Fault diagnosability evaluation method based on multi-signal flow graph and similarity measure

被引：0

作者：

Qin Y. ^{[1
]}

Shi X. ^{[1
]}

机构：

[1] College of Coastal Defense Force, Naval Aviation University, Yantai

来源：

Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2023年 / 45卷 / 01期

关键词：

diagnosability evaluation; Euclidean distance; fault diagnosability; multi-signal flow diagram; similarity measure;

D O I：

10.12305/j.issn.1001-506X.2023.01.35

中图分类号：

学科分类号：

摘要：

Aiming at the problem that the fault diagnosability of electronic system cannot be quantitatively evaluated based on qualitative model, the paper combines qualitative model with data-driven methods, and a method of fault diagnosability evaluation based on multi-signal flow graph and similarity measure is proposed. Firstly, the multi-signal flow graph model is established according to the composition of the system and the fault-test correlation matrix is obtained. Based on the fault-test correlation matrix, the fault diagnosability evaluation criteria is proposed. Secondly, the Shannon entropy of the wavelet packet of the test signal is extracted as the feature vector, and the Euclidean distance is used as the similarity measurement index. The problem of fault diagnosability quantitative evaluation is transformed into the similarity measure problem of the feature vector of the test signal under different fault modes. Then, the fault diagnosability evaluation matrix is constructed, and the system diagnosability indicators is proposed according to the fault diagnosability evaluation matrix. Finally, the effectiveness of the proposed method is verified by simulation analysis, and the results show that the method proposed in the paper can realize the quantitative evaluation of the fault diagnosability of electronic system without constructing mathematical model. © 2023 Chinese Institute of Electronics. All rights reserved.

引用

页码：302 / 312

页数：10

共 31 条

[1] WANG D Y, FU F Z, LIU C R, Et al., Connotation and research status of diagnosability of control systems: a review[J], Acta Automatica Sinica, 44, 9, pp. 1537-1553, (2018)
[2] HUA Y Z, LI Q D, REN Z, Et al., Overview of fault diagnosability evaluation methods for continuous systems, Control and Decision, 31, 12, pp. 2113-2121, (2016)
[3] LIU W J, LIU C R, WANG N H, Et al., Quantitative and qualitative model based fault diagnosability evaluation of momentum wheel, Chinese Space Science and Technology, 31, 4, pp. 54-63, (2011)
[4] WANG Z X, LIU C R, ZHANG Q, Et al., Gyro's diagnosability method based on multi-signal flow graph and improved BHS-tree, Aerospace Control and Application, 38, 4, pp. 1-5, (2012)
[5] LIU W J, LI W B, ZHANG X Y, Et al., Fault diagnosability evaluation of deep space exploration spacecraft based on graph theory, Control Theory & Applications, 36, 12, pp. 2074-2084, (2019)
[6] LIU J, HUA Y Z, LI Q D, Et al., Fault diagnosability qualitative analysis of spacecraft based on temporal fault signature matrix, Proc. of the IEEE Chinese Guidance, Navigation & Control Conference, pp. 1496-1500, (2016)
[7] ERIKSSON D, FRISK E, KRYSANDER M, A method for quantitative fault diagnosability analysis of stochastic linear descriptor models, Automatica, 49, 6, pp. 1591-1600, (2013)
[8] JUNG D E, KRYSANDER M, FRISK E., Quantitative fault diagnosability performance of linear dynamic descriptor models, Proc. of the 22nd IEEE International Workshop on Principles of Diagnosis, (2011)
[9] JUNG D E, KRYSANDER M, FRISK E., Quantitative stochastic fault diagnosability analysis, Proc. of the 50th IEEE Conference on Decision and Control and European Control, pp. 1563-1569, (2011)
[10] JUNG D E, KRYSANDER M, FRISK E, Using quantitative diagnosability analysis for optimal sensor placement, IFAC Proceedings Volumes, 45, 20, pp. 940-945, (2012)

← 1 2 3 4 →