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中文摘要：
针对间歇过程复杂非线性的特点,提出一种基于角结构统计量的多向核熵成分分析（MKECA）间歇过程监测方法。该方法首先将间歇过程数据进行标准化预处理,然后采用KECA提取间歇过程数据的主成分矩阵。研究表明,经过KECA投影后的主成分数据具有良好的角结构,因此利用主成分矩阵构造基于角结构的统计量,并且采用核密度估计算法计算其控制限。与传统的统计量相比,无需假设过程变量服从高斯分布。最后通过青霉素发酵的仿真平台和大肠杆菌实际生产过程验证,实验结果表明,相比于传统MKPCA方法,能够有效利用主成分的结构信息,明显降低了故障的误报率、漏报率。
英文摘要：
Aiming at monitoring the batch process with complex nonlinear characteristic,a multi-way kernel entropy component analysis（ MKECA） method based on the angle structure statistic is proposed. In this method,the process data is firstly preprocessed,and then the principal component matrices of the batch process data are extracted by KECA. Research shows that KECA reveals angular structure relating to the Renyi entropy of the input space data set,and angular structure statistic is constructed using the principal component matrix structure. And then the control limits are calculated by the kernel density estimation algorithm. Finally,through the simulation of the penicillin fermentation and the actual production process of recombinant,the experiment results show that the proposed method effectively uses the structural information of the principal components compared to the traditional method of process monitoring. So error rate and false alarming rate are significantly lowered.
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