针对间歇过程过渡状态下具有的复杂过程特性，提出一种基于二维动态主成分分析(2ddpca)的故障诊断方法。
A novel modeling method, two-dimensional dynamic principal component analysis (2ddpca), was developed for monitoring batch process transition in author's previous work.
河南省；粮食生产动态变化；主成分分析。
Henan Province; grain production; dynamic variety; Principal Component Analysis.
对多维动态数据系统给出了全局主成分分析(GPCA)模型，并对时序立体数据表进行立体式的综合与简化。
Global principal components analysis (GPCA) model is presented to the multidimensional dynamic data system and the time sequence solid data table is three-dimensionally synthesized and simplified.
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