多信息源条件下战损部件恢复能力评估方法

doi:10.3969/j.issn.1001-506X.2020.11.16

Abstract

Abstract:

In assessing the resilience of battle damage parts of wartime equipment, in order to solve the problem that the collected sample information is not consistent with the actual battlefield situation, based on the battlefield environment and the maintenance support conditions of the repair teams, the sample information screening model and the internal product model where the sample repair time is converted to the actual repair time are established respectively. The random weighting method is adopted to convert the sample information into parameter distribution, and the support degree and maximum entropy are combined to determine the weight of information sources. The similar parts and parts' historical information are fused to obtain the evaluation results of parts' resilience under the corresponding battlefield conditions. Example analysis shows that this method can be used to evaluate the resilience of parts under various complex battlefield conditions. The proposed multi-source fusion method is more accurate and practical than the traditional estimation method.

Key words: resilience, assessment, battle damage part, multiple information source

CLC Number:

Mingchang SONG, Quan SHI, Zhifeng YOU, Lei LI, Cai CHEN. Evaluation method of resilience of battle damage parts under multiple information sources[J]. Systems Engineering and Electronics, 2020, 42(11): 2538-2545.

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References 30

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编号	资源需求(m_k¹, m_k², m_k³)	修复效果(F_k)	(δ₁₁, δ₁₂, δ₁₃)	(Δt₁₁, Δt₁₂, Δt₁₃)	(Δt₂₁, Δt₂₂, Δt₂₃)	(t₁, t₂, t₃)
1	(1, 1, 0)	1	(0, 0, 0)	(0, 0.5, 0)	(1, 0, 1)	(5, 19, 8)
2	(1, 0, 0)	1	(0, 0.1, 0)	(0, -1, 0)	(-1, 0, -1)	(5, 50, 6)
3	(0, 0, 1)	0	(0, 0, 0)	(0, 0, -0.2)	(0.5, 0, 0.5)	(6, 20, 4)
4	(1, 0, 0)	0	(0, 0, 0.1)	(0, 0, 0)	(0.2, 0, 0.2)	(8, 20, 13)
5	(1, 1, 0)	1	(0, 0, 0.2)	(0.1, 0, 0.1)	(-0.4, 0, 0.4)	(5, 20, 11)
6	(1, 0, 0)	0	(0.1, 0.1, 0)	(0, 0.4, 0)	(0, 0.5, 0)	(8, 10, 7)
7	(0, 0, 1)	0	(0, 0, 0)	(0, 0, 0)	(0, 0.4, 0)	(5, 30, 6)
8	(1, 1, 1)	0	(-0.1, 0, 0)	(0, -0.2, 0.2)	(0.1, -0.5, 0.1)	(4, 19, 4)
9	(1, 0, 0)	1	(-0.1, 0.1, 0)	(0, 0, 0)	(0, 0, 0)	(4, 28, 4)
10	(1, 0, 1)	0	(0, 0.1, 0.1)	(-0.2, -0.8, -0.3)	(0.1, 0, 0.1)	(4, 8, 4)
11	(1, 0, 0)	1	(0.1, 0, 0)	(0, 1.0, 0)	(0.2, -0.6, 0.2)	(8, 15, 7)
12	(1, 0, 1)	0	(-0.1, -0.2, 0)	(0, 0, 0)	(0.5, 0, 0.5)	(4, 10, 5)
13	(0, 0, 1)	0	(0, 0, -0.1)	(0.2, -0.5, 0.2)	(0, -0.4, 0)	(4, 10, 4)
14	(0, 0, 0)	1	(0, 0, 0)	(0, 0, 0)	(0, 0, 0)	(8, 28, 6)
15	(1, 0, 1)	0	(0, 0.1, 0)	(0.2, -0.5, 0.3)	(0.1, 0.2, 0.1)	(6, 11, 5)
16	(1, 0, 0)	1	(0.1, -0.1, 0.1)	(0, -0.2, 0)	(0.2, -0.3, 0.2)	(5, 25, 6)
17	(0, 1, 1)	0	(0.2, -0.1, 0.1)	(0, 1, 0)	(-0.5, 0, -0.5)	(4, 8, 4)
18	(1, 0, 0)	1	(0, 0, 0)	(0.2, 0.8, 0.5)	(0.2, 0.3, 0.2)	(4, 50, 11)
19	(0, 1, 0)	1	(0, 0.1, 0)	(-0.3, 1.3, 0)	(0, 0, 0)	(6, 25, 6)
20	(1, 0, 0)	1	(-0.1, 0, -0.1)	(0, 0, 0)	(0, 0.5, 0)	(7, 39, 6)

数据组	k-s检验值	临界检验值
部件历史数据	0.334 8	0.409 3
相似部件1数据	0.274 6	0.337 6
相似部件2数据	0.293 9	0.337 6

参数分布	部件历史数据	相似部件1数据	相似部件2数据
π_i(μ)	N(3.698 9, 0.091 2)	N(3.573 2, 0.083 9)	N(3.5267, 0.0756)
π_i(ln(σ²))	N(-2.372 8, 0.388 7)	N(-2.160 3, 0.282 3)	N(-2.008 8, 0.343 9)

均值分布	支持度	融合权值	方差分布	支持度	融合权值
π₁(μ₁)	0.472 1	0.533 9	π₁(ln(σ₁²))	0.725 5	0.540 5
π₂(μ₂)	0.299 9	0.466 1	π₂(ln(σ₂²))	0.616 7	0.459 5

参数	熵值	权重
π_p(μ)	S_p(μ)=0.321 6 S_l(μ)=0.221 6	ω_p(μ)=0.595 4 ω_l(μ)=0.404 6
π_p(ln(σ²))	S_p(ln(σ²))=0.840 1 S_l(ln(σ²))=0.946 5	ω_p(ln(σ²))=0.470 2 ω_l(ln(σ²))=0.529 8

Evaluation method of resilience of battle damage parts under multiple information sources

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Figures/Tables 13

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方法	时间
方法	T=30	T=34	T=38	T=42	T=46
部件历史	0	0.022	0.183	0.485	0.674
相似部件1	0.015	0.210	0.569	0.712	0.729
相似部件2	0.035	0.364	0.678	0.728	0.730
传统估计	0.030	0.207	0.496	0.673	0.722
融合估计	0.014	0.151	0.446	0.655	0.720

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