In this paper, a dynamic modeling method for unevenly sampled data is proposed for the monitoring of bi-layer (i.e., a process layer and a quality layer) dynamic processes. First, a novel uneven data dynamic canonical correlation analysis method with an integrated dynamic time window is proposed for interlayer latent structure modeling, which captures the dynamic relations between regularly sampled process data and quality data with slow and irregular sampling. The new model is a step toward big data modeling to deal with data irregularity and diversity. Second, after extracting covariations using an interlayer model, intralayer variations are extracted using subsequent principal component analysis on the residual subspaces of the original process data and quality data, respectively. Third, a concurrent monitoring method for unevenly sampled bi-layer data is proposed. Finally, the proposed method is demonstrated using an illustrative simulation example and applied successfully to a real blast furnace iron-making process.
Bibliographical noteThis work was supported in part by the National Natural Science Foundation of China under Grant 61673097, Grant 61490704, Grant 61525302, Grant 61573022, and Grant 61304107, in part by the Fundamental Research Funds for the Central Universities under Grant N160804002 and Grant N160801001, in part by the International Postdoctoral Exchange Fellowship Program under Grant 20130020, in part by the Fundamental Disciplinary Research Program of the Shenzhen Committee on Science and Innovation under Grant 20160207, and in part by the Texas-Wisconsin-California Control Consortium. Paper no. TII-16-1018.
- Dynamic processes
- dynamic canonical correlation analysis (CCA)
- interlayer and intralayer concurrent monitoring
- unevenly sampled data modeling