基于核熵投影技术的多阶段间歇过程监测研究  

Research on batch process monitoring based on multi-stage kernel pattern entropy projection technology

作　　者：常鹏[1] 王普[1] 高学金[1] 齐咏生[2] 

机构地区：[1]北京工业大学电子信息与控制工程学院,北京100124 [2]内蒙古工业大学电力学院,呼和浩特010051

出　　处：《仪器仪表学报》2014年第7期1654-1661,共8页Chinese Journal of Scientific Instrument

基　　金：国家自然科学基金(61174109;61364009);高等学校博士学科点专项基金(20101103110009)资助项目

摘　　要：针对间歇生产过程中非线性和多阶段的特性,提出一种基于改进展开的子阶段多向核熵成分分析(ISMKECA)的新方法。该方法首先将三维历史数据按照所提数据展开策略进行数据预处理,解决数据填充引入模型误差的问题;其次通过核映射将数据从低维空间映射到高维特征空间,解决数据的非线性特性;然后在高维特征空间依据核熵和角结构信息对数据进行阶段划分,并在划分的阶段里分别建立ECA监控模型,解决数据的多模态问题;最后将提出的算法应用于工业青霉素发酵的在线监测,验证了该方法的有效性。Aiming at the nonlinear and multi-stage features in batch production process, a new method of improved sub-stage multi-way kernel entropy component analysis （ISMKECA） based on improved multi-stage expansion is proposed. In this method, three-dimensional historical data is preprocessed using the proposed data expansion strategy, which solves the model error problem caused by data filling; and then through kernel mapping, the data is mapped from low-dimensional space into high dimensional feature space, which solves the nonlinear characteristic of the data. In high dimensional feature space, the stage division of the data is carried out according to kernel entropy and angle structure information, arid the ECA monitoring models are established in the divided stages, which solves the multiple modal problem of the data. Finally, the proposed algorithm was applied to the online monitoring of industrial penicillin fermentation simulation system, which verifies the effectiveness of the proposed method.

关 键 词：间歇过程 MKPCA ISMKECA 过程监控 

分 类 号：TP206[自动化与计算机技术—检测技术与自动化装置]