You are here: Home Аrchive of articles (from 2017 till now) 2026 №3 List of Article 05 Usko I. A., Dolgushin A. A., Shilkina M. V., Rakov N. V. Evaluation of AR Assistant Effectiveness for Machinery Technical Service

PDF To download article.

DOI: 10.15507/2658-4123.26363.541-560

UDK 629.3.083.4:004.9:631.256

 

Evaluation of AR Assistant Effectiveness for Machinery Technical Service

 

Ivan A. Usko
Postgraduate Student, Siberian State University Engineering and Biotechnology (160 Dobrolyubov St., Novosibirsk 630039, Russian Federation), ORCID: https://orcid.org/0009-0007-1932-0804, SPIN-код: 5563-6698, This email address is being protected from spambots. You need JavaScript enabled to view it.

Alexey A. Dolgushin
Dr.Sci. (Eng.), Associate Professor, Head of the Department of Machinery and Tractor Fleet Operation, Siberian State University Engineering and Biotechnology (160 Dobrolyubov St., Novosibirsk 630039, Russian Federation), ORCID: https://orcid.org/0000-0002-7506-6309, Scopus ID: 57212168418, SPIN-код: 1470-7343, This email address is being protected from spambots. You need JavaScript enabled to view it.

Maria V. Shilkina
Postgraduate Student, Siberian State University Engineering and Biotechnology (160 Dobrolyubov St., Novosibirsk 630039, Russian Federation), ORCID: https://orcid.org/0009-0007-5007-0831, This email address is being protected from spambots. You need JavaScript enabled to view it.

Nikolay V. Rakov
Cand.Sci. (Eng.), Associate Professor, National Research Mordovia State University (68 Bolshevistskaya St., Saransk 430005, Russian Federation), ORCID: https://orcid.org/0000-0003-3687-9371, SPIN-код: 5593-8618, This email address is being protected from spambots. You need JavaScript enabled to view it.

 

Abstract
Introduction. The increasing complexity of modern agricultural machinery design options, coupled with a systemic shortage of qualified engineering personnel, has led to greater routine maintenance labor input and increased risk of errors during maintenance. Up to 15–25% of working time an operator wastes to search reference information in technical documentation.
Aim of the Study. The study is aimed at improving the efficiency of maintenance of agricultural machines by developing and implementing the hardware-software complex “Digital Engineering Assistant” based on augmented reality (AR) technology.
Materials and Methods. The object of the study was the daily maintenance process for the John Deere 6125M tractor. The hardware component of the system was implemented using industrial AR glasses RealWear HMT-1, while the software was developed in Kotlin with a local database ensuring autonomous operation. There was conducted a single-factor comparative experiment involving 50 novice operators (control group: traditional documentation, n = 25; experimental group: AR assistant, n = 25). The time of performing operations was measured by continuous timing.
Results. The was determined a statistically highly significant reduction in total maintenance time by 24.6% – from 820.6 ± 123.8 s to 618.6 ± 94.8 s. Cost breakdown revealed that 45.5% of the savings resulted from a reduction in information search time. The absolute variability of the results decreased by 23.4%.
Conclusion. AR technology effectively compensates for the lack of practical experience of novice specialists through standardizing the maintenance process. The annual economic benefit is 5.61 person-hours per a unit of equipment. The prospects are determined by applying the proposed technology for complex maintenance and repair operations.

Keywords: technical service, augmented reality (AR), digitalization of the agro-industrial complex, operation of agricultural machinery, labor input of operations

Conflict of interest: The authors declare that there is no conflict of interest.

For citation: Usko I.A., Dolgushin A.A., Shilkina M.V., Rakov N.V. Evaluation of AR Assistant Effectiveness for Machinery Technical Service. Engineering Technologies and Systems. 2026;36(3):541–560. https://doi.org/10.15507/2658-4123.26363.541-560

Authors contribution:
I. A. Usko – ideas; formulation or evolution of overarching research goals and aims; conducting a research and investigation process, specifically performing the experiments, or data/evidence collection; programming, software development; designing computer programs; implementation of the computer code and supporting algorithms; testing of existing code components; preparation, creation and/or presentation of the published work, specifically writing the initial draft (including substantive translation).
A. A. Dolgushin – oversight and leadership responsibility for the research activity planning and execution, including mentorship external to the core team; management and coordination responsibility for the research activity planning and execution; preparation, creation and/or presentation of the published work by those from the original research group, specifically critical review, commentary or revision – including pre- or post-publication stages.
M. V. Shilkina – application of statistical, mathematical, computational, or other formal techniques to analyse or synthesize study data.
N. V. Rakov – management activities to annotate (produce metadata), scrub data and maintain research data (including software code, where it is necessary for interpreting the data itself) for initial use and later re-use.

All authors have read and approved the final manuscript.

Submitted 11.10.2025;
revised 12.11.2026;
accepted 10.03.2026

 

REFERENCES

  1. Dorokhov A.S. [Efficiency of Quality Assessment of Agricultural Machinery and Spare Parts]. Agricultural Engineering. 2015;(1):31–35. (In Russ.) https://elibrary.ru/tyjhrn
  2. Zhuravlev S.Yu. Modern Concept of Organization of Technical Service of Machines in the Agro-Industrial Complex. Izvestia Orenburg State Agrarian University. 2021;(3):119–125. (In Russ., abstract in Eng.) Available at: https://orensau.ru/ru/obuniversitete/12/4781-izvestiya-orenburgskogogosudarstvennogo-agrarnogo-universiteta (accessed 03.11.2025).
  3. Khodabakhshian R. Maintenance Management of Tractors and Agricultural Machinery: Preventive Maintenance Systems. CIGR Journal. 2013;15(4):147–159. Available at: https://cigrjournal.org/index.php/Ejounral/article/view/2616 (accessed 03.11.2025).
  4. Tsarev Yu.A., Tsarev O.Yu. Method of Increasing the Operational Reliability of Complex Agricultural Machines. Polythematic Online Scientific Journal of Kuban State Agrarian University. 2023;(189):153–163. (In Russ., abstract in Eng.) Available at: http://ej.kubagro.ru/archive.asp?y=2023 (accessed 04.12.2025).
  5. Nikitchenko S.L., Lesnik N.A., Smykov S.V. Management of Agricultural Machinery Maintenance Under the Automationand Digitalization of Productio. Agricultural Engineering. 2023;25(4):33–42. (In Russ., abstract in Eng.) https://doi.org/10.26897/2687-1149-2023-4-33-42
  6. Goncharov R.D., Isaev A.D. Application of Interactive Technologies for Technical Support of Agricultural Machinery Maintenance. AgroEcoEngineering. 2025;(1):145–158. (In Russ., abstract in Eng.) https://doi.org/10.24412/2713-2641-2025-1122-145-158
  7. Malta A., Farinha T., Mendes M. Augmented Reality in Maintenance–History and Perspectives. Journal of Imaging. 2023;9(7):142. https://doi.org/10.3390/jimaging9070142
  8. Palmarini R., Erkoyuncu J.A., Roy R., Torabmostaedi H. A Systematic Review of Augmented Reality Applications in Maintenance. Robotics and Computer-Integrated Manufacturing. 2018;(49):215–228. https://doi.org/10.1016/j.rcim.2017.06.002
  9. Yazdi M. Augmented Reality (AR) and Virtual Reality (VR) in Maintenance Training. Advances in Computational Mathematics for Industrial System Reliability and Maintainability. 2024:169–183. https://doi.org/10.1007/978-3-031-53514-7_10
  10. Bottani E., Vignali G. Augmented Reality Technology in the Manufacturing Industry: A Review of the Last Decade. Iise Transactions. 2019;51(3):284–310. https://doi.org/10.1080/24725854.2018.1493244
  11. Egger J., Masood T. Augmented Reality in Support of Intelligent Manufacturing – A Systematic Literature Review. Computers & Industrial Engineering. 2020;(140):106195. https://doi.org/10.1016/j.cie.2019.106195
  12. Mourtzis D., Siatras V., Angelopoulos J. Real-Time Remote Maintenance Support Based on Augmented Reality (AR). Applied Sciences. 2020;10(5):1855. https://doi.org/10.3390/app10051855
  13. Gattullo M., Scurati G.W., Fiorentino M., Uva A.E., Ferrise F., Bordegoni M. Towards Augmented Reality Manuals for Industry 4.0: A Methodology. Robotics and Computer-Integrated Manufacturing. 2019;(56):276–286. https://doi.org/10.1016/j.rcim.2018.10.001
  14. Kollatsch C., Klimant P. Efficient Integration Process of Production Data Into Augmented Reality Based Maintenance of Machine Tools. Production Engineering. 2021;(15):311–319. https://doi.org/10.1007/s11740-021-01026-6
  15. Blattgerste J., Strenge B., Renner P., Pfeiffer T., Essig K. Comparing Conventional and Augmented Reality Instructions for Manual Assembly Tasks. In: Proceedings of the 10th International Conference on Pervasive Technologies Related to Assistive Environments. Corfu:ACM; 2017. pp. 75–82. https://doi.org/10.1145/3056540.3056547
  16. Masoni R., Ferrise F., Bordegoni M., Gattullo M., Uva A.E., Fiorentino M., et al. Supporting Remote Maintenance in Industry 4.0 through Augmented Reality. Procedia Manufacturing. 2017;(11):1296–1302. https://doi.org/10.1016/j.promfg.2017.07.257
  17. Lohan S.K., Prakash C., Lohan N., Kansal S., Karkee M. State-of-the-Art in Real-Time Virtual Interfaces for Tractors and Farm Machines: A Systematic Review. Computers and Electronics in Agriculture. 2025;(231):109947. https://doi.org/10.1016/j.compag.2025.109947
  18. Phupattanasilp P., Tong S.-R. Augmented Reality in the Integrative Internet of Things (AR-IoT): Application for Precision Farming. Sustainability. 2019;11(9):2658. https://doi.org/10.3390/su11092658
  19. Solovyov D.А., Chumakova S.V., Goncharov R.D., Chumakov R.V. Justification for the Implementation of Ar-Technologies in the Maintenance of Agricultural Machinery in the Agro-Industrial Complex. Niva Povolzhyia. 2022;(3):3006. (In Russ., abstract in Eng.) https://doi.org/10.36461/NP.2022.63.3.020
  20. Goncharov R.D., Isaev A.D., Rossoshansky S.D., Ruzhev V.A., Shakhov V.A. Development of a Digital Platform for Training in Working with Agricultural Machinery. Izvestia Orenburg State Agrarian University. 2025;(5):148–154. (In Russ., abstract in Eng.) Available at: https://orensau.ru/ru/obuniversitete/12/4781-izvestiya-orenburgskogo-gosudarstvennogo-agrarnogo-universiteta (accessed 03.11.2025).
  21. Melzer J.E. A Survey of Augmented and Virtual Reality Wearable Displays in Medical and Surgical Applications: Architectures, Requirements, Performance, and Benefits. In: Optical Architectures for Displays and Sensing in Augmented, Virtual, and Mixed Reality (AR, VR, MR) VII: Conference Proceedings. 2026. pp. 155–170. https://doi.org/10.1117/12.3081439
  22. Röltgen D., Dumitrescu R. Classification of Industrial Augmented Reality Use Cases. Procedia CIRP. 2020;(91):93–100. https://doi.org/10.1016/j.procir.2020.01.137
  23. Fraga-Lamas P., FernáNdez-CaraméS T.M., Blanco-Novoa ÓS., Vilar-Montesinos M.A. A Review on Industrial Augmented Reality Systems for the Industry 4.0 Shipyard. Ieee Access. 2018;(6):13358–13375. https://doi.org/10.1109/ACCESS.2018.2808326
  24. Dey A., Billinghurst M., Lindeman R.W., Swan II J.E. A Systematic Review of 10 Years of Augmented Reality Usability Studies: 2005 to 2014. Frontiers in Robotics and AI. 2018;(5):37. https://doi.org/10.3389/frobt.2018.00037

 

Licensed under a Creative Commons
This work is licensed under a Creative Commons Attribution 4.0 License.

Details
Hits: 3