计算机工程与应用2023，Vol.59Issue(24)：309-318,10.DOI:10.3778/j.issn.1002-8331.2208-0186
工业设备故障处置知识图谱构建与应用研究
Research on Construction and Application of Knowledge Graph for Industrial Equipment Fault Disposal
摘要
Abstract
The use of knowledge graph to assist industrial equipment fault disposal can effectively improve the fault dis-posal 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 enti-ties 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 experi-mental 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 applica-tion 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 recom-mends maintenance personnel and maintenance methods based on the knowledge graph.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Key words
equipment fault handling/knowledge graph/semi-automatic marking/entity extraction/intelligent recommendation分类
信息技术与安全科学引用本文复制引用
瞿智豪,胡建鹏,黄子麒,张庚..工业设备故障处置知识图谱构建与应用研究[J].计算机工程与应用,2023,59(24):309-318,10.基金项目
科技创新2030—"新一代人工智能"重大项目(2020AAA0109302). （2020AAA0109302）
