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Vol 275
Pages:
30-41
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RUS ENG

Digital transformation of industrial machinery repair and maintenance to build an industrial metaverse

Authors:
Natalia I. Koteleva1
Vladislav V. Valnev2
Aleksandr S. Simakov3
Maysam Mohammadzadeh Shirazi4
About authors
  • 1 — Ph.D. Associate Professor Empress Catherine II Saint Petersburg Mining University ▪ Orcid
  • 2 — Postgraduate Student Empress Catherine II Saint Petersburg Mining University ▪ Orcid
  • 3 — Ph.D. Associate professor Empress Catherine II Saint Petersburg Mining University ▪ Orcid
  • 4 — Ph.D. Head of Department Shiraz University ▪ Orcid
Date submitted:
2025-02-21
Date accepted:
2025-09-02
Online publication date:
2025-10-07
Date published:
2025-10-31

Abstract

Industrial metaverse is a new direction for development of industrial enterprises. Nowadays the process of full understanding term “industrial metaverse”, its conception, its effectiveness for enterprises is not actualy completed. The methodology, tools, and methods for building an industrial metaverse have not been clearly defined. Therefore, it is advisable to experimentally implement a part of the metaverse on one or several processes with future scaling to other processes. The process of industrial machinery repair and maintenance is proposed as an experimental zone for implementing the industrial metaverse. This process is well suited as an experimental zone. Launching the industrial metaverse concept on it will solve a number of problems, such as the diversity of equipment with unique diagnostic and repair methods, human errors made during repair work, etc. This article presents the concept of building and the architecture of industrial metaverse. A general description of the physical, cyber-physical, and social spaces and the interaction layer between them is provided, without any details of qualitative and quantitative indicators. The avatar of a service engineer is highlighted as one of the elements of the cyber-physical space. The process of creating an avatar of a cyber-physical service engineer is considered: a description of the main functionality is provided, it is shown that a combined system of wearable devices – a glove and a video camera integrated into glasses, a vest, a helmet, or represented by an independent device – is sufficient for its creation. Laboratory experiments were conducted, where the created avatar was tested to determine the task of servicing a centrifugal pump. The results of processing 518 experimental datasets of 10 points, each of which belongs to one of six classes corresponding to a specific technological operation during servicing of a centrifugal pump are shown. Three types of models were obtained (accuracy on training data 0.99; 1.0; 1.0, accuracy on test data 0.625; 0.7; 1.0). It has been shown that achieving 1.0 accuracy on training and test data requires first identifying features representing frequency and temporal characteristics obtained through time series processing. The obtained results allow to make conclusions about the readiness of these technologies for industrial implementation.

Область исследования:
Geotechnical Engineering and Engineering Geology
Keywords:
industrial machinery repair and maintenance industrial metaverse digital transformation Industry 5.0 service engineer avatar wearable devices artificial intelligence
Go to volume 275

References

  1. Bouabid D.A., Hadef H., Innal F. Maintenance as a sustainability tool in high-risk process industries: A review and future directions. Journal of Loss Prevention in the Process Industries. 2024. Vol. 89. N 105318. DOI: 10.1016/j.jlp.2024.105318
  2. Tokarev I.S., Nazarychev A.N., Shklyarsky Ya.E., Skvortsov I.V. Ensuring the sustainable operation of autonomous power systems in the gas industry. Energetik. 2024. N 7, p. 15-19 (in Russian).
  3. Mallioris P., Aivazidou E., Bechtsis D. Predictive maintenance in Industry 4.0: A systematic multi-sector mapping. CIRP Journal of Manufacturing Science and Technology. 2024. Vol. 50, p. 80-103. DOI: 10.1016/j.cirpj.2024.02.003
  4. Nedashkovskaya E.S., Sheshukova E.I., Korogodin A.S. et al. Structure of the system of maintenance and repair of mining machines. Transport, mining and construction engineering: science and production. 2024. N 25, p. 155-162 (in Russian). DOI: 10.26160/2658-3305-2024-25-155-162
  5. Dayo-Olupona O., Genc B., Celik T., Bada S. Adoptable approaches to predictive maintenance in mining industry: An overview. Resources Policy. 2023. Vol. 86. Part A. N 104291. DOI: 10.1016/j.resourpol.2023.104291
  6. Wari E., Weihang Zhu, Gino Lim. Maintenance in the downstream petroleum industry: A review on methodology and implementation. Computers & Chemical Engineering. 2023. Vol. 172. N 108177. DOI: 10.1016/j.compchemeng.2023.108177
  7. Zhukovsky Yu.L., Suslikov P.K. Assessment of the potential effect of applying demand management technology at mining enterprises. Sustainable Development of Mountain Territories. 2024. Vol. 16. N 3 (61), p. 895-908 (in Russian). DOI: 10.21177/1998-4502-2024-16-3-895-908
  8. Psarommatis F., May G., Azamfirei V. Envisioning maintenance 5.0: Insights from a systematic literature review of Industry 4.0 and a proposed framework. Journal of Manufacturing Systems. 2023. Vol. 68, p. 376-399. DOI: 10.1016/j.jmsy.2023.04.009
  9. Alves F.F., Ravetti M.G. Hybrid proactive approach for solving maintenance and planning problems in the scenario of Industry 4.0. IFAC-PapersOnLine. 2020. Vol. 53. Iss. 3, p. 216-221. DOI: 10.1016/j.ifacol.2020.11.035
  10. Palmitessa E., Premoli A., Roda I., Macchi M. Integrating maintenance and energy problems through a Digital Twin-based decision support framework under the guidance of Asset Management. IFAC-PapersOnLine. 2024. Vol. 58. Iss. 8, p. 7-12. DOI: 10.1016/j.ifacol.2024.08.042
  11. Kans M., Campos J. Digital capabilities driving industry 4.0 and 5.0 transformation: Insights from an interview study in the maintenance domain. Journal of Open Innovation: Technology, Market, and Complexity. 2024. Vol. 10. Iss. 4. N 100384. DOI: 10.1016/j.joitmc.2024.100384
  12. Ahmed Murtaza A., Saher A., Hamza Zafar M et al. Paradigm shift for predictive maintenance and condition monitoring from Industry 4.0 to Industry 5.0: A systematic review, challenges and case study. Results in Engineering. 2024. Vol. 24. N 102935. DOI: 10.1016/j.rineng.2024.102935
  13. Litvinenko V.S. Digital Economy as a Factor in the Technological Development of the Mineral Sector. Natural Resources Research. 2020. Vol. 29. Iss. 3, p. 1521-1541. DOI: 10.1007/s11053-020-09716-1
  14. Korolev N., Kozyaruk A., Morenov V. Efficiency Increase of Energy Systems in Oil and Gas Industry by Evaluation of Electric Drive Lifecycle. Energies. 2021. Vol. 14. Iss. 19, p. 6074. DOI: 10.3390/en14196074
  15. Cherepovitsyn A., Solovyova V., Dmitrieva D. New challenges for the sustainable development of the rare-earth metals sector in Russia: Transforming industrial policies. Resources Policy. 2023. Vol. 81. N 103347. DOI: 10.1016/j.resourpol.2023.103347
  16. Makhovikov A.B., Filyasova Yu.A. Information technologies for solid mineral extraction in the Arctic. Sustainable Development of Mountain Territories. 2024. Vol. 16. N 3 (61), p. 1110-1117 (in Russian). DOI: 10.21177/1998-4502-2024-16-3-1110-1117
  17. Xiao Wang, Yutong Wang, Jing Yang et al. The survey on multi-source data fusion in cyber-physical-social systems: Foundational infrastructure for industrial metaverses and industries 5.0. Information Fusion. 2024. Vol. 107. N 102321. DOI: 10.1016/j.inffus.2024.102321
  18. Martínez-Gutiérrez A., Díez-González J., Perez H., Araújo M. Towards industry 5.0 through metaverse. Robotics and Computer-Integrated Manufacturing. 2024. Vol. 89. N 102764. DOI: 10.1016/j.rcim.2024.102764
  19. Alkaeed M., Qayyum A., Qadir J. Privacy preservation in Artificial Intelligence and Extended Reality (AI-XR) metaverses: A survey. Journal of Network and Computer Applications. 2024. Vol. 231. N 103989. DOI: 10.1016/j.jnca.2024.103989
  20. Hosseini S., Abbasi A., Magalhaes L.G. et al. Immersive Interaction in Digital Factory: Metaverse in Manufacturing. Procedia Computer Science. 2024. Vol. 232, p. 2310-2320. DOI: 10.1016/j.procs.2024.02.050
  21. Starly B., Koprov P., Bharadwaj A. et al. “Unreal” factories: Next generation of digital twins of machines and factories in the Industrial Metaverse. Manufacturing Letters. 2023. Vol. 37, p. 50-52. DOI: 10.1016/j.mfglet.2023.07.021
  22. Hosseini S., Abbasi A., Magalhaes L.G. Immersive Interaction in Digital Factory: Metaverse in Manufacturing. Procedia Computer Science. 2024. Vol. 232, p. 2310-2320. DOI: 10.1016/j.procs.2024.02.050
  23. Shankar A., Gupta R., Kumar A. et al. Exploring the adoption of Enterprise Metaverse in Business-to-Business (B2B) organisations. Industrial Marketing Management. 2025. Vol. 124, p. 224-238. DOI: 10.1016/j.indmarman.2024.11.017
  24. Kumar A., Shankar A., Behl A. et al. Implementing enterprise metaverse as a means of enhancing growth hacking performance: Will adopting the metaverse be a success in organizations? Journal of Business Research. 2025. Vol. 188. N 115079. DOI: 10.1016/j.jbusres.2024.115079
  25. Shahzad K., Ashfaq M., Zafar A.U., Basahel S. Is the future of the metaverse bleak or bright? Role of realism, facilitators, and inhibitors in metaverse adoption. Technological Forecasting and Social Change. 2024. Vol. 209. N 123768. DOI: 10.1016/j.techfore.2024.123768
  26. Salminen K., Aromaa S. Industrial metaverse – company perspectives. Procedia Computer Science. 2024. Vol. 232, p. 2108-2116. DOI: 10.1016/j.procs.2024.02.031
  27. Shufei Li, Hai-Long Xie, Pai Zheng, Lihui Wang. Industrial Metaverse: A proactive human-robot collaboration perspective. Journal of Manufacturing Systems. 2024. Vol. 76, p. 314-319. DOI: 10.1016/j.jmsy.2024.08.003
  28. Menezes C., Cunha H., Siqueira G. et al. Metaverse framework for power systems: Proposal and case study. Electric Power Systems Research. 2024. Vol. 237. N 111039. DOI: 10.1016/j.epsr.2024.111039
  29. Junlang Guo, Jiewu Leng, J. Leon Zhao et al. Industrial metaverse towards Industry 5.0: Connotation, architecture, enablers, and challenges. Journal of Manufacturing Systems. 2024. Vol. 76, p. 25-42. DOI: 10.1016/j.jmsy.2024.07.007
  30. Oliveri L.M., Lo Iacono N., Chiacchio F. et al. A Decision Support System tailored to the Maintenance Activities of Industry 5.0 Operators. IFAC-PapersOnLine. 2024. Vol. 58. Iss. 8, p. 186-191. DOI: 10.1016/j.ifacol.2024.08.118
  31. Fede G., Sgarbossa F., Paltrinieri N. Integrating production and maintenance planning in process industries using Digital Twin: A literature review. IFAC-PapersOnLine. 2024. Vol. 58. Iss. 19, p. 151-156. DOI: 10.1016/j.ifacol.2024.09.124
  32. Sai S., Sharma P., Gaur A., Chamola V. Pivotal role of digital twins in the metaverse: A review. Digital Communications and Networks. 2024. DOI: 10.1016/j.dcan.2024.12.003
  33. Brahma M., Rejula M.A., Srinivasan B. et al. Learning impact of recent ICT advances based on virtual reality IoT sensors in a metaverse environment. Measurement: Sensors. 2023. Vol. 27. N 100754. DOI: 10.1016/j.measen.2023.100754
  34. Khokhlov S., Abiev Z., Makkoev V. The Choice of Optical Flame Detectors for Automatic Explosion Containment Systems Based on the Results of Explosion Radiation Analysis of Methane- and Dust-Air Mixtures. Applied Sciences. 2022. Vol. 12. Iss. 3. N 1515. DOI: 10.3390/app12031515
  35. Romashev A.O., Nikolaeva N.V., Gatiatullin B.L. Adaptive approach formation using machine vision technology to determine the parameters of enrichment products deposition. Journal of Mining Institute. 2022. Vol. 256, p. 677-685. DOI: 10.31897/PMI.2022.77
  36. Boykov A.V., Payor V.A. Machine vision system for monitoring the process of levitation melting of non-ferrous metals. Tsvetnye Metally. 2023. N 4, p. 85-89 (in Russian). DOI: 10.17580/tsm.2023.04.11
  37. Lee P., Heepyung Kim, Zitouni M.S. et al. Trends in Smart Helmets With Multimodal Sensing for Health and Safety: Scoping Review. JMIR mHealth and uHealth. 2022. Vol. 10. N 11. N e40797. DOI: 10.2196/40797
  38. Wagner M., Leubner C., Strunk J. Mixed Reality or Simply Mobile? A Case Study on Enabling Less Skilled Workers to Perform Routine Maintenance Tasks. Procedia Computer Science. 2023. Vol. 217, p. 728-736. DOI: 10.1016/j.procs.2022.12.269
  39. Rajendran S.D., Wahab S.N., Yeap S.P. Design of a Smart Safety Vest Incorporated With Metal Detector Kits for Enhanced Personal Protection. Safety and Health at Work. 2020. Vol. 11. Iss. 4, p. 537-542. DOI: 10.1016/j.shaw.2020.06.007
  40. Abdollahi M., Quan Zhou, Wei Yuan. Smart wearable insoles in industrial environments: A systematic review. Applied Ergonomics. 2024. Vol. 118. N 104250. DOI: 10.1016/j.apergo.2024.104250
  41. Koteleva N., Valnev V. Automatic Detection of Maintenance Scenarios for Equipment and Control Systems in Industry. Applied Science. 2023. Vol. 13. Iss. 24. N 12997. DOI: 10.3390/app132412997
  42. Koteleva N., Simakov A., Korolev N. Smart Glove for Maintenance of Industrial Equipment. Sensors. 2025. Vol. 25. Iss. 3. N 722. DOI: 10.3390/s25030722
  43. Surian D., Kim V., Menon R. et al. Tracking a moving user in indoor environments using Bluetooth low energy beacons. Journal of Biomedical Informatics. 2019. Vol. 98. N 103288. DOI: 10.1016/j.jbi.2019.103288
  44. Yuhao Guo, Yicheng Li, Shaohua Wang et al. Pedestrian multi-object tracking combining appearance and spatial characteristics. Expert Systems with Applications. 2025. Vol. 272. N 126772. DOI: 10.1016/j.eswa.2025.126772
  45. Padma B., Erukala S.B. End-to-end communication protocol in IoT-enabled ZigBee network: Investigation and performance analysis. Internet of Things. 2023. Vol. 22. N 100796. DOI: 10.1016/j.iot.2023.100796
  46. Pease S.G., Conway P.P., West A.A. Hybrid ToF and RSSI real-time semantic tracking with an adaptive industrial internet of things architecture. Journal of Network and Computer Applications. 2017. Vol. 99, p. 98-109. DOI: 10.1016/j.jnca.2017.10.010
  47. Mingsen Du, Yanxuan Wei, Yupeng Hu et al. Multivariate time series classification based on fusion features. Expert Systems with Applications. 2024. Vol. 248. N 123452. DOI: 10.1016/j.eswa.2024.123452
  48. Dempster A., Petitjean F., Webb G.I. ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery. 2020. Vol. 34. Iss. 5, p. 1454-1495. DOI: 10.1007/s10618-020-00701-z
  49. Lines J., Bagnall A. Time series classification with ensembles of elastic distance measures. Data Mining and Knowledge Discovery. 2015. Vol. 29. Iss. 3, p. 565-592. DOI: 10.1007/s10618-014-0361-2
  50. Chaudhuri A., Behera R.K., Bala P.K. Factors impacting cybersecurity transformation: An Industry 5.0 perspective. Computers & Security. 2025. Vol. 150. N 104267. DOI: 10.1016/j.cose.2024.104267
  51. Azad M.A., Abdullah S., Arshad J. et al. Verify and trust: A multidimensional survey of zero-trust security in the age of IoT. Internet of Things. 2024. Vol. 27. N 101227. DOI: 10.1016/j.iot.2024.101227
  52. Itodo C., Ozer M. Multivocal literature review on zero-trust security implementation. Computers & Security. 2024. Vol. 141. N 103827. DOI: 10.1016/j.cose.2024.103827

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