文献类型：期刊文献

英文题名：A Semisupervised Learning Framework for Recognition and Classification of Defects in Transient Thermography Detection

作者：Liu, Lishuai[1];Guo, Chenjun[2,3];Xiang, Yanxun[1];Tu, Yanxin[3];Wang, Liming[3];Xuan, Fu-Zhen[1]

机构：[1]East China Univ Sci & Technol, Sch Mech & Power Engn, Minist Educ, Key Lab Pressure Syst & Safety, Shanghai 200237, Peoples R China;[2]Yunnan Power Grid Co Ltd, Elect Power Res Inst, Kunming 650217, Yunnan, Peoples R China;[3]Tsinghua Univ, Tsinghua Shenzhen Int Grad Sch, Shenzhen 518055, Peoples R China

年份：2022

卷号：18

期号：4

起止页码：2632

外文期刊名：IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

收录：;EI(收录号：20213210745219);WOS:【SCI-EXPANDED(收录号:WOS:000739636900048)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant U1930202, Grant 12025403, and Grant 51835003, in part by Shanghai Sailing Program under Grant 21YF1408900, and in part by the Natural Science Foundation of Shanghai under Grant 21ZR1417100.

语种：英文

外文关键词：Transient analysis; Testing; Substrates; Thermal conductivity; Support vector machines; Reflection coefficient; Feature extraction; Defect type classification; multiclass Laplacian support vector machine (MCLSVM); semisupervised learning (SSL); transient thermography

摘要：The defect classification task is of great benefit to evaluating the safety performance of equipment and providing useful feedback information for discovering production process problems. In this article, we present a semisupervised learning (SSL) framework for transient thermography detection to employ the temporal and spatial information encoded into the three-dimensional transient thermal tensor data and provide pixel-level classification results for defect types. The time- and frequency-domain physical models for the transient thermal evolution of different kinds of defects are established to illustrate the theoretical foundation of defects classification based on transient thermography. The semisupervised multiclass Laplacian support vector machine is proposed to enable involving the abundant unlabeled data for enhancing learning performance in practical industrial applications where labeled samples are insufficient and labeling work is costly and laborious. A case study on silicone insulating materials with various types of artificial simulated internal defects validates the stronger generalized ability of the proposed method. This work, for the first time, proposes an SSL framework in transient thermography-based defect detection studies. It is believed that our proposed method is quite inspired for introducing SSL techniques to transient thermography for preferable performance in practical industrial applications.