A dynamic graph convolutional network-based framework for the unsteady operating states recognition of multi-product pipeline systems

Abstract

Considering that the existing methods lack spatial and temporal information mining of pipeline multidimensional operation data, it is unable to accurately recognize the unsteady operation conditions among pipeline stations. In this study, a dynamic graph convolutional network classification model is proposed for the recognition of unsteady operating states in multi-product pipeline systems. Firstly, dynamic graph convolutional network of multi-pipeline system (DPipeNet) is constructed based on the visibility graph algorithm, mutual information and long and short-term memory network model. Secondly, static graph convolutional network of multi-pipeline system (SPipeNet) is constructed by using the real geographic location information of each station of multi-pipeline. Then, the input subgraph of the graph convolutional network is used to construct the multi-pipeline system operational state relationship network (OSRN), and the vulnerable state nodes of the system are evaluated using complex network centrality metrics. Finally, the proposed model is applied to real operational data of a multi-pipeline system in China. The results show that in the two-classification scenario, both DPipeNet and SPipeNet have higher accuracies, but DPipeNet has a lower missed rate. In the multi-classification scenario, DPipeNet has the highest precision, which can reach more than 85%, and the recall rate is improved by 13%–25% compared with the neural network models in recent literature and SPipeNet. In the vulnerability analysis scenario, the intermediate station pump startup/stoppage of multi-pipeline has higher vulnerability. The proposed method also provides decision support for managers in pipeline system operation and maintenance management.