Classification and sizing of surface defects of pipelines based on the results of complex diagnostics by ultrasonic, eddy current and visual and measuring methods of nondestructive testing

Abstract

The issues of classification and determination of parameters of surface operational defects according to the results of ultrasonic, eddy current and visual and measuring methods of nondestructive testing are considered. At the same time, the visual and measuring method was realized with the use of a television inspection camera equipped with a computer vision function and a laser triangulation sensor. The paper presents a dataset containing 5760 images of pipelines with and without pitting corrosion. A convolutional neural network (CNN) is presented, which has been applied to classify the images obtained from a TV inspection camera into images without corrosion and images with pitting corrosion. The paper presents a dataset containing 269 measurements of planar and volumetric surface defects. A model for surface defect sizing based on gradient boosting is presented. The paper develops an algorithm for classification and sizing of surface defects in complex diagnostics, in which the obtained models are applied, and determines the accuracy of this algorithm by the RMSE metric, which was calculated within the studying test data set and amounted to 0.011 mm.