Online monitoring of the technical condition of energy saturated agricultural equipment using neural networks

Abstract

The article presents a technique for continuous monitoring of the technical condition of energy-saturated agricultural machinery (SHT) using digital technologies, aimed at introducing new intelligent methods for diagnosing machines in the agro-industrial complex. It is noted that the main task of the digital monitoring system is to analyze the effective operation of equipment. The proposed neural network can continuously receive data on the technical condition of agricultural machinery in real time, analyzes and structures input values, such as engine speed, hourly fuel consumption, coolant temperature, which depend on the load and operating modes of the machine engine. The advantage of the digital method of monitoring the parameters of the technical condition is its assessment in the process of diagnosing in real time. The method allows to determine not only the cause of engine failure, but also to evaluate the efficiency of complex energy-saturated agricultural machinery in general. The developed architecture of the neural network is capable of analyzing and transmitting data obtained during the diagnostic process to a special server for storing information. The proposed method for continuous monitoring of the technical condition of complex energy-saturated equipment according to controlled parameters, based on the use of neural networks, can be quickly adapted to different brands of equipment when diagnosing.