中文摘要：

针对间歇过程非线性的特点，将核方法引入到Fisher判别分析（Fisher Discriminaat Analysis，FDAl中，提出了基于多模型核多向Fisher判别分析（Multimodel Kernel Multiway FDA，MKMFDA）的间歇过程非线性监测与故障诊断方法。该方法仅利用已获得的数据测量值对过程进行监控，避免了传统多向主元分析（Multiway Principal Component Analysis，MPCA）方法对未来测量值的估计；且在线监控时通过比较核Fisher特征向量之间的欧氏距离来实现，而最优核Fisher判别向量用来鉴别故障类型。青霉素发酵过程应用表明，MKMFDA方法比传统的MPCA方法能更及时地监测出过程异常情况，更准确地判断异常发生的原因。

英文摘要：

Because of the non-linear characteristics of batch process, taking advantage of kernel theory, a novel batch performance non-linear monitoring and fault diagnosis method based on multi-model kernel multi-way fisher discriminant analysis （MKMFDA） was proposed. The approach only uses present data and overcomes pre-estimating the unknown part of process variable trajectory in multi-way principal component analysis （MPCA）. The key to the proposed approach is to calculate the distance of block data which are projected to the optimal kernel Fisher discriminant vector between new batch and reference batch. Through comparing distance with the predefined threshold, it can be considered whether the batch is normal or abnormal. Similar degree between the present discriminant vector and the optimal discriminant vector of fault in historical data set was used to perform fault diagnosis. Application results on a penicillin fermentation process demonstrate that, in comparison to the MPCA method, the proposed method is more accurate and efficient to detect and diagnose the malfunctions.