Application of an artificial neural network for determination of the cylinder-piston group wear level of the automobile-tractor engine

Abstract

The article proposes reducing the redundancy of the neural network and the need to reduce the number of neurons in the hidden layer for a given level of network learning error. The minimum number of neurons of the hidden layer for the case of 11 monitoring standard sensors, the parameters of the automobile and tractor engine (ATE) and five classes of typical defects of the ATE nodes can be reduced to 5-7 with a high quality of recognition of the state of the ATE engine. The goal is to provide an expanded reliable knowledge base, the speed of information processing, the accuracy of the resulting technical diagnosis and the ability to quickly determine the technical state of an automotive engine in the mode real time. The basis of the proposed method is to ensure obtaining an extended reliable knowledge base, the speed of information processing, the accuracy of the obtained technical diagnosis and the ability to quickly determine the technical state of an ATE engine in real time.
A feature of the proposed method is the use of voltages obtained in an artificial neural network from sensors that are standard in an ATE engine as input signals, and additionally indicate the output signal of the fuel cut-off device, provided for one step, containing a winding of a normally closed electromagnetic valve, which redirects fuel to the drain line. The use of the algorithm for identifying the values of the indicators of operating modes and malfunctions of the cylinder-piston group is the result of the analysis of an artificial neural network, which receive the results of the diagnostic parameters of the automotive engine. Having studied the artificial neural network 1 with different volumes of training data, we obtained the dependence of the change in the reliability of the result on the size of the training data and the reliability of the recognition result is 91.2%, the optimal amount of training data is 1200. Having examined the artificial neural network 2 with different volumes of training data, we obtained the dependence of the change in the reliability the result from the size of the training data and the reliability of the recognition result is 86.5%, the optimal amount of training data is from 10 to 15. The results obtained show the fundamental possibility of creating predictive models of units and assemblies of the tested automotive engines. The model can be created using the apparatus of artificial neural networks and using a fairly large database of tests performed.