摘要
文章对基于漏磁信号的长输油气管道缺陷重构方法进行了研究。由于反向求解具有不适定性,而深度学习模型具有强大的非线性映射能力以及特征提取能力,因此搭建CNN-Transformer混合架构模型作为量化模型来预测缺陷尺寸;并对仿真漏磁信号的修正方法进行研究,以减小仿真数据与实验数据之间误差。经验证,修正后仿真轴向分量数据与实验数据峰值之间误差平均下降了83.73%,而径向分量峰值误差平均下降了28.25%,解决了深度学习模型训练样本不充足的问题;并且修正后的数据集作为训练集训练的混合架构模型在预测缺陷尺寸时具有较好的预测精度,模型在预测缺陷长度、宽度与深度时平均相对误差分别降低了21.35%、22.58%和21.55%,具有较高的准确性与鲁棒性。
This paper studies the method of defect reconstruction in long distance oil and gas pipelines based on MFL signals.Because the inverse solution is ill posed,and the Deep Learning model has strong nonlinear mapping ability and feature extraction ability,the CNN-Transformer hybrid architecture model is developed as a quantitative model to predict the defect size.Additionally,it studies the correction methods for simulated MFL signals to reduce the discrepancy between simulation data and experimental data.It is verified that the error between this simulated axial component data and the peak value of the experimental data decreases by 83.73%on average,while the peak error of the radial component decreases by 28.25%on average,which solves the problem that the training samples of the Deep Learning model are not sufficient.Moreover,the hybrid architecture model trained by the corrected dataset as the training set has better prediction accuracy when predicting the defect size.The Mean Relative Error of the model in predicting the length,width and depth of the defect is reduced by 21.35%,22.58%and 21.55%respectively,which has high accuracy and robustness.
作者
于祉祺
刘皓源
何璐瑶
杨理践
刘斌
YU Zhiqi;LIU Haoyuan;HE Luyao;YANG Lijian;LIU Bin(School of Information Science and Engineering,Shenyang University of Technology,Shenyang 110870,China)
出处
《现代信息科技》
2024年第20期48-54,60,共8页
Modern Information Technology
