知识图谱在智能制造领域的研究现状及其应用前景综述

doi:10.3901/JME.2021.05.090

机械工程学报 ›› 2021, Vol. 57 ›› Issue (5): 90-113.doi: 10.3901/JME.2021.05.090

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知识图谱在智能制造领域的研究现状及其应用前景综述

张栋豪¹, 刘振宇¹, 郏维强^1,2, 刘惠¹, 谭建荣¹

1. 浙江大学计算机辅助设计与图形学国家重点实验室杭州 310027;
2. 信雅达系统工程股份有限公司浙江省重点大数据研究院杭州 310053

收稿日期:2020-03-15 修回日期:2020-09-18 出版日期:2021-03-05 发布日期:2021-04-28
通讯作者: 刘振宇(通信作者),男,1974年出生,博士,教授,博士研究生导师。主要研究方向为复杂装备数字化设计与制造、复杂装备健康管理等。E-mail:liuzy@zju.edu.cn
作者简介:张栋豪,男,1995年出生,博士研究生。主要研究方向为知识图谱、自然语言处理、基于深度学习的数据挖掘、质量预测、复杂装备健康管理等。E-mail:dhz@zju.edu.cn;郏维强,男,1986年出生,博士。主要研究方向为知识图谱、自然语言处理、基于深度学习的数据挖掘等。E-mail:wqjia@zju.edu.cn;weiq.jia@sunyard.com;刘惠,男,1994年出生,博士研究生。主要研究方向为传感器数据分析、基于深度学习的故障预测、复杂装备健康管理等。E-mail:liuhui2017@zju.edu.cn;谭建荣,男,1954年出生,博士,教授,博士生研究生导师,中国工程院院士,中国机械工程学会副理事长。主要研究方向为复杂装备数字化设计与制造、机械设计及理论、复杂装备健康管理等。E-mail:egi@zju.edu.cn
基金资助:
国家自然科学基金（51935009，51805473）和浙江省重点研发计划（2021C01008）资助项目。

A Review on Knowledge Graph and Its Application Prospects to Intelligent Manufacturing

ZHANG Donghao¹, LIU Zhenyu¹, JIA Weiqiang^1,2, LIU Hui¹, TAN Jianrong¹

1. State Key Laboratory of CAD & CG, Zhejiang University, Hangzhou 310027;
2. Key Research Institute for Big Data of Zhejiang Province, Sunyard System Engineering Co., Ltd., Hangzhou 310053

Received:2020-03-15 Revised:2020-09-18 Online:2021-03-05 Published:2021-04-28

摘要/Abstract

摘要： 数据和知识是新一代信息技术与智能制造深度融合的基础。然而，当前产品设计、制造、装配和服务等过程中，数据及知识的存储大多以传统关系型数据库为基础，这导致了数据及知识的冗余性和搜索及推理的低效性。近年来，知识图谱技术飞速发展起来，它本质上是基于语义网络的思想，可以实现对现实世界的事物及其相互关系的形式化描述。该技术为智能制造领域数据及知识的关联性表达和相关性搜索推理问题的解决带来了可能性，因此其在智能制造的实现过程中扮演着越来越重要的角色。为了给知识图谱在智能制造领域的应用提供理论支撑，总结了知识图谱领域的研究进展；同时探索了知识图谱在智能制造领域的3大类应用方向，共15小类应用前景，分析了在各个应用前景上与传统方法的不同之处，应用过程中所需要使用的知识图谱相关技术以及实施过程中所待突破的关键技术，希望可以为进一步展开针对知识图谱在智能制造领域的研究提供启发，同时为相关企业针对知识图谱的实际应用提供参考；最后以数控车床故障分析为案例，验证了知识图谱在智能制造领域应用的有效性。

关键词: 知识图谱, 研究综述, 语义网络, 智能制造

Abstract: Data and knowledge are the basis for the deep integration of new-generation information technology and intelligent manufacturing. However, the storage of data and knowledge in the processes of product design, manufacturing, assembly and service is mostly based on relational database, which brings data redundancy and inefficiency of searching and reasoning. Recently, knowledge graph technology, based on the idea of semantic network, has developed rapidly. It can achieve the description of real-world things and their relationships, which provides a mean for the correlation representation of data and knowledge, and a solution of the relevance searching and reasoning problem in the area of intelligent manufacturing. Therefore, it plays an increasingly important role in the realization of intelligent manufacturing. In order to provide the theoretical support for the application of knowledge graph, a review about the research status of knowledge graph is provided. At the same time, three major applications of knowledge graph in the area of intelligent manufacturing are explored, including a total of 15 small application prospects. Among them, the differences compared with traditional methods, the knowledge graph technology to be introduced and the key technologies to be breakthrough are detailed. It is hoped that it can provide inspiration for researchers to further carry out study on the knowledge graph in the area of intelligent manufacturing, and provide reference for mechanical companies on the application of knowledge graph. Finally, a case about the lathe failure analysis is used to verify the superiority of the knowledge graph in the area of intelligent manufacturing.

Key words: knowledge graph, literature review, semantic network, intelligent manufacturing

中图分类号:

TP301

张栋豪, 刘振宇, 郏维强, 刘惠, 谭建荣. 知识图谱在智能制造领域的研究现状及其应用前景综述[J]. 机械工程学报, 2021, 57(5): 90-113.

ZHANG Donghao, LIU Zhenyu, JIA Weiqiang, LIU Hui, TAN Jianrong. A Review on Knowledge Graph and Its Application Prospects to Intelligent Manufacturing[J]. Journal of Mechanical Engineering, 2021, 57(5): 90-113.

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知识图谱在智能制造领域的研究现状及其应用前景综述

A Review on Knowledge Graph and Its Application Prospects to Intelligent Manufacturing

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