Fault detection using thermal image based on soft computing methods: Comparative study

被引:9
|
作者
Al-Obaidy, Furat [1 ]
Yazdani, Farhang [1 ]
Mohammadi, Farah A. [1 ]
机构
[1] Ryerson Univ, Dept Elect & Comp Engn, 350 Victoria St, Toronto, ON M5B 2K3, Canada
关键词
Thermal testing; Soft computing methods; Integrated Circuit; Principal component analysis; Histogram analysis;
D O I
10.1016/j.microrel.2017.02.013
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This paper presents Integrated Circuit (IC) fault detection of a Printed Circuit Board (PCB) model using thermal image processing. The thermal image is captured and processed from the PCB model by the finite element method (FEM). The histogram features are extracted from the ICs hotspots which are used as inputs in a classifier model. The effective features are minimized by the principal component analysis method. In this work, a comparative study for image classification and detection is performed based on three soft computing techniques: multilayer perceptron, support vector machine, and adaptive neuron-fuzzy inference system. The effectiveness of the models is evaluated by comparing the performance and accuracy of the classification. To validate the model, the experimental evaluation is performed on Arduino UNO in order to detect the fault condition on the real time operating PCB. (C) 2017 Elsevier Ltd. All rights reserved.
引用
收藏
页码:56 / 64
页数:9
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