The work aims to study the storage life prediction of fuze combined with the intelligent algorithm against the problem that the traditional statistical analysis method adopted for accelerated test data of fuze in a certain motor has high computational complexity and cannot guarantee the storage life prediction accuracy.For the step stress accelerated life test data,the environmental factor method based on Bayesian theory was adopted to convert the storage time at different stress levels.The particle swarm algorithm was improved by evolutionary strategy to adjust and optimize the global parameters of the BP neural network,breaking through the limitations of the traditional method.The converted test time,sample size,and stress level were used as inputs to the network,and the failure count was used as the output to predict the fuze storage life.The trained BP neural network was used to predict the failure count of the fuze under normal stress levels,and then calculate its storage reliability.After 402 iterations,the model found the optimal solution with a prediction error within 1％.Therefore,the combination of step stress accelerated life test and intelligent algorithm can effectively improve the prediction accuracy of fuze storage life.
维普
万方数据
