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

Electromagnetic emissions are radiated from every part of a personal computer motherboard, thus producing electromagnetic interference (EMI). EMI has an adverse effect on the surrounding environment because EMI could cause malfunctions or fatal problems in other digital devices. EMI engineers diagnose motherboard EMI problems using the electromagnetic noise data measured by the spectrum analyzer. Finding the sources (e.g., PS2, USB, VGA) of electromagnetic noise is a time-consuming process. The attribute selection and fault diagnosis was developed based on the advantage of rough set theory (RST). RST is a novel data mining approach for dealing with vagueness and uncertainty. It can be used to find hidden patterns in data sets. In this study, the basic rough set theory concepts are introduced. The rough set approach enables one to discover the minimal subsets of condition attributes associated with the motherboard EMI fault diagnosis problem. The operating sequence includes data collection, data preprocessing, discretization, attribute reduction, reduction filtering, rule generation, and classification accuracy. Historical EMI noise data, colleted from a famous motherboard company in Taiwan, were used to generate diagnostic rules. Our research result (average diagnostic accuracy of 80% above) shows that the RST model is a promising approach for EMI diagnostic support systems.