Abstract
Assessing the potential risk of mission via analyzing the product life cycle data (APRPD) is a useful aerospace product quality assurance tool. The structure of aerospace product parameters has become increasingly complex and correlations exist among the multi-dimensional parameters. It is necessary to take the correlation into account when deriving an effective APRPD analysis. In this article, a density-based APRPD approach for multi-dimensional is proposed. The local outlier factor (LOF) of the observation is investigated and according to the LOF value the potential risk are assessed. This non-parametric APRPD approach can be used to analyze non-linear correlated data and does not require any data distribution assumption. The performance of the approach is demonstrated via a numerical case.
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