Causal variable selection for industrial process quality prediction via attention-based GRU network

Abstract

While constructing predictive model for industrial process quality prediction, the selection of an appropriate input variable set is crucial to the online prediction performance. In this paper, the issue is solved through a deep causality analysis method, where the deep learning feature extracting unit, i.e., the Gated Recurrent Unit (GRU), is combined with the Granger Causality (GC) to extract the causal relationships between process variables. In this way, the nonlinear and dynamic features contained in the process data can be effectively learned in the GC analyzing framework, resulting in a more reliable causal relationship structure. Furthermore, through applying the attention mechanism to the input layer of the GRU cells, the causal relationship between process variables and the quality variable can be extracted, even the long time-lags exist. Based on the direct and indirect causal structure, the quality-related causal input variables are selected, and the predictive model is established through a deep dynamic regression network with GRU cells. A numerical example and a real chemical process case are provided to verify the effectiveness of the proposed causal variable selection method, where the prediction performance is significantly improved comparing with the state-of-the-art methods.