Cases Integration System for Fault Diagnosis of CNC Machine Tools Based on Knowledge Graph

Abstract

 Due to the time-consuming and labor-consuming maintenance required by appealing to service engineers during the diagnosis of CNC machine tool faults, the CNC machine tool fault diagnosis system has become the economical practical choice of intelligent workshop now, which can help CNC machine tool operators quickly locate and eliminate faults. Knowledge graph technology plays a great role in the use of structured data, and has also achieved a certain degree of research progress and good applications in other professional fields. This article focuses on how to extract semi-structured and unstructured fault knowledge from the abundant CNC machine tool fault cases, fault repair manuals and on-site maintenance logs on the market. A complete system is used to integrate this fault diagnosis knowledge with different formats and content but interrelated, so as to realize the structured application of fault diagnosis knowledge of CNC machine tools. This paper first fully analyzes the content and structural characteristics of these fault cases, and optimizes the design of a set of domain ontology for machine tool fault diagnosis, which contains 8 types of ontoly and 7 types of relation. Using the rule-based knowledge extraction method combined with the BiLSTM-CRF information extraction algorithm to extract effective information from this knowledge. Finally, the structured knowledge is stored through the Neo4J graph database for visualization. Under the application of a good ontology, the accuracy and effectiveness of the composite extraction algorithm have been proved to be greatly improved compared with the single information extraction algorithm in the experimental verification process.