Research on Fault Diagnosis and Predictive Maintenance Algorithm of Complex Industrial System Based on Graph Neural Network

Traditional maintenance mode can't meet the demand, but graph neural network (GNN) has advantages in processing graph structure data, which provides the possibility for solving related problems. This paper proposes a framework based on dynamic GNN to realize fault diagnosis and predictive maintenance of complex industrial systems. Its main contributions are as follows: firstly, the self-adaptive graph topology is constructed by combining the physical connection of devices and the correlation analysis driven by data to solve the contradiction between static graph assumption and system dynamics; The second is to design an improved TGAT (Temporary Graph Attention Network) module that integrates time sequence characteristics and topological relations to enhance the ability to identify equipment coupling faults; Thirdly, a multi-task learning framework is established, and the fault classification and residual life prediction (RLP) are jointly optimized, and the dynamic weight allocation mechanism is adopted to improve the diagnosis accuracy in small sample fault scenarios. In terms of research methods, firstly, the graph structure that changes with time is defined, and the construction of dynamic edge set is emphasized. The weights of edges are combined with physical connection strength and dynamic correlation coefficient, and learning fusion weights are introduced. Secondly, an improved TGAT module is designed, which uses Gated Recurrent Unit (GRU) coding time sequence characteristics, introduces dynamic attention mechanism to update node representation, and adopts robustness enhancement strategy. Finally, the multi-task learning framework is constructed, and the dynamic weighted loss function and the dynamic weighting mechanism of fault sample proportion perception are designed. Experiments were carried out on TE data set and compressor set data set of chemical plant. The results show that the proposed method is superior to many baseline models in fault classification and RLP. The ablation experiments verify the effectiveness of each module, and the model has good robustness to noise, so it is feasible and reliable to make accurate life prediction in actual industrial scenes.