Extreme learning machine-based pipeline magnetic flux leakage defect detection method

Abstract

The invention relates to an extreme learning machine-based pipeline magnetic flux leakage defect detection method. The method comprises the following steps: establishing an extreme learning machine model according to data of the length, the width and the depth of a known pipeline magnetic flux leakage defect and a magnetic flux leakage signal waveform characteristic value; training the data of the length, the width and the depth of the known pipeline magnetic flux leakage defect in sample data, wherein the data is taken as the input of the model; selecting the number of nodes of a hidden layer by a cut-and-trial method; and calculating an output matrix and an output weight value of the hidden layer, wherein the magnetic flux leakage signal waveform characteristic value is taken as the output of the model; when a pipeline suffers from magnetic flux leakage, acquiring the waveform of a magnetic flux leakage signal of an unknown magnetic flux leakage shape, and performing pipeline magnetic flux leakage defect detection by the extreme learning machine model. According to the method, the pipeline defect shape is subjected to intelligent inversion by the extreme learning machine model; the method has the advantages of high learning speed, high generalization performance and the like; the shape of the defect can be constructed quickly and accurately by virtue of the waveform of the detected defect, so that the severity of the defect is learnt, a pipeline risk can be foreknown, and the leakage of the pipeline is prevented.

Description

technical field [0001] The invention belongs to the technical field of pipeline detection, and in particular relates to a pipeline magnetic flux leakage defect detection method based on an extreme learning machine. Background technique [0002] In the field of pipeline defect research, there is a large amount of pipeline defect shape data, which requires fast detection speed. For these difficulties, the finite element algorithm is widely used at present. The finite element algorithm is a high-efficiency and commonly used calculation method. It can discretize the continuous discretization into a collection of several finite-sized unit bodies, which can be applied to any differential equation. in various physical fields. However, the finite element algorithm faces the characteristics of a large amount of data, the speed is relatively slow, and the time consumption is long, which cannot meet the requirements. In addition, there are support vector machines. Support vector mach...

AI Technical Summary

Problems solved by technology

Traditional neural network learning algorithms (such as BP algorithm) need to artificially set a large number of network training parameters, and it is easy to generate local optimal solutions

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