Optimization of diagnostics of units and assemblies of Kirovets K-742M tractors using software in the Python programming language

The need for efficient maintenance and timely repair of tractors determines the importance of developing software for diagnostics and detection of faults in the main units and assemblies. The study aimed to develop software in the Python programming language to diagnose units and assemblies of Kirovets K-742M tractors for their timely maintenance, increased operating efficiency and reduced downtime. When developing the software, the author used the expert assessment method integrated into the program interface created using the Tkinter library. Assumptions were made about the software use on tractors equipped with CAN buses and the compatibility of diagnostic data exchange protocols; diagnostics by personnel experienced in using the considered software, and possible variations in the operating conditions of the equipment. The program algorithm takes into account information about the tractor (model, year of manufacture, mileage, etc.) and possible recommendations for diagnostics using the expert assessment method. The developed software was tested at Expedition Company LLC, which uses Case IH Service Advisor software to diagnose units and assemblies of Kirovets K-742M tractors. Having compared the obtained data, the author confirmed the hypothesis about the effectiveness of the developed software: diagnosing with the Phyton software is 5% faster and more accurate as compared to foreign software. The software also ensures timely detection of faults and provides clear recommendations for their elimination, which reduces equipment downtime and increases its reliability. Further expansion of the software, adding units, symptoms and recommendations, and regular diagnostics of tractors will increase its reliability by 30%, prevent serious breakdowns and maintain equipment in good condition.

1. Certificate of state registration of software No. 2024683359 Russian Federation. “Maintenance and Repair of Vehicles”: No. 2024682911: applied on October 02, 2024: issued on October 14, 2024. D.A. Moskvichev, A.S. Guzalov, A.V. Evgrafov, D.A. Filimonov; applicant – Russian State Agrarian University – Moscow Timiryazev Agricultural Academy. (In Russ.)

2. Moskvichev D.A., Khakimov R.T. Specific aspects of designing ai-based computer programs for control systems of agricultural tractors. Agroekoinzhineriya. 2024;4:29-37. (In Russ.)

3. Mityagin G.E., Moskvichev D.A., Pilshchikov V.L. Software development for organizing vehicle maintenance and repair at a motor transport enterprise. International Technical Journal. 2024;5:7-15. (In Russ.)

4. Akhtarov D.N., Shorokhov P.N. Modern systems of on-board diagnostics of tractors and cars. Ustoychivoe nauchno-tekhnicheskoe razvitie agropromyshlennogo kompleksa RF: Proceedings of the All-Russian student scientific and practical conference, Yekaterinburg, October 30, 2023. Yekaterinburg: Ural State Agrarian University, 2023. Pp. 371-374. (In Russ.)

5. Kvashin V.P., Kuzmin D.E., Tukhbatulin I.R. et al. Some ways to keep tractors and cars in working condition. Rol nauchno-issledovatelskoy raboty obuchayushchikhsya v razvitii APK: Proceedings of the III All-Russian (National) scientific and practical conference, Omsk, February 10, 2022. Omsk: Omsk State Agrarian University named after P.A. Stolypina, 2022. Pp. 287-290. (In Russ.)

6. Kolesnikov N.P., Zolototrubov V.V., Vtorov V.S. Diagnostics of John Deere tractors using the service advisor software package using the relative compression test example. Trends in the development of technical means and technologies in the agro-industrial complex: Proceedings of the International scientific and practical conference, Voronezh, January 31, 2025. Voronezh: Voronezh State Agrarian University named after Emperor Peter I, 2025. Pp. 23-27. (In Russ.)

7. Soyunov A.S., Demchuk E.V., Prokopov S.P et al. Development of a prototype device for the ecosystem of monitoring and diagnostics of tractors and self-propelled agricultural machinery. Elektronniy nauchno-metodicheskiy zhurnal Omskogo GAU. 2025;1(40). (In Russ.)

8. Tarasenko V.E., Rolich O.Ch., Yakubovich O.A., Kozlov A.V. Signal processing algorithms for multichannel integrated engines complex diagnostics for automobiles and tractors. Trudy NAMI. 2021;(1):6-15. (In Russ.) https://doi.org/10.51187/0135-3152-2021-1-6-15

9. Tabakov P.A., Agafonov A.V., Tabakov V.P. et al. Level of repairability of the MTZ tractor. The Scientific Heritage. 2021;68-1:58-62. (In Russ.)

10. Khabardin V.N. Determination of technical and economic indicators of maintenance of machines during their single-season use. Izvestiya orenburgskogo gosudarstvennogo agrarnogo universiteta. 2022;3:183-187. (In Russ.) https://doi.org/10.37670/2073-0853-2021-91-5-183-187

11. Insafuddinov S.Z., Veledov M.I., Abdrazakov F.G. Method and technology of continuous predictive diagnostics of automotive tractor and combine engines. Agrotekhnika i energoobespechenie. 2024;4:93-101. (In Russ.)

12. Kuznetsova E.V., Uspensky I.A., Yukhin I.A., Gorokhov A.A. Improving the diagnostics of tractors of the agro-industrial complex. Bulletin of the Ryazan State Agrotechnological University named after P.A. Kostychev. 2025;17(1):135-142. (In Russ.)

13. Vinogradov O.V., Moskvichev D.A., Didmanidze O.N., Parlyuk E.P. Methods of analyzing the structure of the modular car park and the intensity of its operation. Indo American Journal of Pharmaceutical Sciences. 2019;6(3):5289-5292. https://doi.org/10.5281/zenodo.2592821

14. Kumar S., Pranav P.K., Pal A. Effect of hitch distance on haulage performance for 2WD tractors: A theoretical analysis. Spanish Journal of Agricultural Research. 2020;18(2): e0203.

15. Selivanov N.I., Kuznetsov A.V., Averjanov V.V. et al. Wheeled tractors adaptation to zonal tillage technologies. JOP Conference Series: Metrological Support of Innovative Technologies. Krasnoyarsk Science and Technology City Hall of the Russian Union of Scientific and Engineering Associations. Vol. 1515. Krasnoyarsk, Russia: Institute of Physics and IOP Publishing Limited, 2020. P. 42066. https://doi.org/10.1088/1742-6596/1515/4/042066