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Due to its incorporation of prior process knowledge and handling of uncertainty in a probabilistic sense, Bayesian network (BN)-based soft-sensors have shown significant advantages compared to conventional regression-based soft-sensors. In the literature, these soft-sensors are developed under the assumption that the process is operated around certain operating points. Due to the time-varying nature of the process, the prediction performance of the existing BN-based softsensors may deteriorate over time. To account for this, in the current work, adaptive Bayesian inference using a random walk model for both process and sensor drift is proposed. The efficacy of the proposed approaches is demonstrated through simulation and industrial case studies, and they are shown to perform better than the conventional bias updated approach.

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