Unsupervised Sparse Pattern Diagnostic of Defects With Inductive Thermography Imaging System
Unsupervised Sparse Pattern Diagnostic of Defects With Inductive Thermography Imaging System
复制标题
利用感应热成像系统进行缺陷的无监督稀疏模式诊断
DOI：
10.1109/tii.2015.2492925
复制
发表时间：
2016-02-01
影响因子：
12.3
通讯作者：
Tian, Gui Yun
中科院分区：
文献类型：
--
作者：
Gao, Bin;Woo, Wai Lok;Tian, Gui Yun
提出了一种用于感应热成像系统缺陷诊断和监测的无监督方法。所提出的方法是完全自动化的，并且不需要用户手动选择用于缺陷诊断的特定热帧图像。该方法的核心是基于物理的感应热机制与基于信号处理的模式提取算法，使用稀疏贪婪的主成分分析（SGPCA）的混合。一个内部功能内置到所提出的算法来控制稀疏的SGPCA，并呈现更好的精度在大小的缺陷。该方法在金属缺陷的自动诊断和缺陷尺寸的准确性上得到了验证。实验测试和与其他方法进行了比较，以验证所提出的方法的有效性。非常有前途的结果已经获得所提出的方法的性能是非常接近人类的感知。
This paper proposes an unsupervised method for diagnosing and monitoring defects in inductive thermography imaging system. The proposed method is fully automated and does not require manual selection from the user of the specific thermal frame images for defect diagnosis. The core of the method is a hybrid of physics-based inductive thermal mechanism with signal processing-based pattern extraction algorithm using sparse greedy-based principal component analysis (SGPCA). An internal functionality is built into the proposed algorithm to control the sparsity of SGPCA and to render better accuracy in sizing the defects. The proposed method is demonstrated on automatically diagnosing the defects on metals and the accuracy of sizing the defects. Experimental tests and comparisons with other methods have been conducted to verify the efficacy of the proposed method. Very promising results have been obtained where the performance of the proposed method is very near to human perception.