Gaussian mixture continuously adaptive regression for multimode processes soft sensing under time-varying virtual drift

Abstract

Due to time-varying virtual drift in multimode processes, the performance of soft sensors will degrade after online deployment. Traditional adaptive mechanisms have been developed to address this issue, but all have limitations in the real-world scenario. Therefore, this paper explores domain adaptation as a new adaptive mechanism for its unsupervised knowledge calibration effect, and a Gaussian mixture continuously adaptive regression (GMCAR) is proposed for soft sensor modeling. First, we extend Gaussian domain adaptation to Gaussian mixture domain adaptation, in which the target domain could transfer knowledge from multiple Gaussian source domains with different posterior probabilities. Furthermore, a new sensitivity-based continuous domain adaptation is presented to efficiently and naturally update the Gaussian mixture domain adaptation by exploring existing knowledge and using the continuity of data streams. As well, an adaptive Gaussian mixture regression is designed to continuously update the soft sensing model according to the current process state. Finally, experimental results on the TE benchmark process and a real air separation process demonstrate that the proposed GMCAR-based soft sensor achieves state-of-the-art performance under time-varying virtual drift.