基于广义连续时间贝叶斯网络的指挥控制网络系统可靠性分析

doi:10.12305/j.issn.1001-506X.2022.12.34

系统工程与电子技术 ›› 2022, Vol. 44 ›› Issue (12): 3880-3886.doi: 10.12305/j.issn.1001-506X.2022.12.34

基于广义连续时间贝叶斯网络的指挥控制网络系统可靠性分析

李懿凡^1,², 钱华明^2,³, 黄洪钟^1,^2,*, 张庭瑜^1,², 黄土地^1,²

1. 电子科技大学机械与电气工程学院, 四川成都 611731
2. 电子科技大学系统可靠性与安全性研究中心, 四川成都 611731
3. 重庆大学机械传动国家重点实验室, 重庆 400044

收稿日期:2022-09-10 出版日期:2022-11-14 发布日期:2022-11-24
通讯作者: 黄洪钟
作者简介:李懿凡(1989—), 男, 博士研究生, 主要研究方向为系统可靠性建模与评估|钱华明(1992—), 男, 讲师, 博士, 主要研究方向为时变可靠性分析、结构可靠性优化|黄洪钟(1963—), 男, 教授, 博士研究生导师, 博士, 主要研究方向为系统可靠性与安全性、剩余寿命预测|张庭瑜(1993—), 男, 博士研究生, 主要研究方向为系统可靠性建模、故障诊断|黄土地(1995—), 男, 博士研究生, 主要研究方向为系统可靠性建模、可靠性优化
基金资助:
国家自然科学基金(51875089)

Reliability analysis of command and control network system based on generalized continuous time Bayesian network

Yifan LI^1,², Huaming QIAN^2,³, Hongzhong HUANG^1,^2,*, Tingyu ZHANG^1,², Tudi HUANG^1,²

1. School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
2. Center for System Reliability and Safety, University of Electronic Science and Technology of China, Chengdu 611731, China
3. State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China

Received:2022-09-10 Online:2022-11-14 Published:2022-11-24
Contact: Hongzhong HUANG

摘要/Abstract

摘要：

指挥控制网络作为典型的复杂网络系统，其可靠性通常可以利用动态贝叶斯网络(dynamic Bayesian network, DBN)来分析。由于DBN并不能很好地解决包含时间和非时间事件的指挥控制网络系统可靠性问题，因此提出一种具有二态条件概率表的广义连续时间贝叶斯网络(generalized continuous time Bayesian network, GCTBN)的建模方法。在此模型的基础上，得到了不同逻辑门的可靠度算法求解，进而针对指挥控制网络系统提出了无线电传播、通信设备以及网络的可靠性分析方法，从而综合地考虑到指挥控制网络执行不同任务的可靠性问题。通过对指挥控制网络可靠性问题的实际案例分析，验证了所提方法的有效性。

关键词: 指挥控制网络, 二态条件概率表, 贝叶斯网络, 可靠性分析

Abstract:

As a typical complex network system, the reliability of command and control network can usually be analyzed by using dynamic Bayesian network (DBN). However, DBN cannot solve the reliability problem of command and control network system including time and non-time events. To end this, a modeling method of generalized continuous time Bayesian network (GCTBN) with two state conditional probability table is proposed. Based on this model, the reliability algorithm of different logic gates is proposed, and the reliability analysis method of wireless propagation, communication devices and network is proposed for command and control network system, so as to comprehensively consider the reliability problem of command and control network to perform different tasks. The effectiveness of the proposed method is verified by a real case study of command and control network reliability.

Key words: command and control network, conditional probability table of two states, Bayesian network, reliability analysis

中图分类号:

TN91

李懿凡, 钱华明, 黄洪钟, 张庭瑜, 黄土地. 基于广义连续时间贝叶斯网络的指挥控制网络系统可靠性分析[J]. 系统工程与电子技术, 2022, 44(12): 3880-3886.

Yifan LI, Huaming QIAN, Hongzhong HUANG, Tingyu ZHANG, Tudi HUANG. Reliability analysis of command and control network system based on generalized continuous time Bayesian network[J]. Systems Engineering and Electronics, 2022, 44(12): 3880-3886.

图/表 12

图1

表1

图2

表2

图3

图4

图5

图6

表3

表4

表5

图7

参考文献 25

1	LIU Q , RENIERS A C F , JOHANNSEN U , et al. Improved probing reliability in antenna-on-chip measurements[J]. IEEE Antennas and Wireless Propagation Letters, 2018, 17 (9): 1745- 1749. doi: 10.1109/LAWP.2018.2865605
2	蓝羽石, 易侃, 王珩, 等. 网络化C4ISR系统结构时效性分析方法[J]. 系统工程与电子技术, 2013, 35 (9): 1908- 1914. doi: 10.3969/j.issn.1001-506X.2013.09.18
	LAN Y S , YI K , WANG H , et al. Delay assessment method for networked C4ISR system architecture[J]. Systems Engineering and Electronics, 2013, 35 (9): 1908- 1914. doi: 10.3969/j.issn.1001-506X.2013.09.18
3	张萌萌, 陈洪辉, 罗爱民, 等. 基于动态调整的C4ISR系统结构灵活性分析[J]. 系统工程与电子技术, 2016, 38 (3): 563- 568.
	ZHANG M M , CHEN H H , LUO A M , et al. C4ISR system structure flexibility analysis based on dynamic adjustment[J]. Systems Engineering and Electronics, 2016, 38 (3): 563- 568.
4	PARK J H . Time-dependent reliability of wireless networks with dependent failures[J]. Reliability Engineering & System Safety, 2017, 165, 47- 61.
5	LI B , SALUCCI M , TANG W , et al. Reliable field strength prediction through an adaptive total-variation CS technique[J]. IEEE Antennas and Wireless Propagation Letters, 2020, 19 (9): 1566- 1570. doi: 10.1109/LAWP.2020.3010410
6	BEKASIEWICZ A , KOZIEL S . Reliable multistage optimization of antennas for multiple performance figures in highly dimensional parameter spaces[J]. IEEE Antennas and Wireless Propagation Letters, 2019, 18 (7): 1522- 1526. doi: 10.1109/LAWP.2019.2921610
7	SULYMAN A I , NASSAR A T , SAMIMI M K , et al. Radio propagation path loss models for 5G cellular networks in the 28 GHz and 38 GHz millimeter-wave bands[J]. IEEE Communications Magazine, 2014, 52 (9): 78- 86. doi: 10.1109/MCOM.2014.6894456
8	ZADEHPARIZI F , JAM S . Increasing reliability of frequency-reconfigurable antennas[J]. IEEE Antennas and Wireless Propagation Letters, 2018, 17 (5): 920- 923. doi: 10.1109/LAWP.2018.2823688
9	XIANG S H , YANG J . Performance reliability evaluation for mobile ad hoc networks[J]. Reliability Engineering & System Safety, 2018, 169, 32- 39.
10	HATA M . Empirical formula for propagation loss in land mobile radio services[J]. IEEE Trans.on Vehicular Technology, 1980, 29 (3): 317- 325. doi: 10.1109/T-VT.1980.23859
11	SUN S , RAPPAPORT T S , THOMAS T A , et al. Investigation of prediction accuracy, sensitivity, and parameter stability of large-scale propagation path loss models for 5G wireless communications[J]. IEEE Trans.on Vehicular Technology, 2016, 65 (5): 2843- 2860. doi: 10.1109/TVT.2016.2543139
12	YOO S K , COTTON S L , CHUN Y J , et al. Channel characteristics of dynamic off-body communications at 60 GHz under line-of-sight (LOS) and non-LOS conditions[J]. IEEE Antennas and Wireless Propagation Letters, 2017, 16, 1553- 1556. doi: 10.1109/LAWP.2017.2650863
13	MI J , BEER M , LI Y F , et al. Reliability and importance analysis of uncertain system with common cause failures based on survival signature[J]. Reliability Engineering & System Safety, 2020, 201, 106988.
14	LI X , HUANG H Z , LI Y F , et al. Reliability evaluation for VHF and UHF bands under different scenarios via propagation loss model[J]. Eksploatacja i Niezawodnosc-Maintenance and Reliability, 2019, 21 (3): 375- 383. doi: 10.17531/ein.2019.3.3
15	KASZUBOWSKA A , HU L , BARRY L P . Remote down conversion with wavelength reuse for the radio/fiber uplink connection[J]. IEEE Photonics Technology Letters, 2006, 18 (4): 562- 564. doi: 10.1109/LPT.2005.863995
16	FOTYGA G , CZARNIEWSKA M , LAMECKI A , et al. Reliable greedy multipoint model-order reduction techniques for finite-element analysis[J]. IEEE Antennas and Wireless Propagation Letters, 2018, 17 (5): 821- 824. doi: 10.1109/LAWP.2018.2817391
17	REBELLO S , YU H , MA L . An integrated approach for system functional reliability assessment using dynamic Bayesian network and hidden Markov model[J]. Reliability Engineering & System Safety, 2018, 180, 124- 135.
18	LI Y F , MI J , LIU Y , et al. Dynamic fault tree analysis based on continuous-time Bayesian networks under fuzzy numbers[J]. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 2015, 229 (6): 530- 541. doi: 10.1177/1748006X15588446
19	LI Y F , MI J , HUANG H Z , et al. System reliability modeling and assessment for solar array drive assembly based on Bayesian networks[J]. Eksploatacja i Niezawodnosc-Maintenance and Reliability, 2013, 15 (2): 117- 122.
20	BOUDALI H , DUGAN J B . A discrete-time Bayesian network reliability modeling and analysis framework[J]. Reliability Engineering & System Safety, 2005, 87 (3): 337- 349.
21	BOUDALI H , DUGAN J B . A continuous-time Bayesian network reliability modeling, and analysis framework[J]. IEEE Trans.on Reliability, 2006, 55 (1): 86- 97.
22	MURPHY K . The Bayes net toolbox for Matlab[J]. Computing Science & Statistics, 2001, 33 (2): 1024- 1034.
23	MI J , LI Y F , PENG W , et al. Reliability analysis of complex multi-state system with common cause failure based on evidential networks[J]. Reliability Engineering & System Safety, 2018, 174, 71- 81.
24	LI Y F , HUANG H Z , MI J , et al. Reliability analysis of multi-state systems with common cause failures based on Bayesian network and fuzzy probability[J]. Annals of Operations Research, 2022, 311, 195- 209.
25	LI Y F , LIU Y , HUANG T , et al. Reliability assessment for systems suffering common cause failure based on Bayesian networks and proportional hazards model[J]. Quality and Reliability Engineering International, 2020, 36 (7): 2509- 2520.

A		1	2
B	1	$1-\frac{\mathrm{e}^{-\lambda t}\left(1-\mathrm{e}^{-\alpha \lambda t}\right)}{\alpha\left(1-\mathrm{e}^{-\lambda t}\right)}$	1-e^-αλt
B	2	$\frac{\mathrm{e}^{-\lambda t}\left(1-\mathrm{e}^{-\alpha \lambda t}\right)}{\alpha\left(1-\mathrm{e}^{-\lambda t}\right)}$	e^-αλt

节点	描述	节点	描述
T	通讯故障	X₃	天线故障
M₁	传输故障	X₄	耳塞故障
M₂	接收故障	X₅	电子屏故障
M₃	电力系统故障	X₆	电池故障
X₁	麦克风故障	X₇	电池故障
X₂	基带故障	-	-

D₁		1	2
X_i(i=1-7)	1	$1-\frac{\mathrm{e}^{-\lambda_i t} \int_0^t f_{D_1}\left(t_{D_1}\right) \mathrm{e}^{\lambda_i t D_1} \mathrm{~d} t_{D_1}}{\int_0^t f_{D_1}\left(t_{D_1}\right) \mathrm{d} t_{D_1}}$	0
X_i(i=1-7)	2	$\frac{\mathrm{e}^{-\lambda_i t} \int_0^t f_{D_1}\left(t_{D_1}\right) \mathrm{e}^{\lambda_i t D_1} \mathrm{~d} t_{D_1}}{\int_0^t f_{D_1}\left(t_{D_1}\right) \mathrm{d} t_{D_1}}$	1

节点	设备	λ	节点	设备	λ
X₁	麦克风	0.000 040	X₅	电子屏	0.000 010
X₂	基带	0.000 020	X₆	电池故障	0.000 067
X₃	天线	0.000 023	X₇	电池故障	0.000 067
X₄	耳塞	0.000 040	-	-	-

W₁		1	2
W₂	1	F_W₂	F_W₂
W₂	2	R_W₂	R_W₂

基于广义连续时间贝叶斯网络的指挥控制网络系统可靠性分析

Reliability analysis of command and control network system based on generalized continuous time Bayesian network

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 12

参考文献 25

相关文章 15

编辑推荐

Metrics

本文评价

[1]	王鹏, 孙紫荆, 张帆, 肖国松. 考虑概率型共因失效的多阶段任务系统可靠性分析模型[J]. 系统工程与电子技术, 2022, 44(12): 3887-3898.
[2]	乔殿峰, 梁彦, 马超雄, 杨心语, 汪冕, 李建国. 多域作战下的群目标意图识别与预测[J]. 系统工程与电子技术, 2022, 44(11): 3403-3412.
[3]	刘延钊, 黄志球, 沈国华, 王金永, 徐恒. 基于决策树和BN的自动驾驶车辆行为决策方法[J]. 系统工程与电子技术, 2022, 44(10): 3143-3154.
[4]	赵禄达, 王斌. 基于RS-DBN的电子对抗目标清单生成方法[J]. 系统工程与电子技术, 2021, 43(9): 2373-2382.
[5]	陈洪转, 赵爱佳, 李腾蛟, 蔡匆聪, 程硕, 徐春丽. 基于故障树的复杂装备模糊贝叶斯网络推理故障诊断[J]. 系统工程与电子技术, 2021, 43(5): 1248-1261.
[6]	孙雪, 黄志球, 沈国华, 王金永, 徐恒. 基于本体和BN的无人车行为决策方法[J]. 系统工程与电子技术, 2021, 43(2): 452-465.
[7]	毛泽龙, 王治华, 吴琼, 刘成瑞. 双指标阶段性退化建模及可靠性分析[J]. 系统工程与电子技术, 2021, 43(12): 3725-3731.
[8]	赵长啸, 李浩, 董磊, 王鹏. 基于STPA-Bayes模型的机载平视显示系统安全性分析与评价[J]. 系统工程与电子技术, 2020, 42(5): 1083-1092.
[9]	曾强, 黄政, 魏曙寰. 融合专家先验知识和单调性约束的贝叶斯网络参数学习方法[J]. 系统工程与电子技术, 2020, 42(3): 646-652.
[10]	孟光磊, 周铭哲, 朴海音, 张慧敏. 基于协同战术识别的双机编队威胁评估方法[J]. 系统工程与电子技术, 2020, 42(10): 2285-2293.
[11]	陈宇奇, 徐廷学, 李志强, 李海君. 基于证据GO法的复杂多态系统动态可靠性分析[J]. 系统工程与电子技术, 2020, 42(1): 230-237.
[12]	李孝鹏, 黄洪钟, 李福秋. 基于PRA的复杂航天多阶段任务系统可靠性分析[J]. 系统工程与电子技术, 2019, 41(9): 2141-2147.
[13]	孙海文, 谢晓方, 孙涛, 张龙杰. 小样本数据缺失状态下DBN舰艇编队防空目标威胁评估方法[J]. 系统工程与电子技术, 2019, 41(6): 1300-1308.
[14]	吕学志, 胡晓峰, 吴琳, 贺筱媛. 基于改进竞争性假设分析的战役企图分析方法[J]. 系统工程与电子技术, 2019, 41(3): 555-563.
[15]	刘久富, 丁晓彬, 郑锐, 王彪, 刘海阳, 王志胜. 混沌量子粒子群的权重类条件贝叶斯网络分类器参数学习[J]. 系统工程与电子技术, 2019, 41(10): 2304-2309.