基于多向核熵成分分析的微生物发酵过程多阶段划分及故障监测

Multi-Stage Separation and Fault Monitoring of Microbial Fermentation Processes Based on Multi-Way Kernel Entropy Component Analysis

针对多向核主元分析法(MKPCA)在监控动态非线性和多模态间歇生产过程故障的不足,提出一种基于物理信息熵的多阶段多向核熵成分分析(multiple sub-stage multi-way kernel entropy component analysis,MSMKECA)的新方法用于故障监控。该方法首先通过核映射将数据从低维空间映射到高维特征空间;其次在高维特征空间依据熵结构信息计算每个时刻数据矩阵的相似度指标进行阶段划分,将间歇过程划分为各稳定阶段和各过渡阶段,并在过渡阶段用时变的协方差代替固定协方差;最后在划分的阶段里分别建立模型进行间歇过程监测解决间歇过程的动态非线性和多阶段特性;将所提出的算法应用于青霉素发酵仿真系统的在线监测,验证了该方法的有效性。

Since multi-way kernel principal component analysis （MKPCA） is usually inadequate in monitoring nonlinear and multimodal faults of batch production processes, a new method based on physical information entropy was proposed for fault monitoring （named multiple sub-stage multi-way kernel entropy component analysis （MSMKECA））. The data was first mapped from low-dimensional space to high-dimensional space via kernel mapping. Different steady and transitional stages of batch processes were then divided by calculating the similarity index of data matrices according to the structure information entropy in the high-dimensional feature space. Moreover, fixed covariance was replaced by time-varying covariance in transitional stages. Finally, models were built in different stages for batch process monitoring to resolve dynamic, non-linear and multi-stage characteristics of batch processes. The proposed algorithm was applied in a penicillin fermentation simulation system for on-line monitoring and the effectiveness of this method was verified. monitoring