摘要
在涡流脉冲热像技术中,高频涡流瞬时加热被测物体时,不同区域的热响应会发生混叠现象,这势必影响缺陷区域热响应信号的判别。本文以红外图像序列为观测信号,建立热响应信号的混叠模型;其次,利用不同区域的热响应彼此独立的特点,开展了基于主成分分析的盲源分离数据处理方法研究;最后,建立仿真模型研究了不同区域的热响应形态,采用了基于混叠向量和峰度系数定量分析主成分强化的区域。实验结果表明该方法能够实现不同生热区域的盲源分离,为缺陷的特征提取和识别提供了理论支撑。
In eddy current pulse thermography, test specimens are heated instantaneously by a high-frequency eddy current, and the thermal response of different region aliasing occurs, which inevitably affects the identification of the thermal response of a defect area. In this paper, a blind source separation model is developed that takes the thermal response as an observation,. Since the responses of different areas are independent, principal component analysis is employed to separate data. Then, a simulation model is es- tablished to study the thermal responses of different regions. Based on this model, we propose a method of identifying enhancement regions using principal components based on an aliasing vector and kurtosis coeffi- cient. Experimental results show that the method can separate the principal components that describe differ- ent heating areas. This result provides theoretical support for the feature extraction and automatic identifica- tion of defects.
出处
《红外技术》
CSCD
北大核心
2017年第11期1018-1023,共6页
Infrared Technology
基金
军队科研项目
关键词
涡流脉冲热像
盲源分离
主成分分析
热响应
Eddy current thermography, Blind source separation, PCA, Thermal response signals
