涡流脉冲热像技术中检测条件的粒子群优化

Optimization of eddy current pulsed thermography detection condition using particle swarm optimization

涡流脉冲热像检测中的检测条件优化是最大化裂纹区域生热量以充分发挥检测系统性能的重要保证。针对检测条件选择人工依赖性强等问题,以含有特定尺寸疲劳裂纹的金属平板试件为研究对象,采用仿真和实验相结合的方法,分析了检测条件对裂纹热响应的影响特点,结果表明:裂纹热响应随着激励时间、激励强度的增加而增强;随着提离距离的增加呈现先增强后减弱的趋势。基于仿真与实验结果,提出了一种用于估算特定检测条件下裂纹热响应的多元非线性回归模型,确定了裂纹热响应与不同检测条件之间的定量化关系。最终引入粒子群优化算法进行了检测条件优化,给出了热响应分布图和检出概率分布图。研究成果为涡流脉冲热像检测中的检测条件优化提供理论指导。

Optimization of detection conditions is defined as maximizing the amount of heat generated in crack area, in order to perform better in the Eddy Current Pulsed Thermography（ECPT）. Aiming at standardizing the method of optimization in ECPT, and a single metal plate specimen with a specific crack was taken as the investigated subject. Response signal increased with the excitation time and excitation intensity, and it had a tendency to enhance first and then weaken with the increase of lift-off distance analyzed by results of simulation and experiment. A multivariate nonlinear regression model was proposed to estimate response signal under specific detection conditions, and the quantitative relation between response signal and different detection conditions was determined. Finally, the Particle Swarm Optimization（PSO） algorithm was introduced to optimize the detection conditions, and the distribution of response signal and Probability of Detection（POD） with different detection conditions were drawn. The research results provide theoretical guidance for optimization of detection conditions in ECPT.