基于多阶段多向核熵成分分析的间歇过程故障检测方法

摘要

针对间歇过程的非线性、多阶段特性,提出一种基于多阶段多向核熵成分分析(multistage-MKECA,MsMKECA)的故障检测方法.针对间歇过程的多阶段特性,建立一种时序核熵主元关联度的矩阵相似性阶段划分方法,实现对间歇生产过程的多阶段划分;针对传统批次展开方式在线监控需要预估批次未来值的缺陷,进一步引入一种批次-变量三维数据展开方式建立每个阶段的MKECA非线性统计模型,实现对间歇过程的分阶段监控.最后对盘尼西林发酵过程开展仿真研究,结果表明所提方法能够比传统MKECA方法更为快速地进行故障检测.%A fault detection method,i.e., multistage multiway kernel entropy component analysis (MsMKECA) was proposed on the basis of nonlinearity and multistage characteristics of batch process.First, in order to divide a batch process into multiple stages, a matrix similarity stage division method was constructed from correlation matrixes of the time-series kernel entropy components. Then, a batch-variable 3-D unfolding technique was introduced to build MKECA model in each stage and to monitor operations in each stage of the batch process, which overcame on-line monitoring impediments of requiring estimation on future values by conventional batch-wise unfolding technique. Simulation study on penicillin fermentation process showed that the proposed method can offer much faster fault detection than traditional MKECA.