Residual-aware deep attention graph convolutional network via unveiling data latent interactions for product quality prediction in industrial processes

Abstract

Data-driven modeling based on deep learning is crucial for online product quality prediction in industrial processes. Extracting latent data interactions from sensor variables lies at the core of various data-driven modeling applications. Typically, the observed variables exhibit non-stationary properties due to changes in operating conditions and sensor tuning issues. These fluctuations inevitably affect the reliability of traditional feature extraction methods, thus hindering their applications. Hence, this paper proposes a novel method named residual-aware deep attention graph convolutional network (RaDA-GCN) to explore potential interactions among sensor variables. RaDA-GCN ingeniously incorporates an attention mechanism into the graph convolution layer to extract nonlinear variable-related features according to their importance. Then, a novel residual-aware connection module is designed to reduce data uncertainty and alleviate over-smoothing. By skillfully stacking multiple attention map convolutional layers with the integration of residual-aware connections, deep structural features are obtained to facilitate the effective quantification and revelation of potential relationships among data variables. Finally, the application of the predictive modeling framework based on the proposed method is used to validate its effectiveness in actual industrial process data. The experimental results demonstrate that the proposed RaDA-GCN method exhibits a 23% improvement in the R-squared (R2) indicator and a 13% reduction in the root mean square error (RMSE) compared to the traditional graph convolutional network method.