Utilizing Big Data for Development of Active Learning Dynamic Soft Sensor

Conventional static soft sensor is incapable of handling the dynamic of processes and thus the historical data of the dynamic process should be taken into account and the resultant soft sensor is able to extract dynamic information. For data-based soft sensor development, the raw sensor data collected from the process database can contain measurements of up to several years. When big data are available in the database and the whole data set are used by practitioners the computational load can be excessive. This work presents a latent variable model (LVM) based active learning strategy to select representative data to overcome the problem of variable correlations and large number of sample. The uncertainty information provided by Gaussian process (GP) model is used as a base for selection criterion. The LVM with auxiliary GP model is developed together with 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.