机械工程学报 ›› 2021, Vol. 57 ›› Issue (5): 90-113.doi: 10.3901/JME.2021.05.090
张栋豪1, 刘振宇1, 郏维强1,2, 刘惠1, 谭建荣1
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:
ZHANG Donghao1, LIU Zhenyu1, JIA Weiqiang1,2, LIU Hui1, TAN Jianrong1
Received:
2020-03-15
Revised:
2020-09-18
Online:
2021-03-05
Published:
2021-04-28
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。
中图分类号:
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.
[1] 张映锋,郭振刚,钱成,等. 基于过程感知的底层制造资源智能化建模及其自适应协同优化方法研究[J]. 机械工程学报,2018,54(16):1-10. ZHANG Yingfeng,GUO Zhengang,QIAN Cheng,et al. Investigation on process-aware based intelligent modeling of bottom layer manufacturing resources and self-adaptive collaborative optimization methodology[J]. Journal of Mechanical Engineering,2018,54(16):1-10. [2] 王少杰,侯亮,方奕凯,等. 考虑产品运行大数据的装载机变速箱优化设计[J]. 机械工程学报,2018,54(22):218-232. WANG Shaojie,HOU Liang,FANG Yikai,et al. Optimization design of wheel loader gearbox considering product operational big data[J]. Journal of Mechanical Engineering,2018,54(22):218-232. [3] 徐增林,盛泳潘,贺丽荣,等. 知识图谱技术综述[J]. 电子科技大学学报,2016,45(4):589-606. XU Zenglin,SHENG Yongpan,HE Lirong,et al. Review on knowledge graph techniques[J]. Journal of University of Electronic Science and Technology of China,2016,45(4):589-606. [4] WANG Q,MAO Z,WANG B,et al. Knowledge graph embedding:A survey of approaches and applications[J]. IEEE Transactions on Knowledge and Data Engineering,2017,29(12):2724-2743. [5] HUBAUER T,LAMPARTER S,HAASE P,et al. Use cases of the industrial knowledge graph at siemens[C]//International Semantic Web Conference (P&D/Industry/BlueSky). Monterey,USA,2018: [6] SCHMID S,HENSON C,TRAN T. Using knowledge graphs to search an enterprise data lake[C]//European Semantic Web Conference. Portoroz,Slovenia,2019:262-266. [7] SINGHAL A. Introducing the knowledge graph:Things,not strings[J/OL]. Official Google Blog,2012,5. http://googleblog.blogspot.pt/2015/05/introducing-knowledge-graph-things-nit.html. [8] WU T,QI G,LI C,et al. A survey of techniques for constructing chinese knowledge graphs and their applications[J]. Sustainability,2018,10(9):3245-3270. [9] BOLLACKER K,EVANS C,PARITOSH P,et al. Freebase:A collaboratively created graph database for structuring human knowledge[C]//Proceedings of the 2008 ACM SIGMOD international conference on Management of data. Vancouver,Canada,2008:1247-1250. [10] VRANDEČIĆ D,KRöTZSCH M. Wikidata:A free collaborative knowledgebase[J]. Communications of the ACM,2014,57(10):78-85. [11] LEHMANN J,ISELE R,JAKOB M,et al. DBpedia:A large-scale,multilingual knowledge base extracted from wikipedia[J]. Semantic Web,2015,6(②:167-195. [12] 龚晶,刘检华,赵柏萱,等. 基于知识的管路布局自动评价技术[J]. 计算机集成制造系统,2014,20(10):2522-2531. GONG Jing,LIU Jianhua,ZHAO Boxuan,et al. Knowledge-based automatic evaluating method for pipe routing[J]. Computer Integrated Manufacturing Systems,2014,20(10):2522-2531. [13] 邹慧君,王石刚. 基于多层推理机制的机械产品概念设计[J]. 计算机辅助设计与图形学学报,1997,9(6):548-553. ZOU Huijun,WANG Shigang. Mechanical product conceptual design based on multilevel reasoning[J]. Journal of Computer Aided Design & Computer Graphics,1997,9(6):548-553. [14] 蔡鸿明,何援军,刘胡瑶. 基于分层语义网络的设计资源库建模及实现[J]. 计算机集成制造系统,2005,11(1):73-78. CAI Hongming,HE Yuanjun,LIU Huyao. Modeling and implementation of design resource base based on hierarchy semantics networks[J]. Computer Integrated Manufacturing Systems,2005,11(1):73-78. [15] 郭鑫,赵武,王杰,等. 面向创新设计的工艺设计知识模型及检索方法研究[J]. 机械工程学报,2017,53(15):80-86. GUO Xin,ZHAO Wu,WANG Jie,et al. A study of knowledge modeling and retrieval methods oriented towards innovative design of manufacturing planning[J]. Journal of Mechanical Engineering,2017,53(15):80-86. [16] 李长杰,明新国,邱坤华,等. 基于本体的飞机工装设计知识表示方法[J]. 中国机械工程,2014,25(19):2614-2619. LI Changjie,MING Xinguo,QIU Kunhua,et al. Knowledge representation method for aircraft tooling design based on ontology[J]. China Mechanical Engineering,2014,25(19):2614-2619. [17] CHHIM P,CHINNAM R B,SADAWI N. Product design and manufacturing process based ontology for manufacturing knowledge reuse[J]. Journal of Intelligent Manufacturing,2019,30(2):905-916. [18] 刘航,杜江,白瑀. 基于多维度本体的制造业领域知识语义建模研究[J]. 制造技术与机床,2019(9):140-146. LIU Hang,DU Jiang,BAI Yu. Research on semantic modeling based on multi-dimension ontology for manufacturing domain knowledge[J]. Manufacturing Technology & Machine Tool,2019(9):140-146. [19] BOCK C,ZHA X,SUH H,et al. Ontological product modeling for collaborative design[J]. Advanced Engineering Informatics,2010,24(4):510-524. [20] MIKOLOV T,SUTSKEVER I,CHEN K,et al. Distributed representations of words and phrases and their compositionality[C]//Advances in Neural Information Processing Systems. Nevada,USA,2013:3111-3119. [21] HUANG Z,XU W,YU K. Bidirectional LSTM-CRF models for sequence tagging[J]. arXiv preprint arXiv:.01991,2015. [22] QIU J,WANG Q,ZHOU Y,et al. Fast and accurate recognition of chinese clinical named entities with residual dilated convolutions[C]//2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Madrid,Spain,2018:935-942. [23] YAN H,DENG B,LI X,et al. TENER:Adapting transformer encoder for name entity recognition[J]. arXiv preprint arXiv:.04474,2019: [24] 刘宇飞,尹力,张凯,等. 基于深度迁移学习的技术术语识别——以数控系统领域为例[J]. 情报杂志,2019,38(10):168-175. LIU Yufei,YIN Li,ZHANG Kai,et al. Deep transfer learning for technical term extraction:A case study in computer numerical control system[J]. Journal of Intelligence,2019,38(10):168-175. [25] MINTZ M,BILLS S,SNOW R,et al. Distant supervision for relation extraction without labeled data[C]//Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP:Volume 2-Volume 2. Suntec,Singapore,2009:1003-1011. [26] ZENG D,LIU K,LAI S,et al. Relation classification via convolutional deep neural network[C]//Proceedings of COLING 2014,the 25th International Conference on Computational Linguistics:Technical Papers. Dublin,Ireland,2014:2335-2344. [27] SOROKIN D,GUREVYCH I. Context-aware representations for knowledge base relation extraction[C]//Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Copenhagen,Denmark,2017:1784-1789. [28] LI Z,DING N,LIU Z,et al. Chinese relation extraction with multi-grained information and external linguistic knowledge[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Florence,Italy,2019:4377-4386. [29] BILAN I,ROTH B. Position-aware self-attention with relative positional encodings for slot filling[J]. arXiv preprint arXiv:.03052,2018. [30] LENG J,JIANG P. A deep learning approach for relationship extraction from interaction context in social manufacturing paradigm[J]. Knowledge-Based Systems,2016,100:188-199. [31] WU Y,BAMMAN D,RUSSELL S. Adversarial training for relation extraction[C]//Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Copenhagen,Denmark,2017:1778-1783. [32] SOARES L B,FITZGERALD N,LING J,et al. Matching the blanks:Distributional similarity for relation learning[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Florence,Italy,2019:2895-2905. [33] CHEN M,TIAN Y,YANG M,et al. Multilingual knowledge graph embeddings for cross-lingual knowledge alignment[C]//Proceedings of the 26th International Joint Conference on Artificial Intelligence. Melbourne,Australia,2017:1511-1517. [34] CHEN M,ZHOU T,ZHOU P,et al. Multi-graph affinity embeddings for multilingual knowledge graphs[C]//Proceedings of NIPS Workshop on Automated Knowledge Base Construction. CA,USA,2017: [35] ZHU H,XIE R,LIU Z,et al. Iterative entity alignment via joint knowledge embeddings[C]//Proceedings of the 26th International Joint Conference on Artificial Intelligence. Melbourne,Australia,2017:4258-4264. [36] SUN Z,HU W,ZHANG Q,et al. Bootstrapping entity alignment with knowledge graph embedding[C]//Proceedings of the 27th International Joint Conference on Artificial Intelligence. Stockholm,Sweden,2018:4396-4402. [37] CAO Y,LIU Z,LI C,et al. Multi-channel graph neural network for entity alignment[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Florence,Italy,2019:1452-1461. [38] 张仲伟,曹雷,陈希亮,等. 基于神经网络的知识推理研究综述[J]. 计算机工程与应用,2019,55(12):8-19. ZHANG Zhongwei,CAO Lei,CHEN Xiliang,et al. Survey of knowledge reasoning based on neural network[J]. Computer Engineering and Applications,2019,55(12):8-19. [39] 官赛萍,靳小龙,贾岩涛,等. 面向知识图谱的知识推理研究进展[J]. 软件学报,2018,29(10):74-102. GUAN Saiping,JIN Xiaolong,JIA Yantao,et al. knowledge reasoning over knowledge graph:A survey[J]. Journal of Software,2018,29(10):74-102. [40] BORDES A,USUNIER N,GARCIA-DURAN A,et al. Translating embeddings for modeling multi-relational data[C]//Advances in neural information processing systems. Nevada,USA,2013:2787-2795. [41] WANG Z,ZHANG J,FENG J,et al. Knowledge graph embedding by translating on hyperplanes[C]//Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence. Québec,Canada,2014:1112-1119. [42] KIPF T N,WELLING M. Variational graph auto-encoders[J]. arXiv preprint arXiv:.07308,2016. [43] CAI L,WANG W Y. KBGAN:Adversarial learning for knowledge graph embeddings[C]//Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies,Volume 1(Long Papers). New Orleans,Louisiana,2018:1470-1480. [44] 陈继文,杨红娟,董明晓,等. 基于本体语义块相似匹配的设计知识更新[J]. 机械工程学报,2014,50(7):161-167. CHEN Jiwen,YANG Hongjuan,DONG Mingxiao,et al. Design knowledge updating method based on similarity matching of ontology semantic block[J]. Journal of Mechanical Engineering,2014,50(7):161-167. [45] CAI H,ZHENG V W,CHANG K C-C. A comprehensive survey of graph embedding:Problems,techniques,and applications[J]. IEEE Transactions on Knowledge Data Engineering,2018,30(9):1616-1637. [46] GALáRRAGA L A,TEFLIOUDI C,HOSE K,et al. AMIE:Association rule mining under incomplete evidence in ontological knowledge bases[C]//Proceedings of the 22nd international conference on World Wide Web. Rio de Janeiro,Brazil,2013:413-422. [47] GALáRRAGA L,TEFLIOUDI C,HOSE K,et al. Fast rule mining in ontological knowledge bases with AMIE+[J]. The VLDB Journal-The International Journal on Very Large Data Bases,2015,24(6):707-730. [48] TANON T P,STEPANOVA D,RAZNIEWSKI S,et al. Completeness-aware rule learning from knowledge graphs[C]//International Semantic Web Conference. Vienna,Austria,2017:507-525. [49] HO V T,STEPANOVA D,GAD-ELRAB M H,et al. Rule learning from knowledge graphs guided by embedding models[C]//International Semantic Web Conference. CA,USA,2018:72-90. [50] YANG F,YANG Z,COHEN W W. Differentiable learning of logical rules for knowledge base reasoning[C]//Advances in Neural Information Processing Systems. CA,USA,2017:2319-2328. [51] SADEGHIAN A,ARMANDPOUR M,DING P,et al. DRUM:End-to-end differentiable rule mining on knowledge graphs[C]//Advances in Neural Information Processing Systems. BC,Canada,2019:15321-15331. [52] 曹明宇,李青青,杨志豪,等. 基于知识图谱的原发性肝癌知识问答系统[J]. 中文信息学报,2019,33(6):88-93. CAO Mingyu,LI Qingqing,YANG Zhihao,et al. A question answering system for primary liver cancer based on knowledge graph[J]. Journal of Chinese Information Processing,2019,33(6):88-93. [53] CHEN Y,WU L,ZAKI M J. Bidirectional attentive memory networks for question answering over knowledge bases[C]//Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies,Volume 1(Long and Short Papers). Minneapolis,Minnesota,2019:2913-2923. [54] SAHA A,ANSARI G A,LADDHA A,et al. Complex program induction for querying knowledge bases in the absence of gold programs[J]. Transactions of the Association for Computational Linguistics,2019,7:185-200. [55] YIH W-t,CHANG M-W,HE X,et al. Semantic parsing via staged query graph generation:Question answering with knowledge base[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1:Long Papers). Beijing,China,2015:1321-1331. [56] LI F-L,CHEN W,HUANG Q,et al. AliMe KBQA:Question answering over structured knowledge for e-commerce customer service[J]. arXiv Preprint arXiv:.05728,2019. [57] MILNE D,WITTEN I H. Learning to link with wikipedia[C]//Proceedings of the 17th ACM conference on Information and knowledge management. California,USA,2008:509-518. [58] SPITKOVSKY V I,CHANG A X. A cross-lingual dictionary for english wikipedia concepts[C]//Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC-201②. Istanbul,Turkey,2012:3168-3175. [59] YANG Y,CHANG M-W. S-MART:Novel tree-based structured learning algorithms applied to tweet entity linking[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1:Long Papers). Beijing,China,2015:504-513. [60] 任朝淦,杨燕,贾真,等. 基于注意力机制的问句实体链接[J]. 模式识别与人工智能,2018,31(12):69-75. REN Chaogan,YANG Yan,JIA Zhen,et al. Attention mechanism based question entity linking[J]. Pattern Recognition and Artificial Intelligence,2018,31(12):69-75. [61] LE P,TITOV I. Improving entity linking by modeling latent relations between mentions[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1:Long Papers). Melbourne,Australia,2018:1595-1604. [62] BAO J,DUAN N,YAN Z,et al. Constraint-based question answering with knowledge graph[C]//Proceedings of COLING 2016,the 26th International Conference on Computational Linguistics:Technical Papers. Osaka,Japan,2016:2503-2514. [63] HU S,ZOU L,ZHANG X. A state-transition framework to answer complex questions over knowledge base[C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Brussels,Belgium,2018:2098-2108. [64] ABUJABAL A,SAHA ROY R,YAHYA M,et al. Never-ending learning for open-domain question answering over knowledge bases[C]//Proceedings of the 2018 World Wide Web Conference. Lyon,France,2018:1053-1062. [65] 徐荣振,高琦,邵祖光,等. 基于本体填充的设计案例获取方法研究[J]. 组合机床与自动化加工技术,2019,(7):125-129. XU Rongzhen,GAO Qi,SHAO Zuguang,et al. Research on a method of case requirement based on ontology population[J]. Modular Machine Tool & Automatic Manufacturing Technique,2019,(7):125-129. [66] RENDLE S. Factorization machines with libfm[J]. ACM Transactions on Intelligent Systems Technology,2012,3(3):57-78. [67] YU X,REN X,SUN Y,et al. Personalized entity recommendation:A heterogeneous information network approach[C]//Proceedings of the 7th ACM International Conference on Web Search and Data Mining. NY,USA,2014:283-292. [68] ZHAO H,YAO Q,LI J,et al. Meta-graph based recommendation fusion over heterogeneous information networks[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. NS,Canada,2017:635-644. [69] 朱桂明,宾辰忠,古天龙,等. 基于知识图谱的用户偏好神经建模框架[J]. 模式识别与人工智能,2019,32(7):661-668. ZHU Guiming,BIN Chenzhong,GU Tianlong,et al. Neural user preference modeling framework based on knowledge graph[J]. Pattern Recognition and Artificial Intelligence,2019,32(7):661-668. [70] WANG H,ZHANG F,XIE X,et al. DKN:Deep knowledge-aware network for news recommendation[C]//Proceedings of the 2018 World Wide Web Conference. Lyon,France,2018:1835-1844. [71] ZHANG F,YUAN N J,LIAN D,et al. Collaborative knowledge base embedding for recommender systems[C]//Proceedings of the 22nd ACM SIGKDD international Conference on Knowledge Discovery and Data Mining. San Francisco,USA,2016:353-362. [72] WANG H,ZHANG F,WANG J,et al. Ripplenet:Propagating user preferences on the knowledge graph for recommender systems[C]//Proceedings of the 27th ACM International Conference on Information and Knowledge Management. Torino,Italy,2018:417-426. [73] WANG H,ZHANG F,ZHAO M,et al. Multi-task feature learning for knowledge graph enhanced recommendation[C]//The World Wide Web Conference. CA,USA,2019:2000-2010. [74] 傅柱,王曰芬,丁绪辉. 面向知识重用的设计过程知识语义表示研究[J]. 数据分析与知识发现,2019,3(6):21-29. FU Zhu,WANG Yuefen,DING Xuhui. Semantic representation of design process knowledge reuse[J]. Data Analysis and Knowledge Discovery,2019,3(6):21-29. [75] 毕鲁雁,焦宗夏,范圣韬. 基于本体映射的产品概念设计方案生成新方法[J]. 北京航空航天大学学报,2009,35(7):895-898. BI Luyan,JIAO Zongxia,FAN Shengtao. New product conceptual design scheme generation method based on ontology mapping[J]. Journal of Beijing University of Aeronautics and Astronautics,2009,35(7):895-898. [76] KULAK O,CEBI S,KAHRAMAN C. Applications of axiomatic design principles:A literature review[J]. Expert Systems with Applications,2010,37(9):6705-6717. [77] JIA W,LIU Z,LIN Z,et al. Quantification for the importance degree of engineering characteristics with a multi-level hierarchical structure in QFD[J]. International Journal of Production Research,2016,54(6):1627-1649. [78] 谭建荣,李涛,戴若夷. 支持大批量定制的产品配置设计系统的研究[J]. 计算机辅助设计与图形学学报,2003,15(8):931-937. TAN Jianrong,LI Tao,DAI Ruoyi. Research on configuration design system supporting mass customization[J]. Journal of Computer Aided Design & Computer Graphics,2003,15(8):931-937. [79] 郏维强,刘振宇,刘达新,等. 基于模糊关联的复杂产品模块化设计方法及其应用[J]. 机械工程学报,2015,51(5):130-142. JIA Weiqiang,LIU Zhenyu,LIU Daxin,et al. Modular design method and application for complex product based on fuzzy correlation analysis[J]. Journal of Mechanical Engineering,2015,51(5):130-142. [80] RAN Q,LIN Y,LI P,et al. NumNet:Machine reading comprehension with numerical reasoning[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Hong Kong,China,2019:2474-2484. [81] DU W,ZHANG Y,ZHOU W. Modified non-gaussian multivariate statistical process monitoring based on the gaussian distribution transformation[J]. Journal of Process Control,2020,85:1-14. [82] 陶飞,刘蔚然,刘检华,等. 数字孪生及其应用探索[J]. 计算机集成制造系统,2018,24(1):1-18. TAO Fei,LIU Weiran,LIU Jianhua,et al. Digital twin and its potential application exploration[J]. Computer Integrated Manufacturing Systems,2018,24(1):1-18. [83] LIU Z,ZHANG D,JIA W,et al. An adversarial bidirectional serial-parallel LSTM-based QTD framework for product quality prediction[J]. Journal of Intelligent Manufacturing,2020:1-19. [84] 姚成玉,饶乐庆,陈东宁,等. T-S动态故障树分析方法[J]. 机械工程学报,2019,55(16):17-32. YAO Chengyu,RAO Leqing,CHEN Dongning,et al. T-S dynamic fault tree analysis method[J]. Journal of Mechanical Engineering,2019,55(16):17-32. [85] LIU H,LIU Z,JIA W,et al. A novel deep learning-based encoder-decoder model for remaining useful life prediction[C]//2019 International Joint Conference on Neural Networks (IJCNN). Budapest,Hungary,2019:1-8. [86] 里鹏,史海波,尚文利,等. 基于SP95标准的工厂模型设计与建模方法研究[J]. 计算机集成制造系统,2009,15(3):458-462. LI Peng,SHI Haibo,SHANG Wenli,et al. Design of plant model and its modeling method based on SP95 standard[J]. Computer Integrated Manufacturing Systems,2009,15(3):458-462. |
[1] | 林京. 机器信息学:机械产品智能化的学科支撑[J]. 机械工程学报, 2021, 57(2): 11-20. |
[2] | 王柏村, 黄思翰, 易兵, 鲍劲松. 面向智能制造的人因工程研究与发展[J]. 机械工程学报, 2020, 56(16): 240-253. |
[3] | 任杉, 张映锋, 黄彬彬. 生命周期大数据驱动的复杂产品智能制造服务新模式研究[J]. 机械工程学报, 2018, 54(22): 194-203. |
[4] | 熊振华, 孙宇昕, 丁龙杨. 智能车床的颤振实时辨识与在线抑制系统研究[J]. 机械工程学报, 2018, 54(17): 85-93. |
[5] | 陶飞, 戚庆林. 面向服务的智能制造[J]. 机械工程学报, 2018, 54(16): 11-23. |
[6] | 张映锋, 郭振刚, 钱成, 李锐. 基于过程感知的底层制造资源智能化建模及其自适应协同优化方法研究[J]. 机械工程学报, 2018, 54(16): 1-10. |
[7] | 何学俭;虞钢. 激光智能制造系统中同步控制的实现[J]. , 2004, 40(5): 126-130. |
[8] | 赵福民;王治森 高锷;张勇. Agent技术在智能制造系统中的应用研究[J]. , 2002, 38(7): 140-144. |
阅读次数 | ||||||
全文 |
|
|||||
摘要 |
|
|||||