基于PSO-LS-SVM的储罐底板缺陷量化方法研究

Research on quantification of defects on tank floor based on particle swarm optimization-least square support vector machine

为能准确量化油气储罐底板上的缺陷,提出一种最小二乘支持向量机缺陷量化方法,并以该方法模型建立了缺陷的三维漏磁场信号与缺陷的长度、宽度、深度之间的映射关系。为提高该方法对储罐底板缺陷的量化精度,采用粒子群算法对模型参数进行了优化。仿真结果及分析表明,与BP神经网络方法相比,结合了粒子群优化的最小二乘支持向量机缺陷量化方法的网络训练时间短、缺陷量化精度高,具有较强的工程应用优势。

This paper applies the least square-support vector machine( LS-SVM) to quantify the defects on tank floor of oil and gas,this method builds the relationship between the three-axial magnetic flux leakage( MFL) of defects and the length,width and depth of defects. In order to accurately quantify the defect,the particle swarm optimization( PSO) is adopted to optimize the model parameter of LS-SVM. According to the simulation and analysis results,PSOLS-SVM needs less training time and has better accuracy for the quantification of defects than BP neural network method,and PSO-LS-SVM has better application advantages on the engineering.