Combat-oriented Comprehensive Simulation and Verification Technology for Equipment System RMS

doi:10.16182/j.issn1004731x.joss.25-0116

Abstract

Abstract:

Existing reliability maintainability supportability (RMS) simulation and verification methods for equipment systems are typically conducted under standard conditions and suffer from weak combat environments and task modeling capabilities. To address this limitation, a multi-agent RMS simulation and verification framework was proposed. Key breakthroughs included agent modeling techniques for complex environments and variable tasks, interaction mechanisms among environmental agents, task agents, equipment, and support systems, and a simulation-based comprehensive RMS evaluation method. Case studies demonstrate that the proposed method effectively models complex environments and variable tasks, supports combat-oriented simulation and verification and design scheme evaluation, and meets combat-ready development requirements.

Key words: reliability maintainability supportability(RMS), multi-agent, simulation and verification, complex environment, variable task

CLC Number:

TP391.9

Zhang Yue, Zhang Wenliang, Feng Qiang, Guo Xing, Ren Yi, Wang Zili. Combat-oriented Comprehensive Simulation and Verification Technology for Equipment System RMS[J]. Journal of System Simulation, 2025, 37(7): 1823-1835.

Figures/Tables 15

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Table 1

Fig. 8

Table 2

Design of system

关键分系统	关键组件	指数分布故障率( $λ × 10 - 6 h - 1$ )
动力系统	发动机组件	6.40
动力系统	传动组件	7.21
通信系统	天线组件	4.68
通信系统	计算机组件	2.71
控制系统	导航组件	3.94
控制系统	操控组件	5.26
乘员系统	生命支持组件	3.10
乘员系统	乘员组件	0.11
防护系统	装甲组件	2.43
防护系统	密封组件	6.37

Table 2

Table 3

Environmental factors

场景序号	昼夜影响因子 $a$	地理环境影响因子 $b$	对抗环境影响因子 $c$
Ⅰ	1.0	1.2	1.0
Ⅱ	0.7	1.2	1.0
Ⅲ	1.0	1.2	1.4
Ⅳ	0.7	1.2	1.4

Table 3

Fig. 9

Fig. 10

Fig. 11

Table 4

Evaluation index simulation results

评价指标	场景Ⅰ	场景Ⅱ	场景Ⅲ	场景Ⅳ
使用可用度 $A o$	0.934	0.921	0.908	0.895
保障设备占用率 $O i n s$	0.143	0.169	0.236	0.307
任务可靠度 $R m$	0.717	0.600	0.511	0.405
保障设备满足率 $S i n s$	1	1	0.960	0.906

Table 4

References 23

[1]	吕学志, 冯晓容, 谢智歌. 考虑RMS因素的装备论证多方案分析方法[J]. 指挥控制与仿真, 2024, 46(4): 21-27.
	Xuezhi Lü, Feng Xiaorong, Xie Zhige. Analysis of Alternatives for Equipment Demonstration Considering RMS Factors[J]. Command Control & Simulation, 2024, 46(4): 21-27.
[2]	吴迪, 黄敬. 基于可靠性框图的舰船电力系统可靠性建模[J]. 船电技术, 2019, 39(增1): 28-31.
	Wu Di, Huang Jing. Reliability Modeling for Shipboard Power System Based on Reliability Block Diagram[J]. Marine Electric & Electronic Technology, 2019, 39(S1): 28-31.
[3]	El-Awady Ahmed, Ponnambalam Kumaraswamy. Integration of Simulation and Markov Chains to Support Bayesian Networks for Probabilistic Failure Analysis of Complex Systems[J]. Reliability Engineering & System Safety, 2021, 211: 107511.
[4]	苏续军, 吕学志. 基于离散事件仿真的多状态多阶段任务系统可靠性分析[J]. 兵工学报, 2017, 38(4): 776-784.
	Su Xujun, Xuezhi Lü. Reliability Analysis of Phased-mission System with Multiple States Based on Discrete Event Simulation[J]. Acta Armamentarii, 2017, 38(4): 776-784.
[5]	王家林, 崔楠楠. 基于可靠性框图法的智能变电站继电保护系统可靠性分析[J]. 光源与照明, 2022(3): 138-140.
[6]	李春阳. 发射场典型装备可靠性建模与评估方法研究[D]. 成都: 电子科技大学, 2022.
	Li Chunyang. Reliability Modeling and Assessment of Typical Systems at Space Launch Sites[D]. Chengdu: University of Electronic Science and Technology of China, 2022.
[7]	Feng Qiang, Zhang Yue, Sun Bo, et al. Multi-level Predictive Maintenance of Smart Manufacturing Systems Driven by Digital Twin: a Matheuristics Approach[J]. Journal of Manufacturing Systems, 2023, 68: 443-454.
[8]	Feng Qiang, Guo Xing, Sun Bo, et al. Reliability Modeling of Dynamic Spatiotemporal IoT Considering Autonomy and Cooperativity Based on Multiagent[J]. IEEE Internet of Things Journal, 2024, 11(3): 4712-4721.
[9]	Guo Xing, Feng Qiang, Fan Dongming, et al. An Agent-based Dynamic Reliability Modeling Method for Multistate Systems Considering Fault Propagation: A Case Study on Subsea Christmas Trees[J]. Process Safety and Environmental Protection, 2022, 158: 20-33.
[10]	成城, 陈智杰, 郭子铭, 等. 多智能体协同决策仿真平台研究与开发[J]. 系统仿真学报, 2023, 35(12): 2669-2679.
	Cheng Cheng, Chen Zhijie, Guo Ziming, et al. Research and Development of Simulation Training Platform for Multi-agent Collaborative Decision-making[J]. Journal of System Simulation, 2023, 35(12): 2669-2679.
[11]	石鼎, 燕雪峰, 宫丽娜, 等. 强化学习驱动的海战场多智能体协同作战仿真算法[J]. 系统仿真学报, 2023, 35(4): 786-796.
	Shi Ding, Yan Xuefeng, Gong Lina, et al. Multi-agent Cooperative Combat Simulation in Naval Battlefield with Reinforcement Learning[J]. Journal of System Simulation, 2023, 35(4): 786-796.
[12]	蒋鑫, 孙天宇, 张同舟, 等. 基于多智能体的常规导弹保障仿真试验研究[J]. 环境技术, 2024, 42(12): 115-119.
	Jiang Xin, Sun Tianyu, Zhang Tongzhou, et al. Research on Simulation of Conventional Missile Support Based on Multi Agent[J]. Environmental Technology, 2024, 42(12): 115-119.
[13]	王宇琨, 王泽, 董力维, 等. 基于分层的智能建模方法的多机空战行为建模[J]. 系统仿真学报, 2023, 35(10): 2249-2261.
	Wang Yukun, Wang Ze, Dong Liwei, et al. Research on Multi-aircraft Air Combat Behavior Modeling Based on Hierarchical Intelligent Modeling Methods[J]. Journal of System Simulation, 2023, 35(10): 2249-2261.
[14]	罗尚伟. 基于多Agent的装备体系任务可靠性建模与仿真研究[D]. 重庆: 重庆邮电大学, 2021.
	Luo Shangwei. Research on Modeling and Simulation of Equipment System of Systems Mission Reliability Based on Multi-Agent[D]. Chongqing: Chongqing University of Posts and Telecommunications, 2021.
[15]	赵慧慧. 基于多智能体仿真的舰船电子信息系统任务可靠性评估方法[D]. 哈尔滨: 哈尔滨工业大学, 2023.
	Zhao Huihui. Mission Reliability Evaluation Method of Ship Electronic Information System Basedon Multi-agent Simulation[D]. Harbin: Harbin Institute of Technology, 2023.
[16]	Song Yanjie, Junwei Ou, Pedrycz W, et al. Generalized Model and Deep Reinforcement Learning-based Evolutionary Method for Multitype Satellite Observation Scheduling[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2024, 54(4): 2576-2589.
[17]	A da Rosa Mauro, Armando M Leite da Silva, Miranda Vladimiro. Multi-agent Systems Applied to Reliability Assessment of Power Systems[J]. International Journal of Electrical Power & Energy Systems, 2012, 42(1): 367-374.
[18]	丁善婷, 王淼, 董正琼, 等. 基于多智能体的舰载雷达可靠性、维修性及保障性评估方法[J]. 中国舰船研究, 2021, 16(6): 52-60.
	Ding Shanting, Wang Miao, Dong Zhengqiong, et al. Multi-agent-based RMS Assessment Method for Shipboard Radar[J]. Chinese Journal of Ship Research, 2021, 16(6): 52-60.
[19]	Lohmer Jacob, Bugert Niels, Lasch Rainer. Analysis of Resilience Strategies and Ripple Effect in Blockchain-coordinated Supply Chains: An Agent-based Simulation Study[J]. International Journal of Production Economics, 2020, 228: 107882.
[20]	Cheng Congcong, Bai Guanghan, Zhang Yunan, et al. Resilience Evaluation for UAV Swarm Performing Joint Reconnaissance Mission[J]. Chaos, 2019, 29(5): 053132.
[21]	李超, 李文斌, 高阳. 图多智能体任务建模视角下的协作子任务行为发现[J]. 计算机研究与发展, 2024, 61(8): 1904-1916.
	Li Chao, Li Wenbin, Gao Yang. Discovering Coordinated Subtask Patterns from a Graphical Multi-agent Task Modeling Perspective[J]. Journal of Computer Research and Development, 2024, 61(8): 1904-1916.
[22]	周德海, 应杰, 韩鹏. 海战场环境对反水雷作战影响效应建模仿真分析[J]. 兵器装备工程学报, 2024, 45(6): 127-133, 290.
	Zhou Dehai, Ying Jie, Han Peng. Modeling and Simulation Analysis of the Impact of Naval Battlefield Environment on Anti-mine Warfare Effectiveness[J]. Journal of Ordnance Equipment Engineering, 2024, 45(6): 127-133, 290.
[23]	Guo Xing, Feng Qiang, Wu Zeyu, et al. Multi-agent-based Failure Modeling for Uncrewed Swarm Systems Considering Cross-layer Diffusion Characteristics[J]. Reliability Engineering & System Safety, 2025, 257, Part A: 110831.

场景序号	地理环境	昼夜环境	对抗环境	场景概率
Ⅰ	水陆两栖	白天环境	无对抗	0.1
Ⅱ	水陆两栖	夜晚环境	无对抗	0.2
Ⅲ	水陆两栖	白天环境	有对抗	0.4
Ⅳ	水陆两栖	夜晚环境	有对抗	0.3