A Fault Diagnosis Method Based on Rough Set and Improved D-S Evidence Theory
DING Han
HOU Ruichun
DING Xiangqian
Abstract：In order to solve data problems with redundant,conflict and uncertainty in monitoring large equipment,a data fu?sion equipment monitoring method is proposed through the combination of rough set and improved D-S evidence theory. According to the decision table after the attribute reduction using rough set,the rule strength of attributes can be calculated,thus the basic probability assignment values are objectively determined. Meanwhile,the relative weights of evidences are extracted from the deci?sion importance of attributes,the Dempster's combination rules is then modified in the light of the determined weights. The proposed method remedies the deficiencies of D-S theory in evidence conflict and subjective determination of the basic probability assign?ment. A simulation of fault diagnosing method with application to the ozone generator is carried out using the proposed method,the results show that the accuracy of the proposed method is proved,and the uncertainty of the results is obviously reduced comparing with classic analyzing methods,which concludes that the proposed method has a practical significance in fault diagnosis.
Keywords：rough setsD-S evidence theorydata fusionfault diagnosismultisensorozone generator
Publication Date：2019-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 543-549 )