高校化学工程学报Issue(3)：650-656,7.DOI:10.3969/j.issn.1003-9015.2015.03.022
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
摘要
Abstract
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.关键词
间歇过程/多向核主元分析/多向核熵成分分析/阶段划分/故障监测Key words
batch process/multi-way kernel principal component analysis/multi-way kernel entropy component analysis/stage separation/fault monitoring分类
信息技术与安全科学引用本文复制引用
常鹏,王普,高学金..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[J].高校化学工程学报,2015,(3):650-656,7.基金项目
国家自然科学基金项目(61174109,61364009) （61174109,61364009）
高等学校博士学科点专项科研基金(20101103110009). （20101103110009）
