Dynamic Graph-Based Adaptive Learning for Online Industrial Soft Sensor With Mutable Spatial Coupling Relations

Abstract

Accurate online soft sensor in complex industrial processes remains challenging because underlying spatial coupling relations among process variables have not been effectively mined and exploited. Recently, some deep learning-based studies construct static graphs to explicitly represent underlying spatial coupling relations among process variables, but they neglect the fact that spatial coupling relations have mutable characteristics, leading to the poor performance in online soft sensor. Therefore, we propose a novel deep learning model to address this issue to achieve accurate soft sensor. Specifically, a dynamic graph is proposed to realize adaptive learning and automatic inference for mutable spatial coupling relations, so that the proposed model is endowed with the ability to real-timely sense spatial coupling relations in online industrial soft sensor. Then, based on the dynamic graph, a new multi-hop attention graph convolutional network is proposed to systematically aggregate various crucial node feature representations during graph convolution processes to capture fine-grained spatial dependence features, thereby achieving effective modeling for variation patterns of process variables. Finally, a new multivariate incremental training algorithm is designed for deep learning models to further improve the prediction performance. Verification study on a coal mill rig demonstrates the feasibility and effectiveness of the proposed model.