Graph Semi-supervised Soft Sensor Modeling Based On Label Propagation Algorithm

Abstract Data-driven soft sensor modeling methods have become prevalent in the industry. Nonetheless, the complexity of industrial processes often leads to the absence or difficulty in obtaining key labeled data, and existing methods frequently fail to fully utilize the inherent correlations between variables. This paper proposes a novel graph semi-supervised soft sensor modeling method using the label propagation algorithm to address these issues. This method utilizes correlations within the data to assign pseudo-labels to unlabeled data reasonably and employs graph convolutional networks to capture spatial relationships between nodes. Additionally, by embedding a long short-term memory structure, the model can capture temporal dependencies of the data while focusing on spatial structures. Furthermore, the introduction of a residual structure enables the model to directly learn the differences between inputs and outputs, facilitating information transmission, and improving the model’s feature extraction ability. Experiments demonstrate the effectiveness of the method.