Attribute Selection Based on Rough Set Theory for Electromagnetic Interference (EMI) Fault Diagnosis

Article

Huang, Cheng-Lung; Li, Te-Sheng; Peng, Ting-Kuo   (2006, ASQ and Taylor & Francis Group, LLC)   National Kaohsiung First University of Science and Technology, Kaohsiung Taiwan, ROC; Ming Hsin University of Science and Technology, Hsinchu, Taiwan, ROC

Quality Engineering    Vol. 18    No. 2
QICID: 20470    April 2006    pp. 161-171
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Article Abstract

[This abstract is based on the authors' abstract.]Electromagnetic interference (EMI) generated by the motherboards of personal computers can cause malfunctions or fatal problems in other digital devices in the surrounding environment. Finding the sources of EMI is a time consuming process. The rough set theory (RST), a data mining approach for dealing with vagueness and uncertainty, can be used to identify minimal subsets of condition attributes associated with the motherboard EMI fault diagnosis problem. Historical EMI noise data from a Taiwanese motherboard manufacturer were used to generate diagnostic rules. Study results demonstrate that the RST model is a promising approach for EMI diagnostic support systems.

Keywords

Computers,Data analysis,Diagnostics,Electronics


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