Research On Knowledge Graph Construction Technology For Fault Analysis

With the rapid development of Chinese manufacturing industry,modern industry has the characteristics of continuous production,large equipment,complicated structure,operational automation and intelligence.Due to the characteristics of the manufacturing system structure,once some equipment components malfunction,it may cause catastrophic accidents of the entire system,and even cause huge economic losses.Fault diagnosis technology refers to the fault location of the system through various inspection methods to determine the cause and location of the system failure,thereby reducing or eliminating the occurrence of system failure,which is of great significance for improving system reliability and safety.At present,the main research work in the field of fault analysis and diagnosis mainly focuses on the current operating state of the system.The fault diagnosis software or systems for specific application fields are relatively simple,and there is a lack of general-purpose fault diagnosis software and methods.At the same time,the reuse rate of knowledge and experience resources for fault diagnosis is low.In summary,the construction of intelligent fault diagnosis methods with predictive functions and versatility is the main research focus and direction of scholars at home and abroad.The essence of knowledge graph is to express knowledge through visual methods and explore the relationship between knowledge,which can reveal the dynamic development law of knowledge field and realize knowledge sharing and reuse.Its idea can be traced back to the Semantic Network proposed in the 1950s and 1960s.As a form of knowledge expression,Ontology is applied to the process of knowledge graph construction to complete the integration and unification of the knowledge base in the knowledge graph resource layer.The main research contents of this paper include:First of all,in the research stage,this paper summarizes and analyzes the research and development of corresponding technologies at home and abroad through the scientific text mining visualization software CiteSpace and the document website visual measurement analysis tools.Learning the knowledge of specific fault areas,so that the relevant knowledge obtained is more accurate,and then a more complete domain ontology library is created;Then,using the ontology modeling method in the semantic environment,combined with the advantages of OWL and knowledge graph to store of ontology information.The ontology modeling tool protege is used to establish a general knowledge ontology model in the field of fault analysis.Among them,the fault domain ontology is the conceptual basis in the fault analysis knowledge graph,which provides the hierarchical structure of fault knowledge ontology,and realizes knowledge sharing and reuse.Through the combination of ontology reasoning mechanism and SWRL rule description,the inductive analysis of the established fault ontology relations is carried out;Finally,the powerful ontology graphical representation ability of the graph database Neo4j and SpringBoot programming are used to construct the fault ontology visualization knowledge graph which realizes the dynamic retrieval of fault information,improves the utilization and sharing degree of fault diagnosis knowledge,and provides a feasible for the knowledge graph construction in the field of fault prediction diagnosis.