VIBRATION MONITORING OF INTERNAL COMBUSTION ENGINE
Abstract
The work is devoted to the problem of diagnostics of automotive internal combustion engines. The problem of monitoring the state of internal combustion engine is now most relevant due to the increase in the number of cars and the tightening of environmental requirements. In the work the consequences of operation of faulty internal combustion engine are considered. The purpose of the work is to justify the choice from existing diagnostic methods of such a method, which can help to detect the fault most accurately and quickly. For this purpose, the work details modern diagnostic tools, highlights the principles of work, advantages and disadvantages. With the advent of modern technologies, the long-known method of estimating the state of internal combustion engine by sound can become the most advanced, as the human factor is excluded, for signal processing the computational technique of analysis of the audio spectrum in which is carried out with the help of artificial neural networks is used. The use of artificial neural networks for sound spectrum analysis has found application in speech recognition and for diagnosis of respiratory system diseases. The article considers mechanisms that are capable of generating sound signals during internal combustion engine operation, some of them are phased, i.e. they are tied to operating cycles, some are not phased. The proposed diagnostic technique allows to distinguish "useful" sounds from the total number of internal combustion engine noises, after comparative analysis to point to the node the sound of which differs from the reference, serviceable one. Scientific novelty consists in the fact that the diagnostic process becomes automated, all sounds captured by sensors are processed in a computer or a special scanner, the display shows information about the condition of certain nodes, unlike traditional methods where the diagnosis is carried out visually or by ear. This increases diagnostic accuracy and reduces overall labor intensity by avoiding partial or complete engine disassembly
References
modelirovanie i raschet protsessov [Internal combustion engines: theory, modeling and calculation
of processes] Teoriya rabochikh protsessov i modelirovanie protsessov v dvigatelyakh
vnutrennego sgoraniya [Theory of working processes and modeling of processes in internal
combustion engines], 2005.
2. Shatrov M.G. Shum avtomobil'nykh dvigateley vnutrennego sgoraniya: ucheb. posobie [Noise
of automobile internal combustion engines: a textbook]. MADI, 2014.
3. Dryabzhinskiy O.E. Negativnoe vozdeystvie avtotransporta. Problema shumovogo zagryazneniya
[ Sovremennye tendentsii razvitiya nauki i tekhnologiy. – 2015. – № 8-4. – S. 91-94.
4. Chernyavskiy N.I. Laboratornyy praktikum po mezhdistsiplinarnomu kursu «Tekhnicheskoe
obsluzhivanie i remont avtomobil'nogo elektrooborudovaniya» [Laboratory workshop on the
interdisciplinary course "Maintenance and repair of automotive electrical equipment"].
Tol'yatti: Izd-vo PVGUS, 2016, 72 p.
5. Beresnev A.L., Beresnev M.A. Praktikum po laboratornym rabotam «Diagnostika DVS s
pomoshch'yu gazoanalizatora» [Workshop on laboratory work "Diagnostics of internal combustion
engines using a gas analyzer"]. Taganrog: Izd-vo TTI YuFU, 2011.
6. Beresnev A.L., Beresnev M.A., Bur'kov D.V. Praktikum po laboratornym rabotam «Diagnostika
elektrooborudovaniya DVS s pomoshch'yu motortestera». Dlya studentov spetsial'nosti
140607 [Workshop on laboratory work "Diagnostics of internal combustion engine electrical
equipment using a motor tester". For students of the specialty 140607]. Taganrog: Izd-vo TTI
YuFU, 2008.
7. Available at: http://www.adis-spb.ru/stati/417-o-motor-testerah.html (accessed 25 May 2020).
8. Bepesnev A.L., Bepesnev M.A. Vibpoakusticheskiy metod diagnostiki dvigatelya vnutrennego
sgoraniya [Vibroacoustic method of internal combustion engine diagnostics], Teoreticheskiy i
prikladnoy nauchno-tekhnicheskiy zhurnal mekhatronika, avtomatizatsiya, upravlenie [Theoretical
and applied scientific and technical journal Mechatronics, automation, control], 2010,
No. 6 (111), pp. 27-32.
9. Solov'ev D.V., Ogorodnov S.M. Gazoraspredelitel'nyy mekhanizm dvigatelya [The gas distribution
mechanism of the engine]. Nizhniy Novgorod, 2011.
10. Czech P., Lazarz B., Madej H., Wojnar G. Vibration diagnosis of car motor engines, Acta
technica corviniensis – bulletin of engineering, 2010.
11. Scheffer C., Girdhar P. Practical machinery vibration analysis and predictive maintenance,
Newnes, 2004.
12. Patel V.N., Tandon N., Pandey R. K. Hindawi publishing corporation advances in acoustics
and vibration, Experimental study for vibration behaviors of locally defective deep groove ball
bearings under dynamic radial load, 2014.
13. Burdzik R., Doleček R. Research of vibration distribution in vehicle constructive, Perner’s
contacts, 2012, pp. 16-26.
14. Wang, X. Vehicle noise and vibration refinement, woodhead publishing limited, Cambridge,
2010.
15. Deulgaonkar, V.R. Review and Diagnostics of noise and vibrations in automobiles, International
journal of modern engineering research (IJMER), Vol. 1, No. 2, pp-242-246.
16. Zheretintsev I.A., Glushkov S.V., Zheretintseva N.N. Neyrosetevaya metodika tekhnicheskoy
diagnostiki dvigateley vnutrennego sgoraniya po spektral'nomu analizu shumovykh
kharakteristik [Neural network technique of technical diagnostics of internal combustion engines
by spectral analysis of noise characteristics], Vestnik morskogo gosudarstvennogo
universiteta [Bulletin of the Maritime State University], 2010, No. 37.
17. Patrick Sincebaugh, William Green. A neural network based diagnostic test system for armored
vehicle shock absorbers expert systems with applications, 1996, Vol. 11, No. 2,
pp. 237-244.
18. Krug P.G. Neyronnye seti i neyrokomp'yutery: ucheb. posobie po kursu «Mikroprotsessory»
[Neural networks and neurocomputers: a textbook on the course "Microprocessors"]. Moscow:
Izd-vo MEI, 2002.
19. Burakov M.V. Neyronnye seti i neyrokontrollery: ucheb. posobie [Neural networks and
neurocontrollers: a textbook]. Saint Pbetersburg: GUAP, 2013.
20. Gafarov F.M. Iskusstvennye neyronnye seti i prilozheniya: ucheb. Posobie [Artificial neural
networks and applications: a textbook]. Kazan': Izd-vo Kazan. un-ta, 2018.
21. Vakulenko S.A., Zhikhareva A.A. Prakticheskiy kurs po neyronnym setyam [Practical course on
neural networks]. Saint Pbetersburg, 2018.