Applied Mathematics and Nonlinear Sciences
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Journal

Applied Mathematics and Nonlinear Sciences


Volume
& Issue

Volume 6, Issue 2


Published
on

May 25, 2021


Pages


DOI

Article

The Comprehensive Diagnostic Method Combining Rough Sets and Evidence Theory

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Authors

Guang Yang Affiliation:
School of Entrepreneurship and Innovation, Shenzhen Polytechnic, Shenzhen 518000, Guangdong Province, China
, Shuofeng Yu Affiliation:
School of Foreign Languages and Business, Shenzhen Polytechnic, Shenzhen 518000, Guangdong Province, China
, Shan Lu Affiliation:
Institute of Intelligence Science and Engineering, Shenzhen Polytechnic, Shenzhen 518000, Guangdong Province, China
and George Smith Affiliation:
School of Entrepreneurship and Innovation, Shenzhen Polytechnic, Shenzhen 518000, Guangdong Province, China


Abstract

To solve the difficulties in practice caused by the subjectivity, relativity and evidence combination focus element explosion during the process of solving the uncertain problems of fault diagnosis with evidence theory, this paper proposes a fault diagnosis inference strategy by integrating rough sets with evidence theory along with the theories of information fusion and mete-synthesis. By using rough sets, redundancy of characteristic data is removed and the unrelated essential characteristics are extracted, the objective way of basic probability assignment is proposed, and an evidence synthetic method is put forward to solve high conflict evidence. The method put forward in this paper can improve the accuracy rate of fault diagnosis with the redundant and complementary information of various faults by synthesizing all evidences with the rule of the composition of evidence theory. Besides, this paper proves the feasibility and validity of experiments and the efficiency in improving fault diagnosis.


Keywords

fault diagnosis, information fusion, rough sets, D-S evidence theory


Citation

Yang, G., Yu, S., Lu, S., & Smith, G. (2021). The comprehensive diagnostic method combining rough sets and evidence theory. Applied Mathematics and Nonlinear Sciences, 6(2). https://doi.org/10.2478/amns.2021.2.00006

Published by: Engineering Journals

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