On unifying multiblock analysis with application to decentralized process monitoring
On unifying multiblock analysis with application to decentralized process monitoring
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DOI：
10.1002/cem.667
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发表时间：
2001-10
影响因子：
2.4
通讯作者：
S. Qin;Sergio Valle;M. Piovoso
中科院分区：
文献类型：
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作者：
S. Qin;Sergio Valle;M. Piovoso
Westerhuis等人（J. Chemometrics 1998; 12：301-321）表明，可以分别从常规PCA和PLS分数直接计算出共识PCA和多块PLS的分数（Westerhuis和Coenegracht，J. Chemometrics 1997; 11：379-392）。在本文中，我们表明，无论是负载和得分的共识PCA可以直接从那些经常PCA计算，和多块PLS负载，权重和得分可以直接从那些经常PLS计算。四个多块PCA和PLS算法的正交特性进行了探讨。使用多块PCA和PLS的分散监测和诊断来自定期PCA和PLS得分和残差。虽然多块分析算法基本上等同于常规PCA和PLS，但基于过程知识的大型工厂过程变量块有助于以分散的方式定位故障的根本原因。新的定义块和变量的贡献SPE和T2提出了分散监测。这种基于适当变量分块的分散监控方法已成功地应用于聚酯薄膜生产过程。版权所有© 2001约翰威利父子有限公司。
Westerhuis et al. (J. Chemometrics 1998; 12: 301–321) show that the scores of consensus PCA and multiblock PLS (Westerhuis and Coenegracht, J. Chemometrics 1997; 11: 379–392) can be calculated directly from the regular PCA and PLS scores respectively. In this paper we show that both the loadings and scores of consensus PCA can be calculated directly from those of regular PCA, and the multiblock PLS loadings, weights and scores can be calculated directly from those of regular PLS. The orthogonal properties of four multiblock PCA and PLS algorithms are explored. The use of multiblock PCA and PLS for decentralized monitoring and diagnosis is derived in terms of regular PCA and PLS scores and residuals. While the multiblock analysis algorithms are basically equivalent to regular PCA and PLS, blocking of process variables in a large‐scale plant based on process knowledge helps to localize the root cause of the fault in a decentralized manner. New definitions of block and variable contributions to SPE and T 2 are proposed for decentralized monitoring. This decentralized monitoring method based on proper variable blocking is successfully applied to an industrial polyester film process. Copyright © 2001 John Wiley & Sons, Ltd.