A New Method of Dynamic Latent-Variable Modeling for Process Monitoring

作者：Li Gang^*; Qin S Joe; Zhou Donghua

来源：IEEE Transactions on Industrial Electronics, 2014, 61(11): 6438-6445.

DOI：10.1109/TIE.2014.2301761

• Contribution plots
• dynamic latent-variable (DLV) model
• dynamic principal component analysis (DPCA)
• process monitoring and fault diagnosis
• subspace identification method

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.

• 出版日期2014-11
• 单位清华大学