Condition assessment method of equipment based on rough sets and evidence theory

被引：1

作者：

Wang L. ^{[1
,2
]}

Lu Z. ^{[1
,2
]}

Li Z. ^{[1
,2
]}

机构：

[1] Unit 91776 of the PLA, Beijing

[2] Key Laboratory of Complex Ship System Simulation, Beijing

来源：

Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2020年 / 42卷 / 01期

关键词：

D-S evidence theory; Fuzzy C-means clustering; Health condition assessment; Rough set;

D O I：

10.3969/j.issn.1001-506X.2020.01.19

中图分类号：

学科分类号：

摘要：

To assess the health condition of complex equipment accurately, a health condition assessment method based on rough sets and D-S evidence theory is proposed. Firstly, given that only discrete attributes could be processed by using rough sets, a discretization method for continuous attributes based on the dynamic fuzzy C-means clustering algorithm is put forward. Secondly, the reduction attributes are obtained by using the reduction algorithm based on mutual information. Thirdly, the assessment decision table is processed and the basic probability assignment function is set up. Finally, assessment indexes are fused by the evidence theory to get the health condition grade, and the relationship between assessment indexes and health conditions is mined further. The case study and comparative analysis show that this method can improve the decision credibility effectively and reduce the uncertainty of assessment. © 2020, Editorial Office of Systems Engineering and Electronics. All right reserved.

引用

页码：141 / 147

页数：6

共 21 条

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