Concerning the issue of high-dimensions and low-failure probabilities including implicit and highly nonlinear limit state function, reliability analysis based on the directional importance sampling in combination with...Concerning the issue of high-dimensions and low-failure probabilities including implicit and highly nonlinear limit state function, reliability analysis based on the directional importance sampling in combination with the radial basis function (RBF) neural network is used, and the RBF neural network based on first-order reliability method (FORM) is to approximate the unknown implicit limit state functions and calculate the most probable point (MPP) with iterative algorithm. For good efficiency, based on the ideas that directional sampling reduces dimensionality and importance sampling focuses on the domain contributing to failure probability, the joint probability density function of importance sampling is constructed, and the sampling center is moved to MPP to ensure that more random sample points draw belong to the failure domain and the simulation efficiency is improved. Then the numerical example of initiating explosive devices for rocket booster explosive bolts demonstrates the applicability, versatility and accuracy of the approach compared with other reliability simulation algorithm.展开更多
The amount of information required for reliability assessment of success/failure products is defined and the formula of equivalent information method is deduced. Based on the formula, a method for assessing reliabilit...The amount of information required for reliability assessment of success/failure products is defined and the formula of equivalent information method is deduced. Based on the formula, a method for assessing reliability of initiating explosive devices with small sample size is established. Compared with the assessment method for devices with large sample size, the new method is correct and feasible and can be used to assess the reliability of initiating explosive devices with high reliability requirements.展开更多
文摘Concerning the issue of high-dimensions and low-failure probabilities including implicit and highly nonlinear limit state function, reliability analysis based on the directional importance sampling in combination with the radial basis function (RBF) neural network is used, and the RBF neural network based on first-order reliability method (FORM) is to approximate the unknown implicit limit state functions and calculate the most probable point (MPP) with iterative algorithm. For good efficiency, based on the ideas that directional sampling reduces dimensionality and importance sampling focuses on the domain contributing to failure probability, the joint probability density function of importance sampling is constructed, and the sampling center is moved to MPP to ensure that more random sample points draw belong to the failure domain and the simulation efficiency is improved. Then the numerical example of initiating explosive devices for rocket booster explosive bolts demonstrates the applicability, versatility and accuracy of the approach compared with other reliability simulation algorithm.
基金Supported by the Ministerial Level Foundation(9140C3705041003)
文摘The amount of information required for reliability assessment of success/failure products is defined and the formula of equivalent information method is deduced. Based on the formula, a method for assessing reliability of initiating explosive devices with small sample size is established. Compared with the assessment method for devices with large sample size, the new method is correct and feasible and can be used to assess the reliability of initiating explosive devices with high reliability requirements.