Knowledge graph completion method for industrial equipment fault diagnosis based on heterogeneous graph attention
To address the issue of a serious lack of fault entity attributes and fault relation links in an industrial equipment fault diagnosis knowledge graph,this paper develops an industrial equipment fault diagnosis knowledge graph completion scheme based on a knowledge graph heterogeneous graph attention network(KGHAN)model.By combining fault knowledge structure information and fault graph structure information in a heterogeneous graph attention network(HAN)model,the developed KGHAN model effectively represents the embedding representations of fault entities and fault relations,which enhances the accuracy of the fault entity concept completion task and the hit rate of the fault relation link completion task.We apply our developed KGHAN model-based industrial equipment fault diagnosis knowledge graph completion scheme to the industrial equipment fault operation and maintenance data of a local enterprise.The results show that the accuracy of the fault entity concept completion task and the hit rate of the fault relation link completion task increased by about 10％and 37％,respectively,which confirms the effectiveness of our method.
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