| Citation: | JI X，WU T X，YANG Z W，et al. Power equipment defect prediction based on temporary knowledge graph[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（10）：3131-3138 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0801
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Power system management now includes monitoring power equipment operation, which is crucial given the growing size of power facilities. Defect prediction of power equipment is a key step in power system operation monitoring. In order to solve the problem of defect prediction for power equipment in large-scale power systems, we propose a defect prediction model for power equipment based on a temporary knowledge graph. The attention mechanism fuses the multimodal data, and the relationship-aware graph neural network and recurrent neural network are then employed to extract the temporal representation of entities and relations. Finally, we perform defect prediction of power equipment based on the temporal representation. The method proposed in this paper can make full use of multimodal information to improve the accuracy of power equipment defect prediction. Experimental results show that the model has considerable performance improvement compared to the baseline model.
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