PCA-ICA化工过程监控中的PCA白化性能分析  

Analyze the Effect of PCA Whitening on Chemical Process Monitoring Based on PCA-ICA

作　　者：姜庆超[1] 颜学峰[1] 

机构地区：[1]华东理工大学化工过程先进控制和优化技术教育部重点实验室,上海200237

出　　处：《石油化工高等学校学报》2012年第1期71-75,80,共6页Journal of Petrochemical Universities

基　　金：国家自然科学基金(21176073);教育部博士点基金(20090074110005);上海市曙光计划(09SG29);新世纪优秀人才(NCET-09-0346)

摘　　要：主元分析是基于独立元分析过程监控中一种重要而且常用的白化方法,可以有效地降低监控对象的维数。其基于正常样本数据,根据主元方差贡献率选取主元,保留正常样本中的大部分方差信息,消除噪声。在PCA模型中,每个主元的T2统计量表征着样本数据沿该主元方向的变异程度。通过对故障样本数据每个主元的T2统计量分析,发现某些故障信息投影在方差较小且被舍弃的主元上,从而造成故障信息的损失,进而影响了ICA的监控性能,造成故障的漏检和故障源的误识别。最后,采用一个简易系统和TE过程,验证了PCA白化过程对ICA监控性能的影响。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.

关 键 词：主元分析 数据白化 独立元分析 过程监控 

分 类 号：TE967[石油与天然气工程—石油机械设备] TP277[自动化与计算机技术—检测技术与自动化装置]