考虑抑制耦合关系的5G业务故障分析方法

doi:10.12305/j.issn.1001-506X.2022.06.33

Abstract

Abstract:

The share of infrastructure between 5G network applications makes its fault logic more complicated, which is reflected in the forming of coupling relationships between application faults. Suppress coupling, as a special type of application fault coupling relationship, embodies the mutual suppression between application faults, which greatly affects the operation of applications. In order to analyze the 5G application fault under the influence of suppressing coupling, a coupled fault tree analysis method is proposed, which can analyse application fault under the influence of suppress coupling relationships. Through the analysis and comparison of 5G rubber-tyred container gantry crane (RTG) cases, the effectiveness of the method proposed is proved.

Key words: application fault analysis, suppress coupling, coupled fault tree, 5G rubber-tyred container gantry crane (RTG)

CLC Number:

TN945

Kun CHEN, Ning HUANG, Xiangwei WU, Jingmeng ZHAO. A 5G application fault analysis method considering suppress coupling relationship[J]. Systems Engineering and Electronics, 2022, 44(6): 2043-2050.

Figures/Tables 8

Fig.1

Fig.2

Table 1

Fig.3

Table 2

Table 3

Fig.4

Fig.5

References 30

1	ZHANG R Z, NING L, LI M K, et al. Feature extraction of tra-jectories for mobility modeling in 5G NB-IoT networks[EB/OL]. [2021-11-03]. https://link.springer.com/article/10.1007/s11276-021-02742-8#citeas.
2	REHMAN A , HASEEB K , SABA T , et al. Mobility support 5G architecture with real-time routing for sustainable smart cities[J]. Sustainability, 2021, 13 (16): 9092. doi: 10.3390/su13169092
3	ROXANA R , CEDRIC A . The governance of 5G infrastructure: between path dependency and risk-based approaches[J]. Journal of Cybersecurity, 2021, 7 (1): 017.
4	侯筱宇, 张光毅. 多巴胺诱导大鼠海马脑片Ca²⁺钙调素依赖性蛋白激酶Ⅱ活性的抑制作用[J]. 中国药理学报, 1999, 20 (10): 902.
	HOU X Y , ZHANG G Y . Dopamine induced inhibition of Ca²⁺ calmodulin-dependent protein kinase Ⅱ activity in rat hippocampal brain slices[J]. Acta Pharmacologica Sinica, 1999, 20 (10): 902.
5	李巍, 陈小云. 电路交换域与IMS互通研究[J]. 信息安全与通信保密, 2006, 9, 83- 85.
	LI W , CHEN X Y . Interworking of circuit-switched and IMS[J]. China Information Security, 2006, 9, 83- 85.
6	GUO J L, CUI L, LIU Y, et al. A compact ultra-wideband crossed-dipole antenna for 2G/3G/4G/IMT/5G customer premise equipment applications[EB/OL]. [2021-11-03]. https://link.springer.com/article/10.1631/FITEE.2000456#citeas.
7	YANG K , ZHOU N , ROSTE T . LTE massive MIMO (Pre-5G) test with an updated boat user terminal solution for land-to-boat scenarios in Oslo fjord[J]. IOP Conference Series: Materials Science and Engineering, 2020, 929 (1): 12010- 12019. doi: 10.1088/1757-899X/929/1/012010
8	BISTOUNI F , JAHANSHAHI M . Analyzing the reliability of shuffle-exchange networks using reliability block diagrams[J]. Reliability Engineering & System Safety, 2014, 132, 97- 106.
9	RELKAR , ANAND S . Risk analysis of equipment failure through failure mode and effect analysis and fault tree analysis[J]. Journal of Failure Analysis and Prevention, 2021, 21 (3): 793- 805. doi: 10.1007/s11668-021-01117-7
10	SAKAR C , TOZ A C , BUBER M , et al. Risk analysis of grounding accidents by mapping a fault tree into a Bayesian network[J]. Applied Ocean Research, 2021, 113 (1): 1- 12.
11	TABESH M, ROOZBAHANI A, HADIGOL F, et al. Risk assessment of water treatment plants using fuzzy fault tree analysis and Monte Carlo simulation[EB/OL]. [2021-11-03]. https://link.springer.com/article/10.1007/s40996-020-00498-3#citeas.
12	KUMAR M , KAUSHIK M . System failure probability evaluation using fault tree analysis and expert opinions in intuitionistic fuzzy environment[J]. Journal of Loss Prevention in the Process Industries, 2020, 67, 104236. doi: 10.1016/j.jlp.2020.104236
13	LEWIS E E . A load-capacity interference model for common-mode failures in 1-out-of-2: G systems[J]. IEEE Trans.on Reliability, 2001, 50 (1): 47- 51. doi: 10.1109/24.935017
14	CHIACCHIO F , CACIOPPO M , D'URSO D , et al. A Weibull-based compositional approach for hierarchical dynamic fault trees[J]. Reliability Engineering & System Safety, 2013, 109, 45- 52.
15	MISRA K B . Handbook of performability engineering[M]. London: Springer, 2008.
16	RAUZY A. Binary decision diagrams for reliability studies[M]// MISRA K B, ed. Handbook of Performability Engineering. London: Springer London, 2008: 381-395.
17	BOBBIO A , PORTINALE L , MINICHINO M , et al. Improving the analysis of dependable systems by mapping fault trees into Bayesian networks[J]. Reliability Engineering & System Safety, 2001, 71 (3): 249- 260.
18	JUNG W S, YANG J E. Fast calculation methods of importance measures in the fault tree analysis[C]//Proc. of the International Topical Meeting on Probabilistic Safety Assessment and Analysis, 2011: 95-106.
19	LINDHE A , NORBERG T , ROSEN L . Approximate dynamic fault tree calculations for modelling water supply risks[J]. Reliability Engineering & System Safety, 2012, 106, 61- 71.
20	BOUDALI H , CROUZEN P , STOELINGA M . A rigorous, compositional, and extensible framework for dynamic fault tree analysis[J]. IEEE Trans.on Dependable and Secure Computing, 2010, 7 (2): 128- 143. doi: 10.1109/TDSC.2009.45
21	JIANG G J, LI Z Y, QIAO G, et al. Reliability analysis of dynamic fault tree based on binary decision diagrams for explosive vehicle[EB/OL]. [2020-12-03]. https://www.hindawi.com/journals/mpe/2021/5559475/.
22	PAN Z J , NONAKA Y . A new approach for reliability estimation of systems with complex common cause failures[J]. International Journal of Reliability, Quality and Safety Engineering, 1994, 1 (2): 291- 298. doi: 10.1142/S0218539394000210
23	PARRY G W . Comments on basic aspects of stochastic reliabili ty analysis for redundancy systems[J]. Reliability Engineering & System Safety, 1989, 24, 377- 381.
24	王家序, 周青华, 肖科, 等. 不完全共因失效系统动态故障树模型分析方法[J]. 系统工程与电子技术, 2012, 34 (5): 1062- 1067. doi: 10.3969/j.issn.1001-506X.2012.05.37
	WANG J X , ZHOU Q H , XIAO K , et al. Dynamic fault tree model analysis of systems subjected to incomplete common-cause failure[J]. Systems Engineering and Electronics, 2012, 34 (5): 1062- 1067. doi: 10.3969/j.issn.1001-506X.2012.05.37
25	JAIN M , KUMAR P . Availability analysis of machining system with common cause failure, inspection and imperfect repair[J]. Proceedings of the National Academy of Sciences, 2021, 91, 451- 460.
26	JIN K , SON K , HEO G . Copula-based common cause failure models with Bayesian inferences[J]. Nuclear Engineering and Technology, 2021, 53 (2): 357- 367. doi: 10.1016/j.net.2020.08.014
27	黄宁, 伍志韬. 网络可靠性评估模型与算法综述[J]. 系统工程与电子技术, 2013, 35 (12): 2651- 2660. doi: 10.3969/j.issn.1001-506X.2013.12.32
	HUANG N , WU Z T . Survey of network reliability evaluation models and algorithms[J]. Systems Engineering and Electronics, 2013, 35 (12): 2651- 2660. doi: 10.3969/j.issn.1001-506X.2013.12.32
28	CHEN J Y , FENG Y W , GUO Y N , et al. Dynamic safety assessment of aircraft mechanism using extremum framework with fault tree analysis[J]. IOP Conference Series Materials Science and Engineering, 2021, 1043 (2): 022052. doi: 10.1088/1757-899X/1043/2/022052
29	PETERSEN , MAYA L . Compound treatments, transportability, and the structural causal model[J]. Epidemiology, 2011, 22 (3): 378- 381. doi: 10.1097/EDE.0b013e3182126127
30	JUDEA P . An introduction to causal inference[J]. The International Journal of Biostatistics, 2010, 6 (2): 7.

名称	符号	应用范围
交互抑制耦合		同层业务间的故障横向分析
单向抑制耦合		同层业务间的故障横向分析

基础事件	故障概率
M1	p(M1=1)=0.01
M2	p(M2=1)=0.01
M3	p(M3=1)=0.01
M4	p(M4=1)=0.01
M5	p(M5=1)=0.01
M6	p(M6=1)=0.01
M7	p(M7=1)=0.01

事件发生	条件概率数值
M2=1	p(M3=1)=0.006
AM2-2=1	p(AM2-1=1)=0.03
Sa1-3=1	p(Sa1-5=1)=0.06
M6=1	p(M7=1)=0.005
AM2-1=1	p(AM2-2=1)=0.04
Sa1-2=1	p(Sa1-4=1)=0.05

A 5G application fault analysis method considering suppress coupling relationship

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 8

References 30

Related Articles 0

Recommended Articles

Metrics

Comments