Fault diagnosis approach based on the integration of qualitative model and quantitative knowledge of signed directed graph

The inference based on signed directed graph (SDG) is a self-contained method to effectively diagnosis system failures, the SDG diagnosis method has good completeness, fine resolution and detailed explanation facility, but many limitations restrict it applies in fault diagnosis. The shortcoming of single variable analysis in modifying node state and threshold value can be avoided by combining the principal component analysis (PCA) with SDG, the problem of information explode for reasoning rules can be solved effectively by creating the SDG classification model, and then the patterns that can not be distinguished are diagnosed by using fuzzy knowledge to form a qualitative and quantitative model, and comparing the membership grade of the patterns need be diagnosed to the given fault patterns. The case studies show the improved SDG has better resolution in fault diagnosis.