Distributed partial least squares based residual generation for statistical process monitoring. (March 2019) - Tong, Chudong; Lan, Ting; Yu, Haizhen; Peng, Xin
Highlights: A data-driven residual generation method is proposed for process monitoring. The generated residuals instead of original measurements are modeled and monitored. Case studies demonstrate the effectiveness of the proposed DPLS-RG method. Abstract: The main focus of the current work is to propose a purely data-based residual generation method for statistical process monitoring. The proposed approach utilizes but not limit to the partial least squares (PLS) algorithm to construct a specific regression model for each variable in a distributed manner, the model residual ( i.e., estimation error) instead of the original data and the PLS latent components is then monitored. Given that every variable is transferred into the residual through its corresponding soft sensing model, the generated residual can reflect the variation in the defined input-output relationship. Furthermore, the residual is expected to follow a Gaussian distribution or at least much closer to a Gaussian distribution in contrast to the original data and the latent components, once the output variable is well predicted by the regression model. The main contributions of the presented work are as follows: 1) distributed soft sensing models for generating residuals, 2) statistical process monitoring for the generated residuals instead of original data, and 3) the comparison studies demonstrate the validity and superiority of the proposed monitoring scheme with the utilization of the PLS algorithm. It can be concluded from the comparisons and the illustrated superiority that the proposed approach would be an efficient and comparative alternative in process monitoring.