Adaptive variational empirical mode decomposition aware intelligent data-driven modeling for complex industrial processes

Abstract

Due to the strong noise, high dimensionality and time-varying characteristics of industrial process data, data-driven modeling faces challenges in feature extraction and model interpretability. To address these issues, this paper proposes a new prediction model based on adaptive variational empirical mode decomposition-guided (AVEMDG) graph convolutional networks (GCNs). First, each sensor signal is decomposed into high-frequency and low-frequency features using empirical mode decomposition (EMD) to effectively capture multi-band information. Second, the weights of these features are adaptively updated through variational inference (Ⅵ) combined with Bayesian reasoning to handle the importance and uncertainty of features. Next, the GCN is used to model the spatiotemporal dependencies in the sensor network and is trained using the reweighted feature data. Last, the proposed method is applied to the prediction of the melt viscosity index (MVI), a key performance indicator (KPI) of the actual polyester fiber polymerization process. Ablation study and comparative experiment are conducted to evaluate the contribution of each component and the generality of the proposed model. Experimental results show that this method can effectively improve the model prediction accuracy, thereby enhancing the interpretability of the soft sensor model and providing guidance for the production of industrial processes.

Keywords

Data-driven modeling / soft sensor / industrial process / feature extraction / deep learning