An online soft sensor based on adaptive double Gaussian Bayesian network

Real-time monitoring of key performance indicators (KPI) through online soft sensors plays a crucial role in modern industrial processes to improve product quality and ensure production safety. In this paper, an Adaptive Double Gaussian Bayesian Network (ADGBN) is proposed to efficiently perform online soft sensor tasks, which can be updated according to the actual working conditions. The ADGBN is based on the concept of model bias correction and consists of an offline prediction model and a calibration model. The calibration model enables incremental learning-based parameter updates using newly generated online data. To improve the operational efficiency of the online soft sensor model, a novel dynamic variable window (DVW) is proposed to monitor model performance and enable adaptive updates. A case study on the polyester fiber polymerization process verifies the effectiveness of the proposed ADGBN online soft sensor and demonstrates its superiority over existing methods. •An Adaptive Double Gaussian Bayesian Network is proposed for online soft sensing.•Real-time model performance monitoring is realized by a new dynamic variable window.•Model parameters are updated through incremental learning.•Application to a real industrial process shows its effectiveness and practicality.