A Data Driven Method for Quantitative Fault Diagnosability Evaluation

被引：0

作者：

Hua, Yongzhao ^{[1
]}

Li, Qingdong ^{[1
]}

Ren, Zhang ^{[1
]}

Liu, Chengrui ^{[2
,3
]}

机构：

[1] Beihang Univ, Sch Automat Sci & Elect Engn, Sci & Technol Aircraft Control Lab, Beijing 100191, Peoples R China

[2] Beijing Inst Control Engn, Beijing 100190, Peoples R China

[3] Sci & Technol Space Intelligent Control Lab, Beijing 100190, Peoples R China

来源：

PROCEEDINGS OF THE 28TH CHINESE CONTROL AND DECISION CONFERENCE (2016 CCDC) | 2016年

关键词：

Fault diagnosability; Data driven; Fuzzy sets; Similarity measure; Quantitative evaluation; GENERALIZED FUZZY NUMBERS; SIMILARITY MEASURES;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Knowledge of achievable diagnosability performance can provide theoretical guidance for developing diagnostic algorithms and optimizing sensor placement. A data driven approach for fault diagnosability quantitative evaluation without designing any diagnosis algorithm is proposed. Fault diagnosability is converted to similarity measure of output information which is denoted by fuzzy sets under different fault states. A quantitative evaluation measure and a specific diagnosability evaluation process are given. Finally, an example is presented to demonstrate the effectiveness of the proposed methodology.

引用

页码：1890 / 1894

页数：5

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