基于知识图谱的柴油发动机故障诊断研究与系统设计

Diesel Engine Fault Diagnosis Research and System Design Based on Knowledge Graph

由于高速公路施工项目工期短、成本高等原因,高速公路施工现场的柴油发动机在发生故障时,需要得到及时的故障诊断和故障处理。通过BiLSTM-CRF模型实现故障实体抽取和关系抽取,利用结构化的语义网络来描述柴油发动机故障知识,以此构建柴油发动机故障领域知识图谱。同时,结合贝叶斯网络实现故障原因推理以对其知识图谱进行补全,还设计了基于知识图谱的柴油发动机故障诊断系统,以全面提升高速公路施工现场工程机械的维修效率。

Due to the short construction period and high cost of highway construction projects,diesel engine on the highway construction site needs to receive timely fault diagnosis and troubleshooting when it malfunctions.It uses the BiLSTMCRF model to extract fault entities and relationships,a structured semantic network is used to describe the knowledge of diesel engine faults,and a knowledge graph in the field of diesel engine faults is constructed.At the same time,Bayesian networks are combined to achieve fault cause inference and complete its knowledge graph.A diesel engine fault diagnosis system based on knowledge graph is also designed to comprehensively improve the maintenance efficiency of construction machinery on highway construction site.