Fault Diagnosis based on Rough Sets Decision Tree Model and Ant Colony Algorithm
基于粗糙决策模型和蚁群算法的故障诊断

Rough sets theory is a new mathematical tool to deal with vagueness and uncertain,which can remove redundant information and seek for reduced decision tables effectively.Decision tree can extract diagnosis knowledge from reduced decision tables in the form of symbolic trees and easily understood inference mechanisms. Trend correlation degree,as the heuristic knowledge,is proposed to evaluate the significance of condition attributes for the construction of decision tree model.Also a new intelligent algorithm called ant colony algorithm is introduced. With its outstanding characters,the optimal test sequence can be achieved.The proposed method is applied to the fault diagnosis of reaction temperature in industrial purified terephthalic acid(PTA) oxidation process.The effectiveness of the method is therefore proved through the exemplification.