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.
Bibliographical noteThis work was supported in part by the National Basic Research Program (973 Program) under Grant (2010CB731800), in part by the National Natural Science Foundation of China under Grant 61074084, Grant 61074085, Grant 61210012, Grant 61290324, Grant 61273173, Grant 61304107, and Grant 61020106003, in part by the Beijing Key Discipline Development Program under Grant XK100080537, and in part by the Beijing Natural Science Foundation under Grant 4122029.
- Contribution plots
- dynamic latent-variable (DLV) model
- dynamic principal component analysis (DPCA)
- process monitoring and fault diagnosis
- subspace identification method