基于三维漏磁场信号的储罐底板水平凹槽形缺陷量化方法研究

刘新萌; 赵伟; 黄松岭

刘新萌,赵伟,黄松岭.基于三维漏磁场信号的储罐底板水平凹槽形缺陷量化方法研究[J].电测与仪表,2015,52(12):.
Liu Xinmeng,Zhao Wei,Huang Songling.Quantification of horizontal groove defects on tank floor from three-axial MFL signals[J].Electrical Measurement & Instrumentation,2015,52(12):.

基于三维漏磁场信号的储罐底板水平凹槽形缺陷量化方法研究

Quantification of horizontal groove defects on tank floor from three-axial MFL signals

DOI：

中文关键词: 漏磁检测水平凹槽形缺陷三维漏磁场信号贝叶斯算法 BP神经网络

英文关键词:nondestructive evaluation horizontal groove defects three-axial MFL signal Bayesian Algorithm BP neural network

基金项目:国家自然科学基金( 51277101) 、国家863 重大课题( 2011AA090301 ) 和国家重大科学仪器设备开发专项 ( 2013YQ140505)

作者中文名	作者英文名	单位
刘新萌	Liu Xinmeng	清华大学
赵伟	Zhao Wei	清华大学
黄松岭	Huang Songling	清华大学

摘要点击次数: 1842

中文摘要:

本文针对油气储罐底板上可能存在的水平凹槽形缺陷，采用漏磁检测技术实施测量，并提出了基于贝叶斯算法的BP神经网络缺陷量化方法。该方法将贝叶斯算法引入BP神经网络基本架构中，控制网络复杂度并优化网络参数，从而建立了缺陷漏磁场信号与缺陷长度、宽度、深度的映射关系，且使缺陷量化方法可节约网络计算时间之同时，还提高了对水平凹槽形缺陷的量化精度。为获取更多的缺陷信息，本文采用三维漏磁场信号对水平凹槽形缺陷进行量化，进一步提高了对缺陷长度和宽度的量化精度。仿真结果表明，本文提出的方法在网络训练时间和缺陷量化精度上均具优于已有方法，具有很好的应用优势。

英文摘要:

This paper utilizes a BP neural network based on Bayesian Algorithm to quantify the possible horizontal groove defects on tank floor of oil and gas from Magnetic flux leakage (MFL) signals. This method corporates the Bayesian Algorithm into the BP neural network model to control the complexity of the model and optimize weights of the network in order to build the relationship between the MFL signals and the defect features, which can save the training time of the network and accurately quantify the defect profile. In order to obtain more defects information, this paper uses the three-axial MFL signals to quantify the horizontal groove defects, which further improve the accuracy of quantification. The simulation results show that the method proposed in this paper exhibits better performance in both efficiency and accuracy for the quantification of horizontal groove defects.

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