基于MKECA的非高斯性和非线性共存的间歇过程监测

多向核独立成分分析（multiway kernel independent component analysis，MKICA）在监测间歇过程非高斯性和非线性方面取得了广泛应用，其仅仅是将线性独立成分分析（independent component analysis，ICA）方法利用核主成分分析（kernel principal component analysis，KPCA）白化扩展到非线性领域，但数据经KPCA白化后只考虑数据信息最大化未考虑数据簇结构信息的不足，为解决此问题，采用核熵成分分析（kernel entropy component analysis，KECA）代替KPCA白化的过程监测方法。该方法首先利用AT展开方法将过程三维数据变为二维数据；其次用KECA进行白化处理的同时解决数据的非线性；然后建立ICA监测模型用于非高斯生产过程监测；最后将该方法应用到青霉素发酵仿真和实际的工业过程并与MKICA方法进行对比，验证该方法的有效性。

Monitoring non-Gaussian and non-linear batch process based on multi-way kernel entropy component analysis

Multi-kernel independent component analysis (MKICA) has been widely used in monitoring non-Gaussian and non-linear processes. The technique uses only non-linear extension of linear independent component analysis (ICA) by KPCA data whitening. After KPCA data whitening, the data is considered only to maximize data information but not data cluster structure information. In order to solve this problem, kernel entropy component analysis (kernel entropy component analysis, KECA) was proposed to replace KPCA whitening in process monitoring. First, 3D data is transformed into 2D data by AT expansion. Second, data nonlinearity was resolved during KECA whitening. Third, ICA monitoring model was established for non-Gaussian production process monitoring. The method was applied to simulation and actual industrial process of Penicillin fermentation, which showed effectiveness of the method in comparison with the MKICA method.