Pipeline corrosion defect parameterisation with magnetic flux leakage inspection: a contextual representation approach

Abstract

Corrosion is one of the significant reasons for oil and gas pipeline failures. In pipeline integrity management programmes, the magnetic flux leakage (MFL) technique is widely used to detect and quantify corrosion defects. The inspection results of MFL list the profiles of individual\n corrosion defects; however, the structural safety of a pipeline not only depends on the size of individual corrosion defects but also the pattern of closely spaced defects. To achieve a contextual defect representation, the concept of parameterisation, which considers the adjacent defects\n as additional information of the central defect, is proposed in this study. The process through which to realise this contextual representation is described in this paper. Three parameterisation models are proposed and a two-dimensional Gaussian function is employed to model the interaction\n strength between adjacent defects. The experimental results demonstrate that the shape context (SC) model associated with the interaction strength function (ISF) shares the highest similarity with human inspectors in comparison with the other two models. Thus, the proposed parameterisation\n approach can be used to retrieve similar corrosion defects and analyse defect population distribution along a pipeline.