A Modified Fault Diagnosis Method Based on RS and SVM
Yitian Xu · Microcomputer Information · 2008
Traditional fault diagnosis method based on RS and SVM reduces the diagnosis samples using Rough Set before using SVM for classification. Rough Set is used only as a tool for data reduction, while it is ignored that the decision rules obtained by Rough Set is the brief description of the knowledge embedding in original data. This paper proposes a modified fault diagnosis method based on Rough Set and Support Vector Machine. Firstly, the diagnosis samples are reduced by Rough Set and the decision rules are obtained. Then the obtained rules are integrated into Support Vector Machine for fault diagnosis. This method combines the advantage of Rough Set in processing high dimensional data with the advantage of Support Vector Machine in better generalization capacity. And the obtained decision rules by Rough Set are used to assist Support Vector Machine with classification for improving the prediction accuracy. The diagnosis of a diesel shows a good fault diagnosis ability of the method.