Active learning dynamic soft sensor with forward-update scheme

Abstract

Soft sensors are used to infer the quality variable from easy-to-measure process variables. The conventional static soft sensor is incapable of handling the dynamic of processes. For data-based soft sensor development, with abundance of the raw sensor data, the problem of variable correlations and large number of sample are encountered. This work presents a latent variable model (LVM) based active learning strategy to select representative data for efficient development of the dynamic soft sensor model. In order to carry out data selection the uncertainty information is provided by Gaussian process (GP) model. The LVM with auxiliary GP model is developed under a dynamic framework which is suitable for dynamic processes. A forward-update scheme for updating the soft sensor model in advance is proposed so that the soft sensor is able to reflect the current status of the process and to improve the soft sensor model without waiting for the quality measurements. The proposed method is applied to an industrial fluid catalytic cracking process data.