Fault Diagnosis for Gun’s Anti-recoil Device Based on Gaussian Model and RMSD-DS

doi:10.12382/bgxb.2022.0635

Abstract

Abstract:

To deal with the problem of low reliability of fault diagnosis caused by the ambiguity and high conflict of the state signal of the anti-recoil device of the gun under variable operating conditions, a fault diagnosis method based on the Gaussian model and RMSD (Root Mean Square Deviation)-DS (Dempster-Shafer) is proposed to realize the quantitative characterization of signal fuzziness and the high reliability diagnosis of high-conflict evidence fusion. This method uses the Gaussian model to solve the basic probability assignment of the evidence corresponding to multiple fault feature signals of the anti-recoil device, thus realizing the quantitative characterization of the signal ambiguity. By constructing the RMSD-DS conflict coefficient to solve the similarity between evidences, the importance of each evidence is defined according to the similarity, and the quantitative description of the importance of each evidence in the fusion process is realized. The weight solution and evidence fusion method based on the importance of evidence are put forward to realize the reliable fusion of highly conflicting evidence. The application of simulation test data shows that the proposed method has higher accuracy and robustness than other representative methods.

Key words: Gaussian model, gun’s anti-recoil device, evidence theory, fault diagnosis

CLC Number:

E924
TJ303.4

WEI Jianfeng, ZHANG Faping, LU Jiping, YANG Xiangfei, YANG Pengkai. Fault Diagnosis for Gun’s Anti-recoil Device Based on Gaussian Model and RMSD-DS[J]. Acta Armamentarii, 2023, 44(10): 3101-3114.

Figures/Tables 16

Fig.1 Simple structure of a gun

Fig.2 Force analysis of the recoil part (recoiling)

Fig.3 Force analysis of the recoil part (counter-recoiling)

Fig.4 Displacement and velocity curves during recoiling and counter-recoiling motion

Fig.5 Displacement and velocity curves during recoiling and counter-recoiling motion in different states

Fig.6 Flow chart of the fault diagnosis method

Fig.7 Matching degree on signals with different fault characteristics

Table 1 Fusion results of different methods

方法	m₁, m₂	m₁, m₂, m₃	m₁, m₂, m₃, m₄
DS	m(A)=0 m(B)=0.8571 m(C)=0.1429	m(A)=0 m(B)=0.6316 m(C)=0.3684	m(A)=0 m(B)=0.3288 m(C)=0.6712
Yager^[13]	m(A)=0 m(B)=0.1800 m(C)=0.0300 m(O)=0.7900	m(A)=0 m(B)=0.0180 m(C)=0.0105 m(O)=0.9715	m(A)=0 m(B)=0.0018 m(C)=0.0037 m(O)=0.9945
Murphy^[16]	m(A)=0.1543 m(B)=0.7469 m(C)=0.0998	m(A)=0.3500 m(B)=0.5224 m(C)=0.1276	m(A)=0.6027 m(B)=0.2627 m(C)=0.1346
RMSD-DS	m(A)=0.1543 m(B)=0.7469 m(C)=0.0998	m(A)=0.5100 m(B)=0.3280 m(C)=0.1620	m(A)=0.8493 m(B)=0.0114 m(C)=0.1393

Fig.8 Fusion results of different methods

Fig.9 Fault feature signal corresponding to different fault states

Table 2 Mean and standard deviations of the Gaussian model

故障状态	$μ X m a x$ /m	$σ X m a x$ /m	$μ v m a x$ / (m·s^-1)	$σ v m a x$ / (m·s^-1)	$μ U m a x$ / (m·s^-1)	$σ U m a x$ / (m·s^-1)	$μ U e n d$ / (m·s^-1)	$σ U e n d$ / (m·s^-1)
WTR	1.0224	0.0371	10.6842	0.0642	2.2759	0.1264	0.1388	0.0055
GLR	0.9595	0.0108	10.5730	3×10^-4	2.0598	0.0029	0.1521	0.0020
BRPW	0.9781	0.0108	10.5876	0.0086	1.7774	0.1675	0.1088	0.0216

Table 2 Mean and standard deviations of the Gaussian model

故障状态	$μ X m a x$ /m	$σ X m a x$ /m	$μ v m a x$ / (m·s^-1)	$σ v m a x$ / (m·s^-1)	$μ U m a x$ / (m·s^-1)	$σ U m a x$ / (m·s^-1)	$μ U e n d$ / (m·s^-1)	$σ U e n d$ / (m·s^-1)
WTR	1.0224	0.0371	10.6842	0.0642	2.2759	0.1264	0.1388	0.0055
GLR	0.9595	0.0108	10.5730	3×10^-4	2.0598	0.0029	0.1521	0.0020
BRPW	0.9781	0.0108	10.5876	0.0086	1.7774	0.1675	0.1088	0.0216

Fig.10 Gaussian models belonging to different fault feature signals

Fig.11 Diagnostic results of different methods (confusion matrix)

Table 3 Accuracy of fault diagnosis of different methods%

方法	WTR	GLR	BRPW	平均值
DS	91.5	1.5	95.5	62.8
Yager^[13]	53.5	0	0	17.8
Murphy^[16]	91.0	89.0	96.0	92.0
BP^[4]	99.5	17.5	100	72.3
RMSD-DS	90.5	95.5	97.5	94.5

Fig.12 Comparison of different methods

Fig.13 ROC curves of the five methods

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