基于贝叶斯正则化神经网络的油气管道金属损失缺陷深度反演

Fengmiao Tu, Min Wei, Jun Liu, Lixia Jiang, Jia Zhang
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引用次数: 0

摘要

缺陷深度反演由于其较强的非线性和较低的预测精度,一直被认为是漏磁检测和评估中的一个挑战。目前的反演模型主要关注特定数据集的反演精度,忽略了对不同条件下反演模型的泛化能力的考虑。为了解决这些问题,本文提出了一种基于贝叶斯正则化神经网络(BRNN)模型的管道缺陷反演方法。该方法由两部分组成。首先提取三个域特征,引入Boruta算法降维,得到最佳特征子集;其次,为了逼近多维特征与缺陷深度之间复杂的非线性关系,构建了基于Levenberg-Marquardt优化和贝叶斯学习算法的反向传播神经网络(BPNN)模型;该模型能有效地找到全局最小值,克服过拟合和过训练现象。为了评价所提出的缺陷反演方法的性能,与其他已知的反演算法进行了对比实验。结果表明,该反演方法可以提高缺陷深度的预测精度。更重要的是,该方法提高了不同样本集缺陷反演问题的泛化能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Metal-loss defect depth inversion in oil and gas pipelines based on Bayesian regularisation neural network
Defect depth inversion is generally considered as a challenge in magnetic flux leakage (MFL) testing and evaluation because of its strong non-linearity and low prediction accuracy. Current inversion models focus on the inversion accuracy of specific datasets, ignoring consideration of the generalisation ability of inversion models under different conditions. In order to solve such problems, this paper proposes a novel pipeline defect inversion method based on a Bayesian regularisation neural network (BRNN) model. This method consists of two parts. Firstly, three domain features are extracted and a Boruta algorithm is introduced to reduce the feature dimension and obtain the best feature subset. Secondly, in order to approximate the complex non-linear relationship between multi-dimensional features and defect depth, a back-propagation neural network (BPNN) model based on Levenberg-Marquardt optimisation and a Bayesian learning algorithm is constructed. The model can effectively find a close global minimum and overcome the phenomena of overfitting and overtraining. In order to evaluate the performance of the proposed defect inversion method, a comparative experiment is carried out with other well-known inversion algorithms. The results obtained confirm that the inversion method can improve the prediction accuracy of defect depth. More importantly, this method enhances the generalisation ability of defect inversion problems with different sample sets.
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