Self-Modified Dynamic Domain Adaptation for Industrial Soft Sensing

Data-driven soft sensors have been widely applied to estimating important yet difficult-to-measure quality-relevant variables in industrial processes. The complex industrial data exhibits nonlinearity and dynamics due to changes in operating conditions. Soft sensors developed based on the assumption of identical and independent distributions often struggle to adapt to target domain data with significant distribution discrepancy, which poses a great challenge to traditional soft sensor approaches. Additionally, the dynamic features embedded in the practical industrial processes are of great importance for an accurate soft sensor. Existing soft sensor approaches pay little attention to the combined challenge of distribution discrepancy and dynamic feature transfer, referred to as the dynamic domain adaptation challenge. In this work, we propose a Self-modified Dynamic Domain Adaptation (SDDA) soft sensor approach to solve this problem. We develop a novel sequential optimization framework for dynamic domain adaptation, where the target samples are progressively incorporated and pseudo-labels are iteratively refined to preserve the underlying temporal dependency and enable efficient dynamic feature transfer. Also, we propose a feature alignment with the transfer component analysis (TCA) to avoid potential significant distribution discrepancy and guarantee a stable prediction. We demonstrate the superiority of the proposed method via two real-world industrial cases.