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Warranty calculations for missiles with only current-status data, using Bayesian methods

机译:使用贝叶斯方法对仅具有当前状态数据的导弹进行保修计算

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Recent catastrophic failures of the rocket motor on a US missile caused concern in the US Navy that the missile might not be serviceable past its estimated service life of 20 years. This paper analyzes nearly 2000 firings of the motor under field conditions using classical maximum likelihood estimation (MLE) methods and Bayesian methods. MLE methods indicate that there could be less than a 1% chance that the motor will survive past 20 years of life and are not considered credible. Bayesian methods indicate that there is better than a 99% chance that the motors will survive past 20 years of life. The authors present reasons for preferring the Bayesian analysis and discuss testing schemes for more precise estimates. They comment on the lack of data to perform degradation analysis as a function of temperature cycling for the motor and make recommendations for future missile system data collection.
机译:最近一枚美国导弹上的火箭发动机发生灾难性故障,在美国海军中引起了人们的担忧,即该导弹可能在其估计的20年使用寿命后仍无法使用。本文使用经典最大似然估计(MLE)方法和贝叶斯方法分析了在现场条件下近2000次电动机的点火。 MLE方法表明,电动机可以在20年以上的使用寿命内存活的可能性不到1%,并且不被认为是可靠的。贝叶斯方法表明,电动机可以在20年以上的使用寿命内存活的可能性要高出99%。作者提出了偏爱贝叶斯分析的原因,并讨论了用于更精确估计的测试方案。他们评论说缺乏数据来进行退化分析,以进行发动机温度循环的函数分析,并为未来的导弹系统数据收集提出建议。

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