Development and Application of Neural Networks Technology for Diagnostics of the Technical Condition of Automobile and Tractor Engines

Abstract

The article proposes the use of neural network technology to solve the problems of diagnosing the automobile and tractor engines technical conditions. This allow to work with real data obtained for an individual and reference (average) engine, as well as with data calculated using the mathematical model, based on the comparison of which researcher can take informed decisions about the nature and location of a particular defect. This improves the functional stability of the wheeled vehicle and improves the performance of its intelligent on-board systems. It has been proven that in order to reduce the redundancy of a neural network, it is necessary to reduce the number of neurons in the hidden layer for a given level of network training error. For diagnosing the automobile or tractor engine, the input signals are voltages received in an artificial neural network from sensors that are standard in the engine, and additionally indicate the technical conditions.