分段多向核主元分析的啤酒发酵过程故障检测

基于主元分析的故障诊断模型应用在非线性时变过程中具有局限性。基于间歇过程具有周期性这一特点,在非线性空间的数据提取中,将核变换理论引入其中,提出了一种改进的多向核主元分析故障诊断模型,该方法对于过程数据的非线性问题的解决和非线性信息的充分提取表现出很好的性能,使得非线性主元能够在高维特征空间中被快速提取。 对比实验结果表明,该方法对于缓慢时变的间歇过程具有很好的准确性与实时性。

Fault Detection for Beer Fermentation Process Based on Segmentation Multiway Kernel Principal Component Analysis

The fault diagnosis model based on principal component analysis has limitation in nonlinear time varying process.Based on the characteristics of the batch process,we introduced the theory of kernel transformation into the data extraction of nonlinear space,and proposed an improved fault diagnosis model based on multiple kernel principal component analysis.This method shows good performance for the nonlinear problem of process data and the full extraction of nonlinear information,where the nonlinear principal element can be rapidly extracted in the high dimensional feature space.The method was tested by comparison.The results show that the method has good accuracy and real-time performance in the process of slow time varying batch process.