TY - GEN
T1 - Time-Dependent Reliability Analysis of System Based on Dynamic Bayesian Fault Network
AU - Feng, Yunwen
AU - Song, Zhicen
AU - Lu, Cheng
AU - Yin, Chuxiong
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Prior and sample data information are two key information for Bayesian model to analyze and predict accurately. In order to make the time-dependent analysis results accurately describe the trend of reliability degradation with time, a Dynamic Bayesian fault network model (DBFN) is constructed. Firstly, the prior information is combined with exponential, uniform and normal probability density distribution functions to calculate the failure rate of the system based on curve fitting. Secondly, the backwards reasoning of Bayesian network is used to realize reliability analysis, which can trace the fault phenomenon to the fault cause. Finally, the time-dependent sensitivity analysis is carried out and the trend with time is given. Using the Cabin Door indication system failure as a case, the results show that the failure rate calculated by the dynamic method is closer to the time-varying state of the system than the static value. The method provides an objective means for system time-dependent reliability analysis.
AB - Prior and sample data information are two key information for Bayesian model to analyze and predict accurately. In order to make the time-dependent analysis results accurately describe the trend of reliability degradation with time, a Dynamic Bayesian fault network model (DBFN) is constructed. Firstly, the prior information is combined with exponential, uniform and normal probability density distribution functions to calculate the failure rate of the system based on curve fitting. Secondly, the backwards reasoning of Bayesian network is used to realize reliability analysis, which can trace the fault phenomenon to the fault cause. Finally, the time-dependent sensitivity analysis is carried out and the trend with time is given. Using the Cabin Door indication system failure as a case, the results show that the failure rate calculated by the dynamic method is closer to the time-varying state of the system than the static value. The method provides an objective means for system time-dependent reliability analysis.
KW - Cabin Door indication system
KW - Component
KW - Dynamic Bayes
KW - Reverse reasoning
KW - Sensitivity analysis
KW - Time-dependent reliability
UR - https://www.scopus.com/pages/publications/85126260989
U2 - 10.1109/SRSE54209.2021.00061
DO - 10.1109/SRSE54209.2021.00061
M3 - 会议稿件
AN - SCOPUS:85126260989
T3 - Proceedings - 2021 3rd International Conference on System Reliability and Safety Engineering, SRSE 2021
SP - 331
EP - 336
BT - Proceedings - 2021 3rd International Conference on System Reliability and Safety Engineering, SRSE 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on System Reliability and Safety Engineering, SRSE 2021
Y2 - 26 November 2021 through 28 November 2021
ER -