Ensemble just-in-time model based on Gaussian process dynamical models for nonlinear and dynamic processes

Abstract

Process data have a number of characteristics, such as noise, nonlinearity, process dynamics, and autocorrelation. Ideally, adaptive soft sensors would be used to solve model degradation problems resulting from changes in process characteristics. However, it is necessary to optimize the hyperparameters for each model and, depending on the state of the process, the optimal hyperparameters will change. In this study, we focus on a Gaussian process dynamical model (GPDM), a dimension-reduction method that considers all of the data characteristics. We combine a just-in-time (JIT) model and ensemble learning, and then predict y-values with multiple JIT models that have different sets of hyperparameters. Each JIT model is constructed using latent variables obtained by the GPDM. The weights of the JIT models are determined based on Bayes’ theorem in consideration of their predictive ability. Analysis of two industrial datasets confirms that the proposed model is more accurate than existing approaches.