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  2023, Vol. 44 Issue (5): 622-636    
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于PCA-BP神经网络的管道内壁几何形状识别
甘智超1,郭硕昌2,陶盈盈2,荆瑞江2,余波2
1. 合肥工业大学土木与水利工程学院应用力学研究所
2. 合肥工业大学
Identification of Pipeline Inner Wall Geometry Based on the PCA-BP Neural Network
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摘要 针对石油和天然气等管道内部因腐蚀等因素造成的结构性缺陷问题,基于主成分分析(PCA)和反向传播(BP)神经网络提出了一种有效的管道内壁几何识别方法:首先,借助CAD二次开发实现随机管壁几何模型的自动化建模,并结合二维恒定磁场有限元分析来获取测点磁场响应,建立了自适应样本库生成系统;然后,采用主成分分析法对测点磁场响应数据进行降维处理,去除了数据中的冗余信息;最后,利用BP神经网络基于测点磁场响应与管壁几何参数之间的映射关系对管壁几何形状进行反演识别。算例结果表明,基于PCA-BP神经网络的管道内壁几何反演分析模型可快速准确地预测管道内壁的几何形状。即使对于带有不同程度随机误差的复杂几何,该方法仍具备强大的识别性能。
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甘智超
郭硕昌
陶盈盈
荆瑞江
余波
关键词 管道内壁几何形状识别BP神经网络主成分分析CAD自动化建模identification of pipeline inner wall geometry back propagation neural network principal component analysis CAD automated modeling    
Abstract:Pipelines, such as those used for petroleum and natural gas transportation, are subject to various factors such as temperature, pressure and corrosion during long-term operation. These factors can lead to structural defects within the pipelines, including collapse, deformation, rupture and corrosion. Thus, a pipeline inner wall geometric identification method based on principal component analysis (PCA) and back propagation (BP) neural network is proposed to tackle these problems. Firstly, an automated modeling technique for the adaptive inner wall of the pipeline is adopted using CAD secondary development technology, enabling the rapid acquisition of a large number of randomly generated geometric models. Subsequently, these models are subjected to two-dimensional constant magnetic field finite element analysis to obtain magnetic field response data of specific measurement points. This adaptive sample library generation system reduces the cost required for modeling and simulation while providing necessary data foundation for the identification of pipeline inner wall geometry. Meanwhile, the PCA method is utilized to reduce the dimensionality of the magnetic field response data. This dimensionality reduction technique effectively eliminates redundant information and improves the efficiency of the subsequent training process. Finally, a mapping relationship between the magnetic field responses of the measurement points and the geometric parameters of the pipeline wall is established using a BP neural network which optimized by the Levenberg-Marquardt algorithm. This mapping relationship enables the identification of the geometric shape of the pipe wall based on the magnetic field response data. The results of numerical examples demonstrate that the PCA-BP neural network-based pipeline inner wall geometric inversion analysis model can accurately and rapidly predict the geometric shape of the pipeline inner wall. Even for complex geometries with varying degrees of random errors, the proposed method exhibits strong identification performance. This work provides a new way to address structural defects within pipelines and offers an effective tool for pipeline safety assessment and health management.
收稿日期: 2023-03-24      出版日期: 2023-10-23
基金资助:功能梯度材料瞬态传热问题的等几何边界元法及其反问题非迭代法研究;瞬态热力耦合问题的比例边界有限元法分析及缺陷识别研究
通讯作者: 余波     E-mail: yubochina@hfut.edu.cn
引用本文:   
甘智超 郭硕昌 陶盈盈 荆瑞江 余波. 基于PCA-BP神经网络的管道内壁几何形状识别[J]. , 2023, 44(5): 622-636.
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