Auto-Switch Gaussian Process Regression-based Probabilistic Soft Sensors for Industrial Multi-Grade Processes with Transitions

Prediction uncertainty has rarely been integrated into traditional soft sensors in industrial processes. In this work, a novel auto-switch probabilistic soft sensor modeling method is proposed for online quality prediction of a whole industrial multi-grade process with several steady-state grades and transitional modes. 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. 5 For the online prediction of a new sample, a prediction variance-based Bayesian 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 auto-switch 10 GPR soft sensors for a whole multi-grade 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.