements for oil and gas pipeline transportation are safety and efficiency. However, the working conditions of long-distance pipelines are usually very harsh. Affected by various factors, damages such as corrosion and cracks are prone to occur, or potential defects inside the pipeline develop into damage and cause leakage. Oil and gas leakage not only causes huge economic losses, but also causes serious environmental pollution and threatens personal safety. Therefore, regular non-destructive testing must be carried out on oil and gas pipelines...

AI Technical Summary

Problems solved by technology

The inversion methods of magnetic flux leakage signals are mainly divided into two categories, one is the direct method that does not require a physical model, and the other is the indirect method based on the model. The principle of the direct method that is not based on the model is to directly establish the measurement signal and defect Although the mapping relationship between parameters has the advantage of being fast and simple, since the mapping parameters of this method are based on training samples, the generalization ability of this model is poor. When the actual defect is far from the training sample , the accuracy of the model is low, especially for defects with irregular shapes, which cannot be accurately predicted, and most of the defects in practical applications are irregular

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