Optimization of Diagnostics for Units and Assemblies of Kirovets K-742M Tractors Using a Computer Program in Python

The necessity of efficient maintenance and timely repair of tractors underscores the importance of developing computer programs for diagnosing and detecting faults in their main components and assemblies. This study aimed to develop software in the Python programming language that enables the diagnosis of components and assemblies of Kirovets K-742M tractors to facilitate timely maintenance, improve operational efficiency, and shorten downtime. In developing the diagnostic program, we applied the expert evaluation method, which was integrated into the program interface created using the Tkinter library. We assumed that the program would be used on tractors equipped with CAN buses, that diagnostic data exchange protocols are compatible, that personnel performing diagnostics have experience with the software, and that operating conditions may vary. The program algorithm considers tractor information (model, year of manufacture, mileage, etc.) and gives diagnostic recommendations using the expert evaluation method. We tested the developed software at Expedition Company, where the Case IH Service Advisor software is used for diagnosing components and assemblies of Kirovets K-742M tractors. A comparison of the obtained results confirmed the hypothesis regarding the effectiveness of the developed software: the Python-based program demonstrates a 5% faster and more accurate diagnostic performance compared to foreign software. The program allows for timely fault detection and gives clear recommendations for corrective actions, thereby shortening equipment downtime and improving reliability. Further program expansion, including the addition of components, symptoms, and recommendations, along with regular tractor diagnostics, can increase operational reliability by 30%, prevent serious breakdowns, and maintain equipment in proper working condition.