Prediction of failures in agricultural machinery engines using digital technologies

Abstract

The paper provides information on the causes of failures in agricultural machinery engines, provides a brief overview of the ways to identify malfunctions using digital technologies introduced into the diagnostic process, and ways to eliminate them. The introduction of forecasting as a separate stage in the process of diagnosing agricultural machinery using machine learning technologies in the form of neural networks is analyzed. The results of the study reflect that the neural network, analyzing a huge amount of data obtained during remote diagnostics, is able to more accurately predict failures in agricultural machinery engines.