A sparse loading-based contribution method for multivariate control performance diagnosis. (January 2020) - Wang, Kai; Chen, Junghui; Song, Zhihuan
Highlights: The data-driven design for control performance diagnosis is considered. The sparse method of the generalized eigen-decomposition issue is proposed. Sparse loadings are used to diagnose the potential root causes. Case studies on the simulated and industrial examples are illustrated. Abstract: An automatic and accurate control performance diagnosis algorithm is necessary for general chemical processes with a number of control loops when a performance change occurs. The popular contribution plots for control performance diagnosis have severe smearing effects of diagnosis and the problems are still not solved. A sparse loading based contribution plot is proposed in this paper to enhance the diagnosis performance. A complete iterative optimization procedure related to how to obtain the sparse loadings is developed. A numerical example and a period of practical industrial data are used to demonstrate the efficiency of the proposed method in comparison with the conventional contribution plots.