改进的MPCA及其在批过程实时故障监测中的应用

针对多向主元分析（MPCA）模型批过程在线监测的缺陷，提出了一种基于变量展开和主元协方差随时间变化的MPCA方法，该方法按变量展开，不需要对新批次未反应完的数据进行预估，而数据之间的动态联系通过时变主元协方差得以保存，并且不需要建模批次的长度相等；将该力法应用于青霉素补料分批发酵过程的实时监测中．结果表明该方法比传统的MPCA方法具有更可靠的监测性能。

On-line Fault Monitoring of Batch Process Based on Improved MPCA

When multiway principal component analysis(MPCA) is used for on-line batch monitoring,the future behavior of each new batch must be inferred up to the end of the batch operation at each time and the batch lengths must be equalized.In this paper,a new statistical batch monitoring approach based on variable-wise unfolding and time-varying score covariance structures is proposed.The proposed method does not require prediction of the future values while the dynamic relations of data are preserved by using time-varying score covariance structures,and provides a simple method for handling batches with different durations that avoids the need to equalize the batch lengths.The proposed method was applied to 0n-line monitoring fed-batch penicillin production,and the results show the advantages of the proposed method in comparison to conventional MPCA.