Comparison and Verification of Small-Sample Accelerated Life Test Method
The small-sample accelerated life test method(small-sample method)is verified and compared with the traditional accelerated life test methods based on maximum likelihood estimation(MLE method)and best linear unbiased estimation(BLUE method)through extensive Monte Carlo simulations.The results indicate that,in the case of small samples,the coverage probability of the one-sided lower confidence limit of reliable life under normal use stress level obtained by the MLE method is lower than the set confidence degree,failing to meet the confidence requirements and posing a risk in engineering applications.In contrast,the coverage probabilities of the BLUE method and the small-sample method are higher than and equal to the set confidence degree,respectively,both satisfying the confidence require-ments and being safe for engineering applications.Since the small-sample method strictly meets the confidence requirements,it offers higher evaluation accuracy compared to the BLUE method with the same number of samples and saves a considerable number of samples while maintaining the same accuracy.Furthermore,based on the small-sample method,this paper proposes a fusion assessment method for Weibull distribution with the same shape parameters and a small-sample assessment method for normal distribution with complete data but different variances.
Accelerated life testSmall sampleMaximum likelihood estimationBest linear unbiased estimationReliable lifeConfidence limit
万方数据
维普
