Data-Driven Process Monitoring Using Structured Joint Sparse Canonical Correlation Analysis
Xianchao Xiu, Ying Yang, Lingchen Kong, Wanquan Liu
Abstract
In order to improve the performance of canonical correlation analysis (CCA) based methods for process monitoring, this brief proposes a novel process monitoring approach using the structured joint sparse canonical correlation analysis (SJSCCA). Technically, the graph Laplacian could incorporate structured variable correlation information and the joint sparsity could discard useless variables. The developed two-stage alternating direction method of multipliers is shown to be very efficient because each derived subproblem has a closed-form solution or can be solved by fast solvers. In order to detect abnormal situations, $T^{2}$ test statistic is adopted. The validity of SJSCCA is illustrated by the benchmark Tennessee Eastman process. The achieved results show that the proposed SJSCCA is able to improve the monitoring performance significantly in comparison with the existing state-of-the-art CCA-based methods.