关键词: 主元分析 , 数据白化 , 独立元分析 , 过程监控

Abstract: Principal component analysis (PCA) was an important and most widely used data whitening approach in process monitoring based on independent component analysis (ICA) due to its effectivity in reducing dimensions of objects. PCA model was generated based on sample data of normal process, and the first several PCs which contain the most variance information of normal process were employed for ICA and process noise was eliminated. In PCA model, T2 statistic of each principal component has the property that it can measure variation along direction of the component. By researching the T2 statistic of sample data of fault process, it is found that information of some faults are mostly reflected on the components corresponding to smaller variance contribution, which are regarded as process noise and eliminated, and thus missed detection problem is happened. At last, the effect of PCA whitening on chemical process monitoring based on PCA-ICA is illustrated through simulations of both a simple process and TE process, and the results prove the proposed opinion.

Key words: Principal component analysis, Data whitening, Independent component analysis, Process monitoring