Texture Defect Detection of Wire Rope Surface with Support Vector Data Description

Abstract

It is a difficult problem to describe the feature of wire rope texture with fault. The one-class classification method is adopted to description the feature of the faultless wire rope images. A method is presented to detect the surface defect of wire rope based the support vector data description (SVDD) method. The model selection and parameter optimize methods of SVDD are discussed thoroughly. Then, the bandwidth of Gauss kernel function is optimized to minimize the mean of false alarm rate in the experiment. The experiment is carried out to detect the surface fault of airplane control ropes with different diameters (4-6 mm). The test of defect detection is carried out in 200 wire rope images, and the results indicate that the detecting accuracy is 93%. The method is valuable for detecting the surface local fault of aircraft control rope practically.