文章基本信息

标题：GMM-based Automatic Defect Recognition Algorithm for Pressure Vessels Defect Detection through ECPT ⁎
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作者：Xiao Yang ; Xuegang Huang ; Chun Yin 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：820-825
DOI：10.1016/j.ifacol.2020.12.837
语种：English
出版社：Elsevier
摘要：AbstractIn order to realize the automatic identification of pressure vessel defects, an improved adaptive defect recognition feature extraction algorithm through ECPT (Eddy current pulsed thermography) is proposed. The proposed feature extraction algorithm consists of five elements: thermal image data segmentation, variable interval search, probability density function modeling, data classification, and reconstructed image acquisition. The combination of data block selection and variable interval search can reduce the double counting. And the KG-EM (Kmeans-GMM-EM) algorithm is proposed to obtain the Gaussian mixture model corresponding to the classification, and thus the corresponding probability is obtained to classify the TTRs (Transient Thermal Response). The reconstructed thermal image is obtained by the classified TTRs. This method can extract the main information of the image accurately and efficiently. Experimental results are provided to demonstrate their effectiveness.
关键词：KeywordsDefect recognitionEddy current pulsed thermographyTransient thermal responseGMM clusteringNondestructive testingDefect detectionPressure Vessels