Auto-Switch Gaussian Process Regression-Based Probabilistic Soft Sensors for Industrial Multigrade Processes with Transitions

Prediction uncertainty has rarely been integrated into traditional soft sensors in industrial processes. In this work, a novel autoswitch probabilistic soft sensor modeling method is proposed for online quality prediction of a whole industrial multigrade process with several steady-state grades and transitional modes. It is different from traditional deterministic soft sensors. Several single Gaussian process regression (GPR) models are first constructed for each steady-state grade. A new index is proposed to evaluate each GPR-based steady-state grade model. For the online prediction of a new sample, a prediction variance-based Bayesian inference method is proposed to explore the reliability of existing GPR-based steady-state models. The prediction can be achieved using the related steady-state GPR model if its reliability using this model is large enough. Otherwise, the query sample can be treated as in transitional modes and a local GPR model in a just-in-time manner is online built. Moreover, to improve the efficiency, detailed implementation steps of the autoswitch GPR soft sensors for a whole multigrade process are developed. The superiority of the proposed method is demonstrated and compared with other soft sensors in an industrial process in Taiwan, in terms of online quality prediction