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Research on Intelligent Teaching Based on Knowledge Graph of Marine Machinery Fault Diagnosis

Author(s)

Yan Cong*, Baojun Wang, Taili Du, Dazhi Zhang

Affiliation(s)

Marine Engineering College, Dalian Maritime University, Dalian, Liaoning, China *Corresponding Author.

Abstract

To enhance intelligence and efficiency of teaching in marine machinery fault diagnosis courses, a knowledge graph tailored for assisting classroom instruction has been developed and subsequently applied. This knowledge graph integrates multidimensional marine machinery expertise with extensive educational resources, emphasizing the systematic interconnectivity among various knowledge nodes. The results reveal a consensus among students, who agree that the application of this knowledge graph in teaching significantly improves learning efficiency, broadens their horizons, and satisfies their individual learning needs. The development and utilization of the marine machinery fault diagnosis knowledge graph strengthen students comprehensive grasp of the overall knowledge structure, optimize teaching methodologies and elevate teaching outcomes.

Keywords

Marine Machinery; Resource Management; Fault Analysis; Knowledge Graph

References

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