Architecture and Key Technologies of Digital-twin-driven Intelligent Operation & Maintenance Services for Complex Product

doi:10.3901/JME.2022.12.250

Abstract

Abstract: Focused on the problem of virtual-real interaction and collaboration and intelligent management for complex product, the architecture of digital-twin-driven intelligent operation and maintenance services for complex product (DT-IOMS-CP) is proposed. Then the implementation logic and key technologies of DT-IOMS-CP are elaborated, which includes formalized modelling and cooperative interaction of physical-virtual spaces, operation and maintenance (O&M) knowledge acquisition of complex product based on multi-source data, process tracking and monitoring based on virtual-real interaction, and service-oriented real-time diagnosis and active maintenance, etc. Through the implementation of above-mentioned architecture and key technologies, a management and collaboration mechanism of complex product O&M characterized by virtual-real interaction and fusion, dynamic updating of O&M knowledge and active performing of O&M services is established. Finally, the CNC machine tool is used to verify the effectiveness and feasibility of the proposed architecture and methods in the aspects of architecture application mode and identification method of O&M key performance indicator. The proposed framework and key technologies could provide a reference solution for intelligent, collaborative and service-oriented management and application of complex product.

Key words: digital twin, complex product, knowledge acquisition, operation and maintenance management

CLC Number:

TP39
TP301

HUANG Binbin, ZHANG Yingfeng, HUANG Bo, REN Shan, SHI Lichun. Architecture and Key Technologies of Digital-twin-driven Intelligent Operation & Maintenance Services for Complex Product[J]. Journal of Mechanical Engineering, 2022, 58(12): 250-260.

References

[1] 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. REN Shan, ZHANG Yingfeng, HUANG Binbin. New pattern of lifecycle big-data-driven smart manufacturing service for complex product[J]. Journal of Mechanical Engineering, 2018, 54(22):194-203.
[2] LEE J, BAGHERI B, KAO H A. A cyber-physical systems architecture for industry 4.0-based manufacturing systems[J]. Manufacturing Letters, 2015(3):18-23.
[3] ZHANG Y, GUO Z, LV J, et al. A framework for smart production-logistics systems based on CPS and industrial IoT[J]. IEEE Transactions on Industrial Informatics, 2018, 14(9):4019-4032.
[4] 张映锋, 赵曦滨, 孙树栋, 等. 一种基于物联技术的制造执行系统实现方法与关键技术[J]. 计算机集成制造系统, 2012, 18(12):2634-2642. ZHANG Yingfeng, ZHAO Xibin, SUN Shudong, et al. Implementing method and key technologies for IoT-based manufacturing exection system[J]. Computer Integrated Manufacturing Systems, 2012, 18(12):2634-2642.
[5] MRUGALSKA B, WYRWICKA M K. Towards lean production in industry 4.0[J]. Procedia Engineering, 2017, 182:466-473.
[6] POSADA J, TORO C, BARANDIARAN I, et al. Visual computing as a key enabling technology for industrie 4.0 and industrial internet[J]. IEEE Computer Graphics and Applications, 2015, 35(2):26-40.
[7] 国务院. 国务院关于印发《中国制造2025》的通知.[EB/OL].[2019-11-25]. http://www.gov.cn/zhengce/content/2015-05/19/content_9784.htm, 2015-5-8. The State Council. The notice of made in china 2025 strategy of the state council.[EB/OL].[2019-11-25]. http://www.gov.cn/zhengce/content/2015-05/19/content_9784.htm, 2015-5-8.
[8] 孟松鹤, 叶雨玫, 杨强, 等. 数字孪生及其在航空航天中的应用[J]. 航空学报, 2020, 41(9):6-17. MENG Songhe, YE Yumei, YANG Qiang, et al. Digital twin and its aerospace applications[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(9):6-17.
[9] GLAESSGEN E, STARGEL D. The digital twin paradigm for future NASA and US Air Force vehicles[C]//53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, April 23-26, 2012, Honolulu, HI, United states:AIAA International, 2012:1818.
[10] ZHOU G, ZHANG C, LI Z, et al. Knowledge-driven digital twin manufacturing cell towards intelligent manufacturing[J]. International Journal of Production Research, 2020, 58(4):34-1051.
[11] TAO F, ZHANG M, LIU Y, et al. Digital twin driven prognostics and health management for complex equipment[J]. CIRP Annals, 2018, 67(1):169-172.
[12] WANG J, YE L, GAO R X, et al. Digital twin for rotating machinery fault diagnosis in smart manufacturing[J]. International Journal of Production Research, 2019, 57(12):3920-3934.
[13] 郭飞燕, 刘检华, 邹方, 等. 数字孪生驱动的装配工艺设计现状及关键实现技术研究[J]. 机械工程学报, 2019, 55(17):110-132. GUO Feiyan, LIU Jianhua, ZOU Fang, et al. Research on the state-of art, connotation and key implementation technology of assembly process planning with digital twin[J]. Journal of Mechanical Engineering, 2019, 55(17):110-132.
[14] SUN X, BAO J, LI J, et al. A digital twin-driven approach for the assembly-commissioning of high precision products[J]. Robotics and Computer-Integrated Manufacturing, 2020, 61:101839.
[15] GRIEVES M, VICKERS J. Digital twin:Mitigating unpredictable, undesirable emergent behavior in complex systems[M]. BeHin:Springer International Publishing, 2017.
[16] WARWICK G. GE advances analytical maintenance with digital twins[N]. Aviation Week & Space Technology, 2015-10-19.
[17] 任杉. 产品生命周期大数据驱动的设计运维集成服务方法研究[D]. 西安:西北工业大学, 2019. REN Shan. Product lifecycle big data-driven approach of the integrated service for design and operation and maintenance[D]. Xi'an:Northwestern Polytechnical University, 2019.
[18] ZHANG Y, REN S, LIU Y, et al. A framework for big data driven product lifecycle management[J]. Journal of Cleaner Production, 2017, 159:229-240.
[19] REN S, ZHAO X B, HUANG B B, et al. A framework for shopfloor material delivery based on real-time manufacturing big data[J]. Journal of Ambient Intelligence and Humanized Computing, 2019, 10:1093-1108.
[20] SCHMIDHUBER J. Deep learning in neural networks:An overview[J]. Neural Networks, 2015, 61:85-117.
[21] BENGIO Y, COURVILLE A, VINCENT P. Representation learning:A review and new perspectives[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(8):1798-1828.