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Transformer fault diagnosis is a complex task that includes many possible types of faults and demands special trained personnel. This paper presents an intelligent fault diagnosis method of power transformer based on fuzzy logic and rough set theory. By using a fuzzy logic technique, the continuous attribute values are transformed into the fuzzy values by automatically deriving membership functions from a set of data with similarity clustering. With the concepts of fuzzy similarity relation and fuzzy similarity classes, beta-positive region, beta-negative region and beta-boundary region of rough-fuzzy approximation space are given. Also, a fuzzy rough set learning algorithm is given for inducing rules from quantitative data. The application to fault diagnosis of transformer shows the proposed algorithm can find more objective and effective diagnostic rules from the quantitative data and has yielded promising results.