发动机故障领域知识图谱构建与应用  

Construction and Application of Knowledge Graph in Diesel Engine Fault Field

作　　者：许驹雄 李敏波[1,2] 刘孟珂 曹志月 唐波 葛浩 XU Ju-Xiong;LI Min-Bo;LIU Meng-Ke;CAO Zhi-Yue;TANG Bo;GE Hao(Software School,Fudan University,Shanghai 200438,China;Shanghai Key Laboratory of Data Science,Fudan University,Shanghai 200438,China;Weichai Power Co.Ltd.,Weifang 261061,China)

机构地区：[1]复旦大学软件学院,上海200438 [2]复旦大学上海市数据科学重点实验室,上海200438 [3]潍柴动力股份有限公司,潍坊261061

出　　处：《计算机系统应用》2022年第7期66-76,共11页Computer Systems & Applications

基　　金：国家重点研发计划(2018YFB1703104);国家自然科学基金(61671157)。

摘　　要：发动机生产故障和售后维修报告中有大量动力总成和零部件故障信息.本文将知识图谱引入柴油发动机故障领域,设计发动机故障领域知识图谱构建的系统流程,针对多源故障数据进行本体建模.使用BERT和BiLSTMCRF结合的实体识别框架,挖掘故障数据中的专家知识.提出实体相关性评价指标FF-IEF,并基于知识图谱和贝叶斯网络进行故障诊断.设计并开发EFKG原型系统,共包含12534个实体和408972条三元组,该系统提供知识抽取、可视化检索、辅助决策等功能,有效提高信息检索和维修效率,对知识图谱在发动机故障领域的应用具有一定指导意义.There is a large amount of failure information from the engine after-sales maintenance and failure reports.This study introduces knowledge graphs and designs a systematic building procedure for the field of engine fault.It carries out ontology modeling for the multi-source fault data.The entity recognition framework that combines BERT with BiLSTMCRF is used to mine expert knowledge in fault data.The index FF-IEF(fault frequency-inverse event frequency)is proposed,and fault diagnosis is performed based on the knowledge graph and Bayesian network.We design and develop the prototype system EFKG that contains 12534 entities and 408972 triplets.The system provides knowledge extraction,visual retrieval,and auxiliary decision-making.It can effectively improve the efficiency of information retrieval and maintenance and is of guiding significance for the application of knowledge graphs in the field of engine fault.

关 键 词：柴油发动机 知识图谱 智能制造 FF-IEF 故障诊断 可视化分析 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术] TK407[自动化与计算机技术—计算机科学与技术]