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

机译:确定主要指标以实现组合指标的最佳检测性能

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In process monitoring, faults might be completely detected by T2 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,故障可能完全由T 2 或SPE或两者同时检测到。但是,在实践中,最好使用一个索引而不是两个索引来监视过程。主成分分析(PCA)中的主成分(PC)数量明显影响故障检测性能。在本文中,我们专注于根据组合索引的最佳检测性能为PC数量选择标准,并通过交叉验证讨论PCA模型的不确定性。仿真表明,一种指标具有良好的检测性能,简化了检测系统。

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