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 itsreliability. Further expansion of the software, adding units, symptoms and recommendations, and regular diagnostics
DMITRY A. MOSKVICHEV (Wed,) studied this question.