基于多信号流图和相似性度量的故障可诊断性评价方法

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

Abstract

Abstract:

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.

Key words: fault diagnosability, diagnosability evaluation, multi-signal flow diagram, similarity measure, Euclidean distance

CLC Number:

TP306.3

Yufeng QIN, Xianjun SHI. Fault diagnosability evaluation method based on multi-signal flow graph and similarity measure[J]. Systems Engineering and Electronics, 2023, 45(1): 302-312.

Figures/Tables 15

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故障模式编号	故障模式名称
f₁	电阻R₁短路
f₂	电阻R₁断路
f₃	电阻R₂断路
f₄	运放A₁输入端与输出端断开
f₅	电阻R₃短路
f₆	电阻R₃断路
f₇	电容C₁短路
f₈	电容C₁断路
f₉	电阻R₄短路
f₁₀	电阻R₄断路
f₁₁	运放A₂输入端与输出端断开

故障模式编号	故障模式名称
f₁	电阻R₁₈短路
f₂	电阻R₁₁断路
f₃	电容C₁₀短路
f₄	电阻R₁₀短路
f₅	晶体管Q₁断路
f₆	光电耦合器U₃断路

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

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