Abstract
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.
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