Use of neural networks in the technical operation of self-propelled machinery

The actual service life of tractor engines of traction class 1.4 in the Tomsk region falls significantly short of the warranty period and exhibits high dispersion. Statistical analysis reveals that the mean operating time until the first overhaul does not exceed 7,000 engine hours, with a standard deviation of 1,707 hours and a coefficient of variation of 0.24. The service life of new engines until the first overhaul varies by more than a factor of 2.8. To address the challenge of predicting failure modes based on cumulative operating time, this study employs artificial neural networks (ANNs). The research objective was to train an ANN to identify the most likely cause of engine failures using durability data collected under routine operating conditions of traction class 1.4 tractor engines. The authors developed an intelligent failure diagnostics system using Python and the PyTorch framework. The Matplotlib module was used for visualization, NumPy for matrix operations, and sklearn for input data normalization. The ANN uses a fully connected (dense) architecture consisting of an input layer (one neuron), a hidden layer (10 neurons), and an output layer (four neurons). The model was trained on a dataset from 25 Minsk Motor Plant engines (type 4Ch(N) 11/12.5). Based on the “operating time” input parameter, the model generates a probability distribution across four failure categories: the crank mechanism, the lubrication system, the fuel system, and the cooling system. Initial testing yielded a prediction accuracy of 60%. Future research will focus on fine-tuning the artificial neural network by expanding the training dataset to achieve a target accuracy of 80%.

1. Dokin B.D. The efficient application of agricultural machinery in grain production in Sibiria. Machinery and Equipment for Rural Area. 2009;8:7-11. (In Russ.)

2. Shukhanov S.N., Kuzmin A.V., Boloyev P.A. Reliability of Machine-Tractor Aggregates Operation. Inzhenerernyye tekhnologii i sistemy = Engineering Technologies and Systems. 2020;30(1):8-20. (In Russ.) https://doi.org/10.15507/2658-4123.030.202001.008-020

3. Pirozhkov D.N., Beljaev V.I., Zavora V.A. The fundamentals of theoretical substantiation of technical equipment of crop production at an agricultural enterprise. Vestnik Altayskogo gosudarstvennogo agrarnogo universiteta. 2016;3:166-169. (In Russ.)

4. Starcev A.V., T.E., Alushkin S.V. Romanov, Storozhev I.I. A model for determining the operating costs of machine-tractor units for sowing, taking into account the duration of work and the size of the area. Tractors and Agricultural Machinery. 2020;1:82-87. (In Russ.)

5. Soloviev R.Yu. Goryachev, S.A. Resource saving in the maintenance of agricultural machinery. Agricultural Machinery and Technologies. 2013;2:37-49. (In Russ.)

6. Karagodin V.I., Khapugin R.A. Rationale and frequency of the diagnosis of component parts of tractors Belarus in the development of technology maintenance. IOP Conference Series. Materials Science and Engineering. 2020;832(1):1-8. https://doi.org/10.1088/1757-899X/832/1/012005

7. Ivanov V.V., Dotsenko S.A., Sedov A.V., Nikolaev A.P. The reliability of systems and components for tractors MTZ of the family and the distribution of failure intervals, developments. Perm Agrarian Journal. 2017;4(20):30-35. (In Russ.)

8. Pourdarbani R. Choosing a proper maintenance and repair strategy for tractors (in Urmia). Acta Technologica Agriculturae. 2019;22(1):12-16. https://doi.org/10.2478/ata-2019-0003

9. Alt V.V., Chekusov M.S., Isakova S.P. Digital technologies in crop production. Technical Crops. Scientific Agricultural Journal. 2023;3(2):46-53. (In Russ.)

10. Alt V.V., Olshevskyi S.N. Information technologies in the innovation development of agro-industrial complex. Achievements of Science and Technology in Agro-Industrial Complex. 2016;30(4):67-69. (In Russ.)

11. Shimokhin A.V. Methodology for determining the optimal terms of transport repair using neural network technology. Vestnik Omskogo gosudarstvennogo agrarnogo universiteta. 2023;2(50):153-160. (In Russ.)

12. Shimokhin A.V., Kirasirov O.M. The procedure for improving the management of the maintenance and repair process using the neural network technology. Tractors and Agricultural Machinery. 2023;90(6):561-573. (In Russ.)