Engineering, Energy, Transport AIC

Viktor ANISIMOV – Doctor of Technical Sciences, Professor, Department of Agricultural Engineering and Technical Service, Vinnytsia National Agrarian University (3 Sonyachna St., Vinnytsia, 21008, Ukraine; e-mail: anisimov@vsau.vin.ua; https://orcid.org/0000-0002-3349-1630). 
Vadym RYABOSHAPKA – Candidate of Technical Sciences, Associate Professor, Department of Agricultural Engineering and Technical Service, Faculty of Engineering and Technology, Vinnytsia National Agrarian University (3 Sonyachna St., Vinnytsia, 21008, Ukraine; e-mail: vadym@vsau.vin.ua; https://orcid.org/0000-0003-1812-1030). 
Ihor NISHCHAKOV – Assistant, Department of Agricultural Engineering and Technical Service, Vinnytsia National Agrarian University (3 Sonyachna St., Vinnytsia, 21008, Ukraine; e-mail: ihor956@gmail.com; https://orcid.org/0009-0005-9379-0244).
Yuliia BEZHENAR – Design Engineer, Vocational Training Master at Higher Vocational School №11 Vinnytsa, (78 Nemyrivske Highway, Vinnytsia, 21006, Ukraine; e-mail: pipkoulia057@gmail.com).

Diagnostics and forecasting of the resource is one of the most important factors in managing the efficiency and operational reliability of agricultural machinery. Increasing the requirements for the quality of technical maintenance requires improving approaches to assessing the condition of the engine, which is considered as a system of series-connected elements without redundancy. The purpose of the article is to analyze existing methods for non-disassembly diagnostics of auto-tractor diesel engines and to justify the feasibility of using the pattern recognition method for small deviations of parameters to predict the residual resource. The research methods are based on the analysis of the theory of similarity and dimensions, as well as methods of mathematical statistics and probability theory.
The laws of failure distribution (normal, Weibull, exponential, Rayleigh, gamma distribution) and their application to various types of wear are considered in detail. The research found that classical statistical methods, in particular the normal distribution law, require a large amount of testing, which complicates their use for operational forecasting. The results of the analysis allowed us to substantiate the effectiveness of the pattern recognition method, which allows us to record hidden changes in structural parameters within the tolerance range. The proposed approach allows us to reduce the amount of computational work and increase the accuracy of individual forecasting of the resource of a particular unit. 
The results of the work will be useful for engineering and technical workers of the agro-industrial complex in solving the problems of reducing the variability of the resource of engines and their systems, providing the possibility of prompt detection of pre-failure states based on small deviations of parameters for more accurate forecasting of the durability of equipment. The materials of the scientific article are based on the results of the initiative scientific topics 0122U002187 and 0122U002135

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