An aligned mixture probabilistic principal component analysis for fault detection of multimode chemical processes

A novel approach named aligned mixture probabilistic principal component analysis(AMPPCA) is proposed in this study for fault detection of multimode chemical processes. In order to exploit within-mode correlations,the AMPPCA algorithm first estimates a statistical description for each operating mode by applying mixture probabilistic principal component analysis(MPPCA). As a comparison, the combined MPPCA is employed where monitoring results are softly integrated according to posterior probabilities of the test sample in each local model. For exploiting the cross-mode correlations, which may be useful but are inadvertently neglected due to separately held monitoring approaches, a global monitoring model is constructed by aligning all local models together. In this way, both within-mode and cross-mode correlations are preserved in this integrated space. Finally, the utility and feasibility of AMPPCA are demonstrated through a non-isothermal continuous stirred tank reactor and the TE benchmark process.

可重构流程模型驱动和组件化的MES流程进化

为使制造执行系统流程能够始终与用户业务流程保持一致,提出一种基于可重构流程模型和组件技术的流程进化实施方法。对业务流程可变性进行分析,建立了可重构流程模型,并对其可重构性进行了说明。定义了组件模型,使流程模型能以组件的形式封装和部署。给出了流程模型分析算法与流程组件调整算法,通过流程模型在重构前后发生的变化驱动流程组件进行重构,完成对系统流程的进化。结合某航天企业的开发实施验证了该方法的可行性。