程迪,黄松岭,赵伟,王珅.基于PSO-LS-SVM的储罐底板缺陷量化方法研究[J].电测与仪表,2018,55(4):87-92. Cheng di,Huang Songling,Zhao Wei,Wang Shen.Research on quantification of defects on tank floor based on particle swarm optimization-least square support vector machine[J].Electrical Measurement & Instrumentation,2018,55(4):87-92.
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
This paper applies least square-support vector machine(LS-SVM) to quantify the defects on tank floor of oil and gas, this method build 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, particle swarm optimization(PSO)is adopted to optimize the model parameter of LS-SVM. According to the simulation results, PSO-LS-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.