基于直觉模糊贝叶斯网络的HUD系统多阶段任务可靠性分析
收稿日期: 2021-12-23
修回日期: 2022-02-11
录用日期: 2022-02-21
网络出版日期: 2023-01-06
基金资助
国家重点研发计划(2021YFB1600601);中央高校基本科研业务费中国民航大学资助专项(3122022094)
Reliability analysis for multi-phased mission of HUD system based on intuitionistic fuzzy Bayesian network
Received date: 2021-12-23
Revised date: 2022-02-11
Accepted date: 2022-02-21
Online published: 2023-01-06
Supported by
National Key Research and Development Program of China(2021YFB1600601);Fundamental Research Funds for the Central Universities supported by Civil Aviation University of China(3122022094)
针对国产平视显示系统（HUD）部分功能模块底层可靠性数据积累不足、难以支撑可靠性分析的问题，提出基于直觉模糊贝叶斯网络（IFBN）的平视显示系统多阶段任务可靠性分析方法。首先，研究基于贝叶斯网络的多阶段任务系统（PMS）模型构建方法，并综合考虑可能的共因失效（CCF）影响；其次，研究基于直觉模糊理论的模糊事件失效数据评估方法，提出适用于航电设备的模糊区间划分方法及直觉模糊数与失效数据的转换算法，并采用基于Tω算子的直觉模糊数聚合算法，降低模糊累计；最后，以某型平视显示系统为例进行多阶段任务可靠性计算。所提方法为系统底层失效数据模糊情况下的可靠性分析问题提供支持。
张帆 , 孙紫荆 , 肖国松 , 刘嘉琛 , 王鹏 . 基于直觉模糊贝叶斯网络的HUD系统多阶段任务可靠性分析[J]. 航空学报, 2023 , 44(4) : 226853 -226853 . DOI: 10.7527IS1000-6893.2022.26853
Insufficient bottom reliability data accumulation of some functional modules of the domestic Head Up Display （HUD） system poses difficulty in reliability analysis support. To solve this problem， a reliability analysis method for multi-phased mission of the HUD system based on the Intuitionistic Fuzzy Bayesian Network （IFBN） is proposed. The modeling method for multi-Phased Mission System （PMS） based on the Bayesian network is first studied， and meanwhile the possible Commom Cause Failure （CCF） effects are comprehensively considered. The fuzzy event failure data evaluation method based on the intuitionistic fuzzy theory is then explored， the fuzzy interval division method suitable for avionics equipment and the conversion algorithm between the intuitionistic fuzzy number and failure data proposed， and the intuitionistic fuzzy number aggregation algorithm based on the Tω operator adopted to reduce fuzzy accumulation. Finally， the multi-phased mission reliability is calculated with a HUD system as an example. The proposed method provides support for reliability analysis under the condition of fuzzy bottom failure data.
| 1 | 中国民用航空局. 平视显示器应用发展路线图［R］. 北京： 中国民用航空局， 2012. |
| Civil Aviation Administration of China. Head-up display application development roadmap［R］. Beijing： CAAC， 2012 （in Chinese）. | |
| 2 | KABIR S， WALKER M， PAPADOPOULOS Y， et al. Fuzzy temporal fault tree analysis of dynamic systems［J］. International Journal of Approximate Reasoning， 2016， 77： 20-37. |
| 3 | YAZDI M， KABIR S. A fuzzy Bayesian network approach for risk analysis in process industries［J］. Process Safety and Environmental Protection， 2017， 111： 507-519. |
| 4 | HALLOUL Y， CHIBAN S， AWAD A. Adapted fuzzy fault tree analysis for oil storage tank fire［J］. Energy Sources， Part A： Recovery， Utilization， and Environmental Effects， 2019， 41（8）： 948-958. |
| 5 | 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. |
| 6 | CAI B P， KONG X D， LIU Y H， et al. Application of Bayesian networks in reliability evaluation［J］. IEEE Transactions on Industrial Informatics， 2019，15（4）：2146-2157. |
| 7 | GHOSH A， AHMED S， KHAN F， et al. Process safety assessment considering multivariate non-linear dependence among process variables［J］. Process Safety and Environmental Protection， 2020， 135： 70-80. |
| 8 | GUO C Q， KHAN F， IMTIAZ S. Copula-based Bayesian network model for process system risk assessment［J］. Process Safety and Environmental Protection， 2019， 123： 317-326. |
| 9 | ZAREI E， YAZDI M， ABBASSI R， et al. A hybrid model for human factor analysis in process accidents： FBN-HFACS［J］. Journal of Loss Prevention in the Process Industries， 2019， 57： 142-155. |
| 10 | ROSTAMABADI A， JAHANGIRI M， ZAREI E， et al. A novel Fuzzy Bayesian Network-HFACS （FBN-HFACS） model for analyzing Human and Organization Factors （HOFs） in process accidents［J］. Process Safety and Environmental Protection， 2019， 132： 59-72. |
| 11 | LI Y T， XU D D， SHUAI J. Real-time risk analysis of road tanker containing flammable liquid based on fuzzy Bayesian network［J］. Process Safety and Environmental Protection， 2020， 134： 36-46. |
| 12 | YAN F， XU K L， YAO X W， et al. Fuzzy Bayesian network-bow-Tie analysis of gas leakage during biomass gasification［J］. PLoS One， 2016， 11（7）： e0160045. |
| 13 | ONISAWA T. An approach to human reliability in man-machine systems using error possibility［J］. Fuzzy Sets and Systems， 1988， 27（2）： 87-103. |
| 14 | YU J X， CHEN H C， YU Y， et al. A weakest t-norm based fuzzy fault tree approach for leakage risk assessment of submarine pipeline［J］. Journal of Loss Prevention in the Process Industries， 2019， 62： 103968. |
| 15 | YAZDI M， NIKFAR F， NASRABADI M. Failure probability analysis by employing fuzzy fault tree analysis［J］. International Journal of System Assurance Engineering and Management， 2017， 8（S2）： 1177-1193. |
| 16 | GUO X X， JI J， KHAN F， et al. Fuzzy Bayesian network based on an improved similarity aggregation method for risk assessment of storage tank accident［J］. Process Safety and Environmental Protection， 2021， 149： 817-830. |
| 17 | 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. |
| 18 | YU J X， WU S B， YU Y， et al. Process system failure evaluation method based on a Noisy-OR gate intuitionistic fuzzy Bayesian network in an uncertain environment［J］. Process Safety and Environmental Protection， 2021， 150： 281-297. |
| 19 | KOMAL. Fuzzy reliability analysis of DFSMC system in LNG carriers for components with different membership function［J］. Ocean Engineering， 2018， 155： 278-294. |
| 20 | KUMAR M， YADAV S P. The weakest t-norm based intuitionistic fuzzy fault-tree analysis to evaluate system reliability［J］. ISA Transactions， 2012， 51（4）： 531-538. |
| 21 | 厉海涛， 金光， 周经伦， 等. 贝叶斯网络推理算法综述［J］. 系统工程与电子技术， 2008， 30（5）： 935-939. |
| LI H T， JIN G， ZHOU J L， et al. Survey of Bayesian network inference algorithms［J］. Systems Engineering and Electronics， 2008， 30（5）： 935-939 （in Chinese）. | |
| 22 | KIM J， SHAH A U A， KANG H G. Dynamic risk assessment with Bayesian network and clustering analysis［J］. Reliability Engineering & System Safety， 2020， 201： 106959. |
| 23 | ZHOU D， PAN E S， ZHANG X F， et al. Dynamic model-based saddle-point approximation for reliability and reliability-based sensitivity analysis［J］. Reliability Engineering & System Safety， 2020， 201： 106972. |
| 24 | LEWIS A D， GROTH K M. A dynamic Bayesian network structure for joint diagnostics and prognostics of complex engineering systems［J］. Algorithms， 2020， 13（3）： 64. |
| 25 | LEE D， CHOI D. Analysis of the reliability of a starter-generator using a dynamic Bayesian network［J］. Reliability Engineering & System Safety， 2020， 195： 106628. |
| 26 | XUE S S， LI X G， WANG X F. Fault diagnosis of multi-state gas monitoring network based on fuzzy Bayesian net［J］. Personal and Ubiquitous Computing， 2019， 23（3-4）： 573-581. |
| 27 | YAZDI M. Hybrid probabilistic risk assessment using fuzzy FTA and fuzzy AHP in a process industry［J］. Journal of Failure Analysis and Prevention， 2017， 17（4）： 756-764. |
| 28 | NICOLIS J S， TSUDA I. Chaotic dynamics of information processing： The “magic number seven plus-minus two” revisited［J］. Bulletin of Mathematical Biology， 1985， 47（3）： 343-365. |
| 29 | 中国民用航空局. CCAR-25-R4运输类飞机适航标准 ［S］.北京：中国民用航空局，2016. |
| Civil Aviation Administration of China. Airuorthiness standards for transport aircraft： CCAR 25-R4 ［S］. Beijing： 中国民用航空局， 2016 （in Chinese）. | |
| 30 | 修忠信. 民用飞机系统安全性设计与评估技术概论［M］. 上海： 上海交通大学出版社， 2018. |
| XIU Z X. System safety design & assessmet in civil aircraft［M］. Shanghai： Shanghai Jiao Tong University Press， 2018 （in Chinese）. | |
| 31 | LIU J， YANG J B， RUAN D， et al. Self-tuning of fuzzy belief rule bases for engineering system safety analysis［J］. Annals of Operations Research， 2008， 163（1）： 143-168. |
| 32 | HUANG D， CHEN T， WANG M J J. A fuzzy set approach for event tree analysis［J］. Fuzzy Sets and Systems， 2001， 118（1）： 153-165. |
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