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
陈柯 1谭屈山 2王佳 2李伟 2江雨澳 2袁文丹 1吴浩1
作者信息
- 1. 四川成绵苍巴高速公路有限责任公司成都分公司,四川 成都 618206
- 2. 四川数字交通科技股份有限公司,四川 成都 610218
- 折叠
摘要
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.
Abstract
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 BiLSTM-CRF 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.
关键词
柴油发动机/故障领域/实体抽取/语义网络/贝叶斯网络Key words
diesel engine/fault field/entity extraction/semantic network/Bayesian network引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据