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

摘要/Abstract

摘要：

在进行战时装备战损部件恢复能力评估时,针对收集得到的部件抢修样本信息与实际战场情况不相符的问题,以战场环境和抢修分队维修保障条件为依据,分别建立了样本信息筛选模型和样本抢修时间向实际抢修时间折合的内积模型。采用随机加权法将样本信息转换为参数分布,提出支持度与最大熵相结合的信息源权重确定方法,将相似部件和部件历史信息进行融合,得出部件在对应战场条件下的恢复能力评估结果。算例分析表明,该评估方法可对多种复杂战场条件下的部件恢复能力进行评估,提出的多源融合方法较传统估计方法评估结果更加准确,具有很强的实用性。

关键词: 恢复能力, 评估, 战损部件, 多信息源

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

中图分类号:

宋明昌, 石全, 尤志锋, 李磊, 陈材. 多信息源条件下战损部件恢复能力评估方法[J]. 系统工程与电子技术, 2020, 42(11): 2538-2545.

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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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