中文摘要：

针对间歇过程的多阶段特性,提出一种新的子阶段主元分析(principal component analysis,PCA)监测方法.首先,将间歇过程三维数据沿时间片展开,采用模糊模式识别方法计算相邻时间片负载矩阵变量方向重心的格贴近度,以最小贴近度为原则,根据格贴近度的变化,实现子阶段的划分;然后,在划分的子阶段内采用一种先沿批次后沿变量的改进展开方式建立PCA监控模型;最后,将该算法应用于青霉素发酵仿真系统的在线监测.结果表明该方法在监控过程中能够有效降低误报和漏报.

英文摘要：

An integrated framework consisting of sub-stage models and improved multi-way principal component analysis (MPCA) is developed to monitor the multistage batch process. After batch data time unfolding, lattice degrees of nearness (LDN) of correspondence statistics of adjacent time slices that are the center of variables of loading matrixes are calculated based on the theory of fuzzy pattern recognition. According to the criterion of minimum close-degree, sorted LDN is analyzed to realize operation sub-stage particularly. To overcome alarms' shortcomings of conventional MPCA, an improved MPCA method is adopted. Sub-stage PCA model is established by performing improved MPCA in each sub-stage. The case studies from a simulated fed-batch penicillin cultivation process indicate that it gives better monitoring results in terms of sensitivity and time to fault detection than MPCA and ATMPCA methods.