基于ResGNNet多模态融合的油气管道缺陷等级磁记忆定量识别

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中图分类号：TG115.284DOI：10.3969/j.issn.1004-132X.2025.09.027

Quantitative Identification of Oil and Gas Pipeline Defect Levels Based on Magnetic Memory Using ResGNNet Multi-modal Fusions

XING Haiyan1 WU Xueyuan1CAI ZhihuiZHAO Liwei2 SU Tian1 HAN Qing1 1.School of Mechanical Science and Engineering，Northeast Petroleum University，Daqing， Heilongjiang，163318 2.Wenzhou Special Equipment Inspection & Science Research Institute，Wenzhou， Zhejiang，325038

Abstract：Aiming at the problems of automatic extraction of magnetic memory signal features and quantitative identification of defect levels in oil and gas pipelines，a multimodal fusion model was proposed combining residual neural network and graph neural network（ResGNNet）. The original magnetic memory signals of defects of diferent depths on L245N pipeline steels were collected by metal magnetic memory detector. In order to realize automatic feature extraction，the complete information of the original magnetic memory signals was retained，and the relationship among samples was taken into account.The original signals were converted into a node graph by K nearest neighbor-dynamic time warping，and the original signals were converted into a 2D image by Gram angle field. The designed graph neural network and residual neural network may automatically extract the embedded feature vectors of 1D signals and 2D images respectively.The multimodal embedded feature vectors were fused，weighted and screened by multi-head selfattention mechanism，and then input into the Softmax classification module to complete the defect level identification.The model verification results show that the accuracyof quantitative identification of pipeline defect levels reaches 93% ：

Key words：oil and gas pipeline；metal magnetic memory technology；defect level；graph neural network；residual neural network

0 引言

长输油气管道是解决当前油气产销区域矛盾的主要手段，对资源调配有重要意义。（剩余11725字）