考虑多故障模式的作战试验维修性评估

doi:10.12382/bgxb.2023.1189

摘要/Abstract

摘要：

作战试验中的维修性评估具有现场试验数据不足、故障模式多样以及装备的修复状态和维修任务多样等特点。将与系统及其故障模式有关的先验信息和现场试验数据融合,可以扩充信息来源,为装备的维修性评估提供新思路。对此,利用离散Bayes定理结合故障数据更新各故障模式组合在试验中存在的概率,并在Bayes框架下估计各故障模式维修时间的分布参数。在此基础上获得受试系统维修性评估结果,并进一步考虑战场抢修条件下受试系统维修度的评估。通过算例分析说明新提出方法的具体应用,验证该方法能够得到比现有方法更为准确的维修时间密度函数估计和系统维修度估计,表明在作战试验背景下,将试验数据考虑为多故障模式的情况有利于更深入地理解维修时间分布的机理,更细致地掌握维修性评估的具体逻辑,从而得到更合理的评估结果。

关键词: 作战试验, 维修性评估, 多故障模式, Bayes理论, 信息融合

Abstract:

The evaluation of military equipment maintainability in operational test is characterized by insufficient field test data,various failure modes,different repair states and maintenance tasks of equipment.The fusion of prior information related to the system and its failure modes and field test data can expand the source of information and provide new ideas for the evaluation of equipment maintainability.To this end,the probability of each failure mode combination in the test is updated using discrete Bayes theorem and fault data.The distribution parameters of maintenance time of each failure mode are estimated under Bayes framework.On this basis,the evaluated results of test system maintainability are obtained,and the evaluation of test system maintainability under the condition of battlefield repair is considered.The application of the proposed method is illustrated with an example.It is verified that the proposed method can be used to more accurately estimate the maintenance time density function and system maintainability compared with the existing methods.Result shows that in the context of operational test,taking the test data as a multi-failure mode is conducive to more deeply understand the mechanism for the distribution of maintenance time and grasp the specific logic for the maintenance assessment in more detail,thus obtaining more reasonable assessment results.

Key words: operational test, maintainability evaluation, multiple failure modes, Bayes theory, information fusion

郝建平, 裴模超, 高翠娟, 张建军, 孙亚飞. 考虑多故障模式的作战试验维修性评估[J]. 兵工学报, 2025, 46(1): 231189-.

HAO Jianping, PEI Mochao, GAO Cuijuan, ZHANG Jianjun, SUN Yafei. Evaluation of Military Equipment Maintainability in Operational Test Considering Multiple Failure Modes[J]. Acta Armamentarii, 2025, 46(1): 231189-.

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参考文献 31

[1]	闫耀东, 肖杰, 秦佰雄. 武器装备作战试验研究[J]. 国防科技, 2009, 30(5):32-36,46.
	YAN Y D, XIAO J, QIN B X. Research on equipment operational test issues[J]. Defense-related Science and Technology, 2009, 30(5):32-36,46. (in Chinese)
[2]	GJB 451B—2021装备通用质量特性术语[S]. 北京: 中央军委装备发展部, 2021.
	GJB451B—2021 General quality characteristics terms for materiel[S]. Beijing: Equipment Development Department of the Central Military Commission, 2021. (in Chinese)
[3]	孙翱, 庄益夫, 王宝和, 等. 武器装备作战试验研究与实践[M]. 北京: 国防工业出版社, 2021.
	SUN A, ZHUANG Y F, WANG B H, et al. Research and application of weapons and equipment combat test[M]. Beijing: National Defense Industry Press, 2021. (in Chinese)
[4]	傅义洲. 习近平出席全军装备工作会议并发表重要讲话[EB/OL].[2014-12-04].
	FU Y Z. Xi Jinping attended the military equipment work conference and delivered an important speech[EB/OL].[2014-12-04]. in Chinese)
[5]	武小悦. 装备性能试验[M] .北京: 国防工业出版社, 2022.
	WU X Y. Equipment performance test[M]. Beijing: National Defense Industry Press, 2022. (in Chinese)
[6]	GJBS/Z TE-ABC-001 装备作战试验工作指南[S]. 北京: 中央军委装备发展部,2019GJBS/Z TE-ABC-001.
	Guide for operational test of equipment[S]. Beijing: Equipment Development Departme-nt of the Central Military Commission, 2019. (in Chinese)
[7]	胡起伟, 王广彦, 石全, 等. 装备战场抢修概论[M]. 北京: 国防工业出版社, 2018.
	HU Q W, WANG G Y, SHI Q, et al. The introduction of equipment battlefield damage assessment and repair[M]. Beijing: National Defense Industry Press, 2018. (in Chinese)
[8]	宁小磊, 吴颖霞. 基于等效样本和实仿融合的现场小子样试验制导精度评估方法[J]. 中国惯性技术学报, 2021, 29(2):264-272.
	NING X L, WU Y X. Guidance accuracy evaluation method of field small sample test based on equivalent sample and real simulation fusion[J]. Journal of Chinese Inertial Technology, 2021, 29(2):264-272. (in Chinese)
[9]	周诚, 徐达, 王小闯. 混合验前分布下的小子样装备维修性验证方法[J]. 电光与控制, 2021, 28(4):73-76.
	ZHOU C, XU D, WANG X C. A small-sample test method for equipment maintainability verification under mixed pre-test distribution[J]. Electronics Optics & Control, 2021, 28(4):73-76. (in Chinese)
[10]	张文杰, 杨华波, 张士峰. 基于Bayes混合验前分布的成败型产品可靠性评估[J]. 兵工学报, 2016, 37(3):505-511. doi: 10.3969/j.issn.1000-1093.2016.03.016
	ZHANG W J, YANG H B, ZHANG S F. Reliability assessment for device with only safe-or-failure pattern based on Bayesian hybrid prior approach[J]. Acta Armamentarii, 2016, 37(3):505-511. (in Chinese)
[11]	WILSON A G, FRONCZYK K M. Bayesian reliability:Combining information[J]. Quality Engineering, 2017, 29(1):119-129.
[12]	DEWALD S L, HOLCOMB R, PARRY S, et al. A Bayesian approach to evaluation of operational testing of land warfare systems[J]. Military Operations Research, 2016, 21(4):23-32.
[13]	STEINER S, DICKINSON R M, FREEMAN L J, et al. Statistical methods for combining information:stryker family of vehicles reliability case study[J]. Journal of Quality Technology, 2015, 47(4):400-415.
[14]	GILMAN J F, FRONCZYK K M, WILSON A G. Bayesian modeling and test planning for multiphase reliability assessment[J]. Quality and Reliability Engineering International, 2019, 35(3):750-760.
[15]	JACKSON C. Adaptive flexible maintainability demonst-ration-a new way to learn more with less resources that improves on test method 9[C]//Proceedings of 2018 Annual Reliability and Maintainability Symposium.Reno,NV, US:IEEE,2018:1-6.
[16]	冯静, 潘正强, 孙权, 等. 小子样复杂系统可靠性信息融合方法及其应用[M]. 北京: 科学出版社, 2015.
	FENG J, PAN Z Q, SUN Q, et al. Methods and applications of reliability information fusion for complex system with small sample test[M]. Beijing: Science Press, 2015. (in Chinese)
[17]	贾祥. 韦布尔分布及其可靠性统计方法[M]. 北京: 科学出版社, 2021.
	JIA X. Weibull distribution and its reliability statistical method[M]. Beijing: Science Press, 2021. (in Chinese)
[18]	GUO J Y, HUANG H Z, PENG W, et al. Bayesian informa-tion fusion for degradation analysis of deteriorating products with individual heterogeneity[J]. Proceedings of the Institution of Mechanical Engineers, Part O:Journal of Risk and Reliability, 2019, 233(4):615-622.
[19]	王有元. 基于Bayes方法及其改进的无失效数据和小子样产品可靠性评估[D]. 成都: 电子科技大学, 2022.
	WANG Y Y. Reliability evaluation of the products with zero-failure data and small sample based on Bayes method and its improvement[D]. Chengdu: University of Electronic Science and Technology of China, 2022. (in Chinese)
[20]	徐达, 关矗, 李闯. 基于叠合度的维修性多源数据融合方法[J]. 火力与指挥控制, 2020, 45(4):35-39.
	XU D, GUAN C, LI C. Research on maintenance multi-source data fusion based on coincidence degree[J]. Fire Control & Command Control, 2020, 45(4):35-39. (in Chinese)
[21]	韩小孩, 张耀辉, 王少华, 等. 可修单元组合任务维修度评估方法研究[J]. 兵工学报, 2016, 37(11):2058-2065. doi: 10.3969/j.issn.1000-1093.2016.11.014
	HAN X H, ZHANG Y H, WANG S H, et al. Research on the evaluation of mission maintainability of repairable unit constitution[J]. Acta Armamentarii, 2016, 37(11):2058-2065. (in Chinese)
[22]	郭齐胜, 彭文成, 董志明, 等. 装备体系试验理论与技术[M]. 北京: 电子工业出版社, 2023.
	GUO Q S, PENG W C, DONG Z M, et al. Test theory and technology of equipment system[M]. Beijing: Publishing House of Electronics Industry, 2023. (in Chinese)
[23]	张晓洁, 唐家银, 唐莉. 基于数据迁移下Bayes特征融合可靠度评估模型[J]. 航空动力学报, 2024, 39(3):178-186.
	ZHANG X J, TANG J Y, TANG L. Bayes feature fusion reliability evaluation model based on data migration[J]. Journal of Aerospace Power, 2024, 39(3):178-186. (in Chinese)
[24]	曹晋华, 程侃. 可靠性数学引论[M].修订版. 北京: 高等教育出版社, 2012.
	CAO J H, CHENG K. Introduction to reliability mathema-tics[M]. revised version. Beijing: Higher Education Press, 2012. (in Chinese)
[25]	于永利, 朱小冬, 郝建平, 等. 系统维修性建模理论与方法[M]. 北京: 国防工业出版社, 2007.
	YU Y L, ZHU X D, HAO J P, et al. System maintainability modeling theory and technology[M]. Beijing: National Defense Industry Press, 2007. (in Chinese)
[26]	鲁培耿. 美国海军陆战队作战试验与鉴定[M]. 北京: 国防工业出版社, 2019.
	LU P G. Operational test and evaluation of US marine crops[M]. Beijing: National Defense Industry Press, 2019. (in Chinese)
[27]	HAMADA M S, MARTZ H F, REESE C S, et al. Bayesian reliability[M].New York,NY, US:Springer, 2008.
[28]	ASH R B, DOLEANS-DADE C A. Probability and measure theory[M]. CA, US: Academic Press, 2000.
[29]	韦来生. 贝叶斯统计[M]. 北京: 高等教育出版社, 2016
	WEI L S. Bayesian statistics[M]. Beijing: Higher Education Press, 2016. (in Chinese)
[30]	王会战. 广义t分布的EM估计[J]. 陕西理工学院学报(自然科学版), 2011, 27(3):51-55.
	WANG H Z. EM estimation for generalized t-distribution[J]. Journal of Shaanxi University of Technology (Natural Science Edition), 2011, 27(3):51-55. (in Chinese)
[31]	甘茂治. 维修性设计与验证[M]. 北京: 国防工业出版社,1995.
	GAN M Z. Maintainability design and verification[M]. Beijing: National Defense Industry Press,1995. (in Chinese)

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最新录用	在线预览	正式出版	最新录用	在线预览	正式出版
0	0	21	23	0	49

来源	本网站	其他网站

次数	49	44
比例	53%	47%

摘要

序号	$u θ i$	$l θ i$	$u σ i 2$	$l σ i 2$	$σ^i 2$
1	33.420	25.420	34.214	14.214	24.925
2	37.420	21.420	34.214	14.214	25.105
3	45.420	13.420	34.214	14.214	25.108
4	19.420	35.420	34.214	14.214	25.091
5	23.420	39.420	34.214	14.214	25.091
6	37.420	21.420	44.214	4.214	28.975
7	37.420	21.420	42.214	2.214	28.025
8	37.420	21.420	46.214	6.214	29.901

序号	$u θ i$	$l θ i$	$u σ i 2$	$l σ i 2$	$σ^i 2$
1	33.420	25.420	34.214	14.214	24.925
2	37.420	21.420	34.214	14.214	25.105
3	45.420	13.420	34.214	14.214	25.108
4	19.420	35.420	34.214	14.214	25.091
5	23.420	39.420	34.214	14.214	25.091
6	37.420	21.420	44.214	4.214	28.975
7	37.420	21.420	42.214	2.214	28.025
8	37.420	21.420	46.214	6.214	29.901

参数 (θ,σ²)	方法	MSE		DVT
参数 (θ,σ²)	方法	$θ^M S E$	$σ^2 M S E$	$θ^D V T$	$σ^2 D V T$
(32,58)	本文方法	2.78	413.96	0.20	4.28
(32,58)	对比方法	3.16	400.35	0.21	-9.24
(32,78)	本文方法	3.79	742.86	0.28	4.89
(32,78)	对比方法	4.51	765.74	0.29	-13.77
(32,98)	本文方法	5.14	1089.14	0.34	4.57
(32,98)	对比方法	6.03	1179.45	0.33	-17.74
(30,78)	本文方法	3.79	843.33	0.14	9.09
(30,78)	对比方法	4.47	711.21	0.13	-14.23
(35,78)	本文方法	3.83	757.78	0.21	6.67
(35,78)	对比方法	4.44	690.50	0.17	-11.61
(40,78)	本文方法	3.94	781.19	0.08	5.20
(40,78)	对比方法	4.73	793.14	0.06	-13.26

参数 (θ,σ²)	方法	MSE		DVT
参数 (θ,σ²)	方法	$θ^M S E$	$σ^2 M S E$	$θ^D V T$	$σ^2 D V T$
(32,58)	本文方法	2.78	413.96	0.20	4.28
(32,58)	对比方法	3.16	400.35	0.21	-9.24
(32,78)	本文方法	3.79	742.86	0.28	4.89
(32,78)	对比方法	4.51	765.74	0.29	-13.77
(32,98)	本文方法	5.14	1089.14	0.34	4.57
(32,98)	对比方法	6.03	1179.45	0.33	-17.74
(30,78)	本文方法	3.79	843.33	0.14	9.09
(30,78)	对比方法	4.47	711.21	0.13	-14.23
(35,78)	本文方法	3.83	757.78	0.21	6.67
(35,78)	对比方法	4.44	690.50	0.17	-11.61
(40,78)	本文方法	3.94	781.19	0.08	5.20
(40,78)	对比方法	4.73	793.14	0.06	-13.26