APPLICATION OF THE NEURAL NETWORK APPROACH TO DIAGNOSE THE INTERNAL COMBUSTION ENGINE OF VEHICLES
Abstract
The work is devoted to the problem of diagnosing the internal combustion engine of vehicles
this problem is now the most relevant due to the constant growth of the car fleet and the tightening
of requirements for safe operation. Timely and accurate control of the internal combustion engine
is able to prevent the failure of entire vehicle assemblies, as well as to avoid such serious consequences
as a traffic accident. With the advent of modern technologies the long-known method of
engine condition estimation by sound can become the most advanced, since the human factor is
excluded, for signal processing the computer technique is applied, the analysis of a sound spectrum
in which is carried out by means of artificial neural networks. The application of artificial
neural networks for analyzing the sound spectrum has found application in speech recognition and
for diagnosing diseases of the respiratory system. The article deals with the failure of one of the
main parts of internal combustion engine - the bearing. All possible types of bearing faults and the
reasons why they occur are presented. The nodes and mechanisms of the internal combustion engine
in which bearings are used are listed. The algorithm of the experimental part is described.
The experiment which includes transformation of the received sound signals into spectrograms
and extraction of features with the help of which the classification is carried out, is executed. The
executed experimental part has proved the possibility of diagnosing of the internal combustion
engine by means of artificial neural networks. Scientific novelty lies in the fact that the diagnostic
process becomes automated, all the sounds taken by sensors are processed in a computer or in the
future in a special scanner, the display shows information about the state of certain nodes, unlike
traditional methods where the diagnosis is carried out visually or by ear. Thus, the diagnostic
accuracy increases and the overall labor intensity decreases due to the exclusion of partial or
complete engine disassembly.
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