VIBRATION MONITORING OF INTERNAL COMBUSTION ENGINE

Abstract

The work is devoted to the problem of diagnosing automobile internal combustion engines. The problem of controlling the condition of internal combustion engines is now the most relevant because of the increasing number of cars and tightening of environmental requirements. The paper considers the consequences of a faulty internal combustion engine. The aim of the work is to develop a method that can help to detect the fault most accurately and quickly. With the advent of modern technologies the long known method of internal combustion engines condition estimation by the sound can become the most advanced, as the human factor is excluded, for signal processing the computer techniques of the sound spectrum analysis which is carried out by means of artificial neural networks are applied. The application of artificial neural networks for analyzing the sound spectrum has found application in speech recognition and for diagnosing respiratory diseases. In the article the mechanisms which are capable to generate sound signals during internal combustion engine work are considered, some of them are phased i.e. are connected to working strokes, some of them are not phased. The proposed method of diagnostics allows selecting "useful" sounds from the total number of engine noises and, after a comparative analysis, pointing to a node whose sound differs from the reference, serviceable one. Scientific novelty lies in the fact that the diagnostic process becomes automated, all the sounds recorded by sensors are processed in a computer or a special scanner, the display shows information about the condition of certain nodes, in contrast to traditional methods where the diagnosis is carried out visually or by ear. This increases diagnostic accuracy and reduces overall labor intensity by eliminating partial or complete engine disassembly