LAUSR

Abstract Data based approaches have recently gained extensive attention in modern process industries. Accordingly, data based soft sensing technology used for making online predictions of quality variables plays currently an important role in process control and monitoring. Drifts in operating conditions and process characteristics, however, demand novel online learning methods to be developed for maintaining predictive accuracy of soft sensors. While moving window (MW) and just-in-time-learning (JITL) are the most frequently used online learning methods in soft sensor design, they are, indeed, effective against different types of drifts. In the current study, in order to exploit the merits of both methods within a perspective offered by the recent transfer learning paradigm, we propose combining a task transferred JITL model with a MW learner in a transductive learning setting (JITL TT -MW tr ). The proposed method, tuned via a historical training set, is easy to implement and robust due to cooperative/complementary actions of JITL and MW methods. High prediction accuracy of JITL TT -MW tr is demonstrated on one semi-synthetic and five publicly available real datasets, indicating its efficiency and potential for industrial implementation.