ニューラルネットワークを用いた渦電流探傷試験における欠陥信号の検出とサイジング
概要
This paper proposes backpropagation neural network models which are based on artificial intelligence technology to detect the defect signals and to discern the measurements of defects according to the eddy current testing (ECT) results even under the probe lift-off and probe wobble noise effects. In this paper, a stainless plate with three different depth slits is tested by ECT to collect defect signals. And during the scanning process, the noise which is mentioned above is added. All the collected data are used to analyze and to figure out the differences between defect signals and noise and the relationships of different depth signals. The established neural network models are trained by the collected data and extracted features to achieve the desired functions, such as the detection and qthe sizing of defects. The verification experiment is carried out to confirm the trained neural network. It is evident that the trained neural networks can find out the defect existence and output the depth of defects accurately.
