A dynamic graph structure optimization diagnosis

In the field of industrial equipment management and academic network analy-sis, early fault diagnosis and node classification tasks are of great significance for ensuring the stable operation of equipment and promoting knowledge dis-covery. Existing methods face many challenges in dealing with large-scale and unbalanced data sets, especially in bearing fault diagnosis and scientific litera-ture classification. In response to these challenges, this paper proposes a Dy-namic Graph-Structured Optimization Diagnosis model based on graph neural network. The innovation of the model primarily encompasses two aspects. Firstly, concerning the dataset, the k-nearest neighbor algorithm is utilized to fuse the health status of bearings with vibration signal data. This integration facilitates the construction of a graph structure that accurately captures the complex relationship between different bearing states. At the same time, an optimization strategy combining Focal Loss and graph Deep Open Classifica-tion method is used to further improve the applicability and accuracy in differ-ent fields on the basis of enhancing the performance of the model in dealing with unbalanced data. During the experiment, the DG-SOD model showed ex-cellent performance in the above tasks. The accuracy of bearing fault diagnosis increased to 65 , the accuracy of Core node classification increased from 76 to 86.65 , and the classification accuracy of CiteSeer increased from 70 to 76.05 . The above data show that the DG-SOD model has obvious advantages in dealing with data imbalance problems in industrial equipment detection and scientific literature classification and improving the accuracy of minority class recognition. It provides new ideas and frameworks for future in-dustrial equipment management and academic network analysis.