Online Time-Series Contrastive Learning for Soft Sensing of Biopharmaceutical Processes

Biopharmaceutical processes typically generate abundant high-frequency online sensor data but suffer from sparse and delayed offline quality measurements, creating a “data-rich but label-poor” dilemma that hinders effective process monitoring. Furthermore, these processes are subject to significant distribution shifts due to batch-to-batch variability and time-varying metabolic states. To address these challenges, this article proposes a novel online time-series contrastive learning framework for soft sensing, validated on an industrial erythromycin fermentation process. We introduce a mechanism-informed soft contrastive learning strategy that utilizes information derived from domain knowledge to construct instance-level and temporal similarity matrices, guiding the model to learn physically meaningful representations from unlabeled data. In addition, we develop a dual-timescale online learning architecture comprising a slow branch for robust representation learning and a fast branch for real-time adaptation. Experimental results on a large-scale industrial dataset demonstrate that the proposed framework significantly outperforms state-of-the-art time-series contrastive learning baselines, particularly in predicting complex rheological indicators like broth viscosity under temporal distribution shifts.