Inversion of Eddy Current NDE Signals Using Artificial Neural Network Based Forward Model and Particle Swarm Optimization Algorithm
An inversion algorithm for the reconstruction of natural crack shape from eddy current testing signals is developed by using an artificial neural network based forward model and particle swarm optimization algorithm. Eddy current inspections are performed to measure signals caused by fatigue cracks introduced into plate specimens. The preprocessed ECT signals and the true crack shapes are used in the training of neural network. The parameters of the particle swarm optimization algorithm are modified and the results are discussed. The reconstruction results of crack shape verified both the efficiency of neural network based forward model and the promising of particle swarm optimization algorithm in crack shape inversion.
Siquan Zhang Hefa Yang
College of Air Transportation,Shanghai University of Engineering Science,Shanghai,China Department of Mechanical Engineering,Guangzhou Civil Aviation College,Guangzhou,Guangdong Province,C
国际会议
2009 IEEE International Conference on Information and Automation(2009年 IEEE信息与自动化国际学术会议)
珠海、澳门
英文
1314-1319
2009-06-22（万方平台首次上网日期，不代表论文的发表时间）