关键词: 设备故障处置, 知识图谱, 半自动标注, 实体抽取, 智能推荐

Abstract: The use of knowledge graph to assist industrial equipment fault disposal can effectively improve the fault disposal efficiency. Addressing the problem that the annotation of entities in the field of industrial equipment fault mainly relies on human resources, which is time-consuming and labor-intensive, a semi-automatic annotation method of entities for equipment fault disposal based on external knowledge base is proposed, which achieves semi-automatic annotation of entities in the field using crawled equipment information and external knowledge such as sememe, saving nearly half of the manual annotation cost. Aiming at the problem that the entity types and entity labels are incorrectly identified by using the existing entity extraction methods. The method incorporates the lexical information and word boundary information of the word in the word embedding based on the BERT pre-trained word vector to obtain more semantic information than other word embedding methods, and combines BiLSTM and CRF to form the entity extraction model in this paper. The experimental results show that the recognition performance of the proposed model has been improved by 3.8 percentage points compared with BERT-BiLSTM-CRF. At the same time, better results can be obtained with fewer iterations. On the application of knowledge graph, a multi-modal information fusion method for equipment fault disposal solution recommendation is proposed, which uses deep learning models and sensor information to determine the occurrence of faults, and recommends maintenance personnel and maintenance methods based on the knowledge graph.

Key words: equipment fault handling, knowledge graph, semi-automatic marking, entity extraction, intelligent recommendation