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针对航空发动机的试验样本量和故障数据少,采用传统的数学平均值法对其平均故障间隔时间(MTBF)评估不能反映其真实可靠性水平的问题,基于Bayes理论,把历史试验数据视为先验信息,采用矩等效方法确定先验分布,然后通过Bayes理论综合现场试验数据,建立了一种基于Bayes理论的航空发动机MTBF评估方法。该方法可以扩大MTBF评估所需的信息量。采用所提出的Bayes方法对某航空发动机MTBF进行评估,得到其MTBF评估值为302.68h,比采用数学平均值法约提高了18.7%,评估结果更符合实际。表明该方法可应用于航空发动机MTBF的评估。
According to the theory of Bayesian theory, the historical test data are regarded as the first of all because the test sample size and fault data of the aeroengine are few and the traditional MTBF evaluation can not reflect the true reliability. Based on the Bayes theory, the Bayesian theory is used to evaluate the a priori distribution of aeroengine MTBF. This method can expand the amount of information required for MTBF assessment. The Bayes method is used to evaluate the MTBF of an aero-engine. The MTBF of the aero-engine is estimated to be 302.68 hours, which is 18.7% higher than that of the mathematical mean. The evaluation results are more realistic. It shows that this method can be applied to the evaluation of aeroengine MTBF.