A New Method of Dynamic Latent-Variable Modeling for Process Monitoring
A New Method of Dynamic Latent-Variable Modeling for Process Monitoring
复制标题
过程监控动态潜变量建模的新方法
DOI：
10.1109/tie.2014.2301761
复制
发表时间：
2014-11-01
影响因子：
7.7
通讯作者：
Zhou, Donghua
中科院分区：
文献类型：
--
作者：
Li, Gang;Qin, S. Joe;Zhou, Donghua
动态主元分析（DPCA）被广泛应用于动态多变量过程的监控。在传统的DPCA中，过程变量之间的动态关系是隐含的，难以用变量来解释。为了提取动态相关的显式潜变量，本文提出了一种动态潜变量模型。新的结构可以改善动态过程的建模和解释，提高监控性能。提出了故障检测策略，并对模型进行了贡献分析。通过对田纳西-伊士曼流程的实例分析，验证了所提方法的有效性。
Dynamic principal component analysis (DPCA) is widely used in the monitoring of dynamic multivariate processes. In traditional DPCA where a time window is used, the dynamic relations among process variables are implicit and difficult to interpret in terms of variables. To extract explicit latent variables that are dynamically correlated, a dynamic latent-variable model is proposed in this paper. The new structure can improve the modeling and the interpretation of dynamic processes and enhance the performance of monitoring. Fault detection strategies are developed, and contribution analysis is available for the proposed model. The case study on the Tennessee Eastman Process is used to illustrate the effectiveness of the proposed methods.