Data-driven fault diagnosis method for analog circuits based on robust competitive agglomeration

被引：0
|
作者：
Rongling Lang [1 ]
Zheping Xu [1 ]
Fei Gao [1 ]
机构：
[1] School of Electronic and Information Engineering, Beihang University
来源：
JournalofSystemsEngineeringandElectronics | 2013年 / 24卷 / 04期
基金：
国家高技术研究发展计划(863计划); 中国国家自然科学基金;
关键词：
data-driven; fault diagnosis; analog circuit; robust competitive agglomeration (RCA);
D O I：
暂无
中图分类号：
TN710 [电子电路];
学科分类号：
080902 ;
摘要：
The data-driven fault diagnosis methods can improve the reliability of analog circuits by using the data generated from it. The data have some characteristics, such as randomness and incompleteness, which lead to the diagnostic results being sensitive to the specific values and random noise. This paper presents a data-driven fault diagnosis method for analog circuits based on the robust competitive agglomeration (RCA), which can alleviate the incompleteness of the data by clustering with the competing process. And the robustness of the diagnostic results is enhanced by using the approach of robust statistics in RCA. A series of experiments are provided to demonstrate that RCA can classify the incomplete data with a high accuracy. The experimental results show that RCA is robust for the data needed to be classified as well as the parameters needed to be adjusted. The effectiveness of RCA in practical use is demonstrated by two analog circuits.
引用
收藏
页码：706 / 712
页数：7
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