Power equipment defect prediction based on time series knowledge graph

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With the increasing scale of power facilities, the operation monitoring of power equipment has become an important part of power system management. Defect prediction of power equipment is a key step in power system operation monitoring. In order to solve the problem of defect prediction of power equipment in large-scale power systems, this paper proposes a power equipment defect prediction model based on time series knowledge graph in combination with artificial intelligence technology. Multimodal information is fused through the attention mechanism, and then the time series representation of entities and relations is obtained by using the relationship-aware graph neural network and recurrent neural network. Finally, the defects of power equipment are predicted based on the time series representation. The method proposed in this paper can make full use of multimodal information and improve the accuracy of power equipment defect prediction. Experiments show that the performance of the model proposed in this paper is better than that of the baseline model.