Dynamic statistical process monitoring based on generalized canonical variate analysis

Abstract

A novel generalized canonical variate analysis (GCVA) algorithm is formulated and then applied for data-driven dynamic process monitoring. The proposed GCVA algorithm seeks for different projecting bases for the time-serial samples, so that the sum of squared canonical correlation coefficients between all pairs of the projected latent variables could be maximized. The corresponding dynamic process monitoring scheme first utilizes GCVA to explicitly extract dynamic and static latent variables from the time-serial data, simultaneously. Second, a multivariate regression model is employed for describing the time-serial relationship between the dynamic latent variables, the model residual then services as a good indicator for the inconsistency in the defined time-serial mechanism. For online monitoring purposes, two combined monitoring indices are proposed for detecting abnormalities in the time-dependent and time-independent variations, respectively. Additionally, reconstruction-based contribution indices are also derived for fault diagnosis accordingly. Finally, the capability of the GCVA algorithm in exploiting the time-serial correlation inherited in the given data is demonstrated, the effectiveness and superiority of the proposed GCVA-based approach over other counterparts are validated as well, through comparisons on two dynamic industrial processes.