Comparative Study on Model-Based Named Entity Extraction Methods for Machine Tool Fault Cases
Most machine tool failures have the characteristics of scattered fault cases,no data sharing in different factories,and no standard database management.Aimed at existing similar failures,it is still necessary to shut down for maintenance in factories by unknown failures.Therefore,it is urgent for a standard service platform to collect a large number of fault cases,update maintenance and add new faults at the same time,and provide a fault reference for each factory and reduce maintenance and time costs as much as possible.This paper applies the popular knowledge graphs in the computer field to the fault diagnosis networks of machine tools,comprehensively uses the case knowledge of machine tool fault diagnosis,and constructs a machine tool fault diagnosis network with fault phenomena,reasons and solutions as a core to quickly identify the fault location,provide reasonable fault solutions,and improve the production efficiency of manufacturing industry.The crawler technology is used to obtain the fault case data,the BIO tagging method is used to complete the sample tagging,the Bilstrm-crf,Vgg16 and Bert models are used to complete the entity extraction task,respectively,and the accuracy of the above models are compared from multiple aspects,the knowledge is imported into the Neo4J diagram database,it builds the knowledge graph for the machine tool fault,and finally realizes the visualization of knowledge graph.
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