The digital twins process of equipment operation and maintenance involves massive data and complex business logic. Through data awareness of equipment and workshop environment. According to the characteristics of equipment operation and maintenance, the data characteristics and requirements are analysed, an equipment operation and maintenance management system based on the micro-service framework is proposed, a set of equipment operation and maintenance digital twins are designed and a general reference software architecture is designed and by checking the underlying in-heap pattern reduces high-impact portions of memory to provide accurate follow-up complex digital twins modelling provides the architectural foundation.
QiQTaoF. Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison. IEEE Access2018; 6: 3585–3593.
2.
ZhouGZhangCLiZ, et al. Knowledge-driven digital twin manufacturing cell towards intelligent manufacturing. Int J Prod Res2020; 58: 1034–1051.
3.
MaYZhouHHeH, et al. A digital twin-based approach for quality control and optimization of complex product assembly. In: 2019 international conference on artificial intelligence and advanced manufacturing (AIAM), Dublin, Ireland, 16–18 October 2019, pp.762–767. New York: IEEE.
4.
GrievesMVickersJ. Digital twin: mitigating unpredictable, undesirable emergent behavior in complex systems. In: KahlenJFlumerfeltSAlvesA (eds) Transdisciplinary perspectives on complex systems: new findings and approaches. Cham: Springer, 2017, pp.85–113.
5.
EswaranMBahubalendruniMR. Challenges and opportunities on AR/VR technologies for manufacturing systems in the context of industry 4.0: a state of the art review. J Manuf Syst2022; 65: 260–278.
6.
EswaranMGulivindalaAKInkuluAK, et al. Augmented reality-based guidance in product assembly and maintenance/repair perspective: a state of the art review on challenges and opportunities. Expert Syst Appl2023; 213: 18.
MourtzisDZogopoulosVVlachouE. Augmented reality application to support remote maintenance as a service in the robotics industry. Procedia CIRP2017; 63: 46–51.
9.
ZenisekJWildNWolfartsbergerJ. Investigating the potential of smart manufacturing technologies. Procedia Comput Sci2021; 180: 507–516.
10.
BaoJGuoDLiJ, et al. The modelling and operations for the digital twin in the context of manufacturing. Enterp Inf Syst2019; 13: 534–556.
11.
AheleroffSXuXZhongRY, et al. Digital twin as a service (DTaaS) in industry 4.0: an architecture reference model. Adv Eng Inform2021; 47: 101225.
12.
TuegelEJIngraffeaAREasonTG, et al. Reengineering aircraft structural life prediction using a digital twin. Int J Aerosp Eng2011; 2011: 154798.
13.
NguyenTNPonciroliRBruckP, et al. A digital twin approach to system-level fault detection and diagnosis for improved equipment health monitoring. Ann Nucl Energy2022; 170: 109002.
14.
PivanoLNguyenDTBruun LudvigsenK. Digital twin for drilling operations–towards cloud-based operational planning. In: Offshore technology conference, Houston, TX, USA, May 2019.
15.
AivaliotisPGeorgouliasKChryssolourisG. The use of Digital Twin for predictive maintenance in manufacturing. Int J Comput Integr Manuf2019; 32: 1067–1080.
16.
LiewJXBinJLiuZ. Software as a service: the future of NDI data analysis in the cloud. Insight2019; 61: 341–346.
17.
BarthelmeyALeeEHanaR, et al. Dynamic digital twin for predictive maintenance in flexible production systems. In: IECON 2019-45th annual conference of the ieee industrial electronics society, Lisbon, Portugal, 14–17 October 2019, pp.4209–4214. New York: IEEE.
18.
BellavistaPMoraA. Edge cloud as an enabler for distributed AI in industrial IoT applications: the experience of the IoTwins project. In: AI&IoT@ AI* IA, 2019, pp.1–15.
19.
BonneyMSDe AngelisMDal BorgoM, et al. Development of a digital twin operational platform using Python Flask. Data-Centric Eng2022; 3: e1.
Di FrancescoPLagoPMalavoltaI. Migrating towards microservice architectures: an industrial survey. In: 2018 IEEE international conference on software architecture (ICSA), Seattle, WA, USA, 30 April–4 May 2018, pp.29–2909. New York: IEEE.
28.
ZhouJLiLZhouN. Research and application of battery production data management system based on microservice. In: 2019 IEEE 9th international conference on electronics information and emergency communication (ICEIEC), Beijing, China, 12–14 July 2019, pp.1–5. New York: IEEE.
29.
KuhnMFrankeJ. Smart manufacturing traceability for automotive E/E systems enabled by event-driven microservice architecture. In: 2020 IEEE 11th international conference on mechanical and intelligent manufacturing technologies (ICMIMT), Cape town, South Africa, 20–22 January 2020, pp.142–148. New York: IEEE.
30.
HöhrSTasciTVerlA. Realization of data analytics projects in manufacturing using a microservice-based approach. In: 2019 IEEE international conference on mechatronics (ICM), Ilmenau, Germany, 18–20 March 2019, pp.321–326. New York: IEEE.
31.
Dinh-TuanHBeierleFGarzonSR. MAIA: a microservices-based architecture for industrial data analytics. In: 2019 IEEE international conference on industrial cyber physical systems (ICPS), Taipei, Taiwan, 6–9 May 2019, pp.23–30. New York: IEEE.
FujimotoT. Key concepts for improving supply chain robustness. Discuss Pap Ser2019; 511: 1–20.
34.
ZhouBZhangJFuY, et al. Research on power and vibration signal acquisition method and system of machine tool for tool wear monitoring. J Phys Conf Ser2021; 1939: 012114.
35.
GuoJYangH. Three-stage optimisation method for concurrent manufacturing energy data collection. Int J Comput Integr Manuf2018; 31: 479–489.
36.
HaoZYanH. Research on communication of heterogeneous CNC machine tools based on communication integration class library. In: 2021 IEEE 5th information technology, networking, electronic and automation control conference (ITNEC), Xi’an, China, 15–17 October 2021, pp.227–231. New York: IEEE.
37.
GuoJChengYWangD, et al. Industrial Dataspace for smart manufacturing: connotation, key technologies, and framework. Int J Prod Res2021; 61: 3868–3883.
38.
GaoFYuePWuZ, et al. Geospatial data storage based on HBase and MapReduce. In: 2017 6th international conference on agro-geoinformatics, Fairfax, VA, USA, 7–10 August 2017, pp.1–4. New York: IEEE.
39.
HuangSCaiLLiuZ, et al. Non-structure data storage technology: a discussion. In: 2012 IEEE/ACIS 11th international conference on computer and information science, Shanghai, China, 30 May–1 June 2012, pp.482–487. New York: IEEE.
40.
BernsteinD. Containers and cloud: from LXC to docker to kubernetes. IEEE Cloud Comput2014; 1: 81–84.
41.
PahlC. Containerization and the paas cloud. IEEE Cloud Comput2015; 2: 24–31.
42.
LiuQLuGHuangJ, et al. Development of tunnel intelligent monitoring and early warning system based on micro-service architecture: the case of AnPing tunnel. Geomat Nat Hazards Risk2020; 11: 1404–1425.
43.
HanJHaihongELeG, et al. Survey on NoSQL database. In: 2011 6th international conference on pervasive computing and applications, Port Elizabeth, 26–28 October 2011, pp.363–366. New York: IEEE.
44.
MitchellNSevitskyG. The causes of bloat, the limits of health. In: Proceedings of the 22nd annual ACM SIGPLAN conference on object-oriented programming systems, languages and applications, Montreal, QC, Canada, October 2007, pp.245–260. New York: ACM.
45.
ChisAEMitchellNSchonbergE, et al. Patterns of memory inefficiency. In: MeziniM (ed.) ECOOP 2011–object-oriented programming. Berlin, Heidelberg: Springer, 2011, pp.383–407.
SoldatosJLazaroOCavadiniF. The digital shopfloor-industrial automation in the industry 4.0 era: performance analysis and applications. New York, USA: CRC Press, 2022.
