OFF-LINE DIAGNOSIS OF ARIANE FLIGHTS USING PRINCIPAL COMPONENT ANALYSIS

Cesar Barta; Joaquim Melendez; Joan Colomer

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OFF-LINE DIAGNOSIS OF ARIANE FLIGHTS USING PRINCIPAL COMPONENT ANALYSIS

机译：基于主成分分析的芳烃飞行离线诊断

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After each Ariane mission, data flight analysis is intended to prevent not only the upcoming failures but also even the slightest drop of launcher performances. In this work, the benefits of using principal component analysis (PCA) to achieve this goal have been evaluated. PCA method has confirmed, in a simple example, its capacity for fault detection and location. It is shown that this tool allows a more obvious detection of singularities. It also supports the choice of the minimum set of sensors o calculated data to be evaluated in the systematic analysis.

机译：在执行每个Ariane任务之后，数据飞行分析不仅可以防止即将发生的故障，而且可以防止发射器性能下降一点。在这项工作中，已经评估了使用主成分分析（PCA）来实现此目标的好处。在一个简单的例子中，PCA方法已经确认了其故障检测和定位的能力。结果表明，该工具可以更明显地检测奇点。它还支持选择传感器的最小集合或要在系统分析中评估的计算数据。

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《Space technology》 |2007年第3期|p.99-105|共7页
作者
Cesar Barta; Joaquim Melendez; Joan Colomer;
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正文语种 eng
中图分类航天（宇宙航行）;
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OFF-LINE DIAGNOSIS OF ARIANE FLIGHTS USING PRINCIPAL COMPONENT ANALYSIS

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