%0 Journal Article
%T 基于混合MPLS的多阶段过程质量预报方法<br>Quality prediction method based on hybrid MPLS for multiphases process
%A 叶晓丰
%A 王培良
%A 杨泽宇&lt
%A br&gt
%A YE Xiaofeng
%A WANG Peiliang
%A YANG Zeyu
%J 山东大学学报（工学版）
%D 2017
%R 10.6040/j.issn.1672-3961.0.2017.178
%X 摘要： 针对传统的多向偏最小二乘方法(multi-way partial least squares, MPLS)在质量预报中存在着模型预测精度低、局部预报能力不足等问题,提出一种多MPLS模型融合方法来提高预报表现。利用高斯混合模型(Gauss mixture model, GMM)对每批次过程和质量数据组成的高维空间进行阶段识别。针对多批次同一子阶段长度不等问题,采用动态时间规整(dynamic time warping, DTW)算法依据最长持续时间同步为等长轨迹,并在子阶段中按变量展开方式建立MPLS模型。根据Fisher判据分析(Fisher discriminate analysis, FDA)最小化子阶段数据集间相关性,利用核密度方法估计子阶段数据集去相关后的概率密度分布来在线监测阶段切换。利用贝叶斯原则融合各子阶段MPLS模型进行质量预报。将该方法应用到工业青霉素发酵过程中,表明了所提方法具有更好的监控性能和预报能力。<br>Abstract: When the traditional multi-way partial least squares(MPLS)method was carried out for quality prediction, it performed the problems of low prediction accuracy and lack of local capacity. Considering to multiphase characteristics in industrial batch process, a mixture MPLS model method was proposed for the multiphases quality prediction. The Gauss mixture model(GMM)was employed to model the high dimensional spatial distribution of the measurement and the quality variables,which was used to identity sub-phase data blocks in each batch. Due to the problem of inequality length in identical sub-phase, the dynamic time warping(DTW)algorithm corresponding to the maximum path length of time was carried out to synchronize each data block in same sub-phase, whats more, the sub-phase MPLS model was set up according to the variable expansion method. The Fisher discriminate analysis(FDA)was introduced to minimize the relationship among sub-phase blocks, and then the kernel density method was used to monitor phases switch online by estimating the probability density distribution of less relationship sub-phase blocks. The multiphases quality prediction was established by mixing several sub-phase MPLS models according to Bays principle. Furthermore, the result of quality prediction for penicillin fermentation process showed the effectiveness of proposed method
%K 多向偏最小二乘方法
%K 费舍尔判据分析
%K 多阶段模型融合
%K 多阶段特性
%K 贝叶斯原则
%K &lt
%K br&gt
%K multi phase characteristics
%K bays principle
%K multi-phase model mixture
%K multi-way partial least squares
%K fisher discriminate analysis
%U http://gxbwk.njournal.sdu.edu.cn/CN/10.6040/j.issn.1672-3961.0.2017.178