A novel unbalanced weighted KNN based on SVM method for pipeline defect detection using eddy current measurements

Abstract

Pipeline safety inspections are particularly important because they are the most common means of energy transportation. In order to avoid pipe leakage, eddy current inspection is often used in metal pipe defect detection. However, in practice, due to problems such as noise and interference, a small number of labeled pipeline defect samples, and unbalanced sample distribution, the detection task cannot be completed. To address the above problems, this study proposes an unbalanced weighted k-nearest neighbor (KNN) based on support vector machine (SVM) defect detection algorithm. The multi-segment hybrid adaptive filtering algorithm is adopted to improve the identification of strong interference and large noise eddy current signals in this paper while retaining useful information such as defects. At the same time, the unbalanced weighted KNN based on the SVM defect detection algorithm is used to solve the problems of low accuracy and large limitations of the algorithm. The experimental results show that, compared with the KNN and SVM algorithms, the detection rate, false detection rate, and missed detection rate of defects are significantly improved.