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Determination of the principal components for the optimal detection performance of a combined index

机译:组合指数的最佳检测性能的主成分的确定

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In process monitoring, faults might be completely detected by T~2 or SPE or both of them. However, one index rather than two indices is preferred to monitor a process in practice. The number of principal components (PCs) in principal component analysis (PCA) impacts the fault detection performance obviously. In this paper, we focus on developing a selection criterion for the number of the PCs based on the best detection performance of the combined index and discussing the uncertainty of PCA model through cross validation. The simulation demonstrates that one index has a good detection performance and simplifies the detection system.
机译:在过程监控中,可以通过T〜2或SPE或它们两者完全检测故障。然而,一个索引而不是两个指数是优选在实践中监视过程。主成分分析(PCA)中的主要组件(PC)的数量显然会影响故障检测性能。在本文中,我们专注于根据组合指数的最佳检测性能来开发PC的数量的选择标准,并通过交叉验证讨论PCA模型的不确定性。该模拟表明,一个索引具有良好的检测性能并简化了检测系统。

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