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
收藏
页码:302 / 312
页数:10
相关论文
共 31 条
  • [11] FU F Z, WANG D Y, A method for quantitative fault diagnosability analysis of systems with probabilistic sensor faults, International Journal of Control, Automation and Systems, 17, 8, pp. 2159-2164, (2019)
  • [12] FU F Z, WANG D Y, LI W B, Et al., Data-driven fault identifiability analysis for discrete-time dynamic systems, International Journal of Systems Science, 51, 2, pp. 404-412, (2020)
  • [13] ZHAO D, AHN C K, PASZKE W, Et al., Fault diagnosability analysis of two-dimensional linear discrete systems, IEEE Trans.on Automatic Control, 66, 2, pp. 826-832, (2021)
  • [14] CUI Y Q, SHI J Y, WANG Z L, System-level operational diagnosability analysis in quasi real-time fault diagnosis: The probabilistic approach, Journal of Process Control, 24, 9, pp. 1444-1453, (2014)
  • [15] JIANG D N, LI W, Multi-objective optimal placement of sensors based on quantitative evaluation of fault diagnosability, IEEE Access, 7, pp. 117850-117860, (2019)
  • [16] SHARIFI R, LANGARI R, Isolability of faults in sensor fault diagnosis, Mechanical Systems and Signal Processing, 25, 7, pp. 2733-2744, (2011)
  • [17] SHARIFI R, LANGARI R, Sensor fault diagnosis with a proba- bilistic decision process, Mechanical Systems and Signal Processing, 34, 1-2, pp. 146-155, (2013)
  • [18] LI W B, WANG D Y, LIU C R, An approach to fault diagnosability quantitative evaluation for a class of nonlinear systems, Journal of Astronautics, 36, 4, pp. 455-462, (2015)
  • [19] LIN L X, WANG Q, HE B W, Et al., Evaluation of fault diagnosability for nonlinear uncertain systems with multiple faults occurring simultaneously, Journal of Systems Engineering and Electronics, 31, 3, pp. 634-646, (2020)
  • [20] HUA Y Z, LI Q D, REN Z, Et al., A data driven method for quantitative fault diagnosability evaluation, Proc. of the Chinese Control and Decision Conference, pp. 1890-1894, (2016)