中文摘要：

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,提出了基于贝叶斯算法的BP神经网络缺陷量化方法。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,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,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. The BP neural network is used to build the relationship between MFL signals and defect features on defects＇ length,width and depth. In order to save the training time and accurately quantify the defect profile,the Bayesian Algorithm is embedded into the BP neural network to control the complexity and optimize the parameters. In order to obtain more defects information,this paper uses the three-axial MFL signals to quantify the horizontal groove defects,which further improves the accuracy of the length and width 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 and has good application advantages.