Identification Methods for Pipeline Defect Classification and Grading Based on Magnetic Memory Signal Characteristics

Based on support vector machine (SVM) method and magnetic memory detection data of pipeline in simulated oilfield scene, identification methods for pipeline defect classification and grading were established. The defect types of 5 oil and gas pipelines in service were identified and two degrees of corrosion, perforated corrosion and non-perforated corrosion, of the test pipelines were identified by the methods. The results showed that the identification rates of defect classification were 77.08%, 89.58% and 95.83% respectively for the SVM models established with different combination of three types of characteristic quantities, in which the identification rate of the model based on characteristic quantities of time domain, form and frequency domain was the highest. In addition, the classification rate of defect grading was 90%. The methods could effectively be used to identify corrosion defects and stress concentration defects, and to grade corrosion defects.