关键词: 贝叶斯估计, 蒙特卡洛模拟, BP神经网络, 动态贝叶斯, 可靠性

Abstract:

In order to solve the problem of strong subjectivity of the prior data of the dynamic Bayesian network (DBN) obtained when analyzing the reliability of the system, BP neural network was used to optimize the prior data of DBN, taking the burner system of gasifier as the research object. According to the empirical formula of the number of neurons in the hidden layer, the DBN of the gasifier burner system was divided into three subsystems, which were transformed into BP neural network respectively, and the estimated prior distribution of DBN was corresponding to the input function and output function of the BP neural network respectively, the performance of the system is studied and the DBN parameters are optimized by using the characteristics of information is transmitted forward and error backward of BP neural network. The two?way reasoning of the gasifier burner system DBN is carried out to realize the dynamic reliability analysis of the gasifier burner system. The results show that the forward reasoning of the gasifier burner system DBN can obtain the optimized system reliability trend; the reverse reasoning is performed to obtain that the results of key events and weak links remain the same before or after optimization and the weak links are the high value and fluctuation of oxygen coal ratio.

Key words: Bayesian estimation, Monte Carlo simulation, BP neural network, Dynamic Bayesian network, Reliability