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首页> 外文会议>Reliability and Maintainability Symposium (RAMS), 2012 Proceedings - Annual >Planning, tracking, and projecting reliability growth a Bayesian approach
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Planning, tracking, and projecting reliability growth a Bayesian approach

机译：贝叶斯方法规划，跟踪和预测可靠性增长

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摘要

Liquid rocket engine reliability growth modeling is a blend of art and science because of data scarcity and heterogeneity, which result from the limited number of engine development programs as well as testing profiles that are much different from the actual mission profile. In particular, hot fire tests are shorter than full mission duration due to test facility limitations and some of them are performed at extreme load points to demonstrate robustness and design margin. The well-known empirical Duane and analytical Crow/AMSAA models are therefore no longer best practice because the reliability growth rate is calculated using a MTBF estimate that is simply the total accumulated test time divided by the total number of failures. Therefore, we propose a new, fully Bayesian estimation based methodology that estimates the system reliability while taking into account the test profile characteristics and aggregating component, subsystem, and system level hot fire test data. The methodology is applied to planning, tracking, and projecting reliability growth and illustrated using an example. In the example, a system reliability target must be demonstrated in a TAAF program. The system reliability target defines the scope of the hot fire test plan for the reliability growth planning using pseudo numbers for the planned hot fire tests. At each occurrence of a failure, the methodology is used in the context of reliability growth tracking, i.e. the attained system level reliability is estimated. The test plan is updated to reflect the need for additional tests to meet the system reliability target. Reliability growth projection is easily performed using either specific projection models or the prior distribution that features a knowledge factor to model the specified level of fix effectiveness.
机译：液体火箭发动机可靠性增长建模由于数据稀缺和异质性而将艺术与科学融合在一起，这是由于发动机开发程序数量有限以及测试配置文件与实际任务配置文件存在差异而导致的。特别是，由于测试设备的限制，热火测试比完整的任务持续时间短，并且其中一些是在极端负载点执行的，以证明其坚固性和设计余量。因此，众所周知的经验杜安模型和Crow / AMSAA分析模型不再是最佳实践，因为可靠性增长率是使用MTBF估算值来计算的，该估算值只是总的累积测试时间除以故障总数。因此，我们提出了一种新的，完全基于贝叶斯估计的方法，该方法可在考虑测试配置文件特征以及聚合组件，子系统和系统级热火测试数据的情况下估计系统可靠性。该方法适用于计划，跟踪和预测可靠性增长，并通过示例进行说明。在该示例中，必须在TAAF程序中演示系统可靠性目标。系统可靠性目标使用拟进行的热火测试的伪数来定义用于可靠性增长计划的热火测试计划的范围。在每次出现故障时，都在可靠性增长跟踪的背景下使用该方法，即估算获得的系统级可靠性。测试计划已更新，以反映对满足系统可靠性目标的其他测试的需求。使用特定的投影模型或以知识因素为模型的固定有效性模型进行建模的先验分布，可以轻松地进行可靠性增长预测。

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