CAJ | 학술논문

利用热图时序特征和PNN，提出了一种以像素为单位，实现缺陷红外无损检测的新方法。该方法首先采用红外热像仪获取加热试件在降温过程中的红外时序热图；其次，提取时序热图中正常和异常区域的灰度值，建立不同区域的灰度值与时间的关系，进而获得相应的初始特征；再次，采用主成分分析方法对初始特征进行提取，获得时序特征；最后，以时序特征作为训练样本，构建概率神经网络，实现孔洞缺陷检测。实验结果表明，正常区和异常区识别率分别可达到95%和85%。
이용열도시서특정화PNN，제출료일충이상소위단위，실현결함홍외무손검측적신방법。해방법수선채용홍외열상의획취가열시건재강온과정중적홍외시서열도；기차，제취시서열도중정상화이상구역적회도치，건립불동구역적회도치여시간적관계，진이획득상응적초시특정；재차，채용주성분분석방법대초시특정진행제취，획득시서특정；최후，이시서특정작위훈련양본，구건개솔신경망락，실현공동결함검측。실험결과표명，정상구화이상구식별솔분별가체도95%화85%。
This paper presents a novel method of infrared NDT for detecting hole defects based on temporal charac-teristics and probabilistic neural network (PNN). Firstly, the sequence image was obtained by thermal imaging camera. Secondly, the gray value of normal and abnormal area was extracted and different parts of the gray value of time were set up, and then the initial characteristics were achieved. The principal component analysis was used to extract initial characteristics and get the temporal characteristics. Finally, the temporal characteristics were adopt-ed as the training sample, and the probabilistic neural network was founded for the hole defect detection. Results showed that the recognition rates of the normal and abnormal area were 95%and 85%respectively.