https://doi.org/10.4081/jae.2021.1158
Diagnostic method and device for evaluating and forecasting the technical condition of farm machinery in operation
Published: 23 December 2021
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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The paper discusses methods and ways to diagnose the technical condition of agricultural machines and harvesters, existing practices, and approaches to get reliable data on the current health of the machinery used. The device for assessing and predicting machines’ technical condition includes software and technical means developed with virtual technologies to measure diagnostic parameters of the machinery. The main device elements are digital sensors with physical modifiers (pressure, temperature, medium composition and motion sensors, a-d converters with signal amplifiers), software to configure data gathering, and output to conduct analyses and produce recommendations. The core of the present approach is the technology of virtual prediction of breakdowns by changes in the technical condition parameters. It is based on modular devices, software with an interface that collects and processes data and provides a complete set of failure diagnostics and forecasting. The given method based on a device operating in the information and communication network increases farm machinery’s performance. Furthermore, it reduces operating costs due to the prevention of expensive breakdowns, individual forecasting, and scheduled maintenance of machines in operation. The approach under consideration was applied in the laboratory of digital engineering technologies of the Bashkir State Agrarian University Republic of Bashkortostan of the Russian Federation. The given work is aimed to boost the efficiency of the farm machinery diagnostics and maintenance system by applying a virtual breakdown prediction technology to conduct an automated evaluation, registration, and analysis of a machine’s condition. It can be achieved by developing software and technical means to register data and their structure systematization.
Azzoni P.M., Minelli G., Moro D., Flora R., Serra G. 1999. Indicated and load torque estimation using crankshaft angular velocity measurement. SAE Int. J. Engine. 108:752-8.
DOI: https://doi.org/10.4271/1999-01-0543
Ball J.K., Bowe M.J., Stone C.R., McFadden P.D. 2000. Torque estimation and misfire detection using block angular acceleration. SAE Int. J. Engine.109:622-43.
DOI: https://doi.org/10.4271/2000-01-0560
Bashirov R.M., Insafuddinov S.Z. 2012. Pa tent № 2429373 rus. Sta nd for testing a nd a djustment of diesel fuel equipment. Filed. 12.12.2011. Pub. 24.02.2012.
Bosch, n.d. Bosch mobility solutions. Available from: https://www.bosch-mobility-solutions.com/en/ Accessed: 16 November 2020.
Buklagin D.S. 2016. Development of agricultural machinery testing technologies. Int. Res. J. 4:45-51.
Bulat M.P., Bulat P.V., Denissenko P.V., Esakov I.I., Grachev L.P., Volkov K.N., Volobuev I.A. 2018. Ignition of lean and stoichiometric air-propane mixture with a subcritical microwave streamer discharge. Acta Astronaut. 150:153-61.
DOI: https://doi.org/10.1016/j.actaastro.2017.11.030
Bulat P.V., Zasukhin O.N., Upyrev V.V., Silnikov M.V., Chernyshov M.V. 2017. Base pressure oscillations and safety of load launching into orbit. Acta Astronaut. 135:150-60.
DOI: https://doi.org/10.1016/j.actaastro.2016.11.042
Cavallo E., Ferrari E., Coccia M. 2015. Likely technologica l tra jectories in a gricultura l tra ctors by a na lysing innova tive a ttitudes of fa rmers. Int. J. Technol. Policy Ma na g. 15:158-77.
DOI: https://doi.org/10.1504/IJTPM.2015.069203
Chernoivanov V.I., Gabitov I.I., Negovora A.V. 2017. Digital technologies and electronic devices in the system of technical service and repair of cars, tractors, and combine-harvesters. In: Proceedings of the scientific conference, ‘Smart machine technologies and machinery in agriculture.’ Russian Science Academy, Federal Scientific Agroengineering center VIM, Moscow.
Dorokhov A.S. 2010. Monitoring the technical state of the agricultural machinery. Farm Mechan. Electr. 2:23-6.
Espadafor F.J.J., Villanueva J.A.B., Guerrero D.P., García M.T., Trujillo E.C., Vacas F.F. 2014. Measurement and analysis of instantaneous torque and angular velocity variations of a low speed two stroke diesel engine. Mech. Syst. Signal Process 49:135-53.
DOI: https://doi.org/10.1016/j.ymssp.2014.04.016
Fisinin V.I., Y Lachuga F., Zhuchenko A.A., Ivanov A.L., Ushachev I.G., Ezhevsky A.A., Krasnoshchekov N.V., Chernoivanov V.I., Izmailov A.Y., Strebkov D.S., Popov V.D., Lipkovich E.I., Voitovich N.V., Kiryushin V.I., Artyushin A.A., Gorbachev I.V., Fedorenko V.F., Taranov M.A., Kryazhkov V.M., Elizarov V.P., Orsik L.S., Rakhmanov A.A., Chekmarev P.A., Shapochkin V.A., Sorokin N.T., 2009. Strategy to upgrade agricultural machinery and equipment in Russia for the period up to 2020. Federal State Scientific Institution ‘‘Rosinformágrotekh’, Moscow.
Gabitov I.I., Negovora A.V., Khasanov E.R., Galiullin R.R., Farhshatov M.N., Khamaletdinov R.R., Martynov V., Gusev D., Yunusbaev N., Razyapov M.M. 2019. Risk reduction of thermal damages of units in machinery heat preparation for load acceptance. J. Eng. Appl. Sci. 14:709-16.
DOI: https://doi.org/10.36478/jeasci.2019.709.716
Insafuddinov S.Z., Safin F.R., Gaisin E.M., Davletov A.F. 2013. Diesel diagnostics when injected into a back-pressure environment. Registration certificate for computer program EVM RUS 2014611323 10.12.2013.
Johnson B., Troy M. 2009. Engine, ba ttery a nd vehicle simula tion stra tegies for tra nsmission testing. In: Proceedings of the 2009 Ground Vehicle Systems Engineering a nd Technology Symposium (GVSETS).
Johnston P.R., Shusto L.M. 1987. Analysis of diesel engine crankshaft torsional vibrations (No. 872540). SAE Technical Paper.
DOI: https://doi.org/10.4271/872540
Malaczynski G.W., Van der Poel R. 2010. Phase diagrams of different modes of misfire calculated from the digital fourier transformation of angular crankshaft velocity (No. 2010-01-0167). SAE Technical Paper.
DOI: https://doi.org/10.4271/2010-01-0167
Mamala J., Brol S., Bieniek A. 2011. The System for estimation parameters of internal combustion engine in the road test. J. KONES 18:279-86.
Mudarisov S., Khasanov E., Rakhimov Z., Gabitov I., Badretdinov I., Farchutdinov I., Gallyamov F., Davletshin M., Aipov R., Jarullin R.J. 2017. Specifying two-pha se flow in modeling pneuma tic systems performa nce of fa rm ma chines. J. Mech. Eng. Res. Dev 40:706-15.
Presidential Decree of the Russian Federation, 2020. The Russian Food security doctrine (approved by the Presidential Decree of the Russian Federation No. 20, January 21, 2020).
Raikwar S., Wa ni L.J., Kuma r SA., Rao M.S. 2019. Ha rdwa re-in-the-Loop test a utoma tion of embedded systems for a gricultura l tra ctors. Mea surement 133:271-80.
DOI: https://doi.org/10.1016/j.measurement.2018.10.014
Rizzoni G. 1989. Diagnosis of individual cylinder misfires by signature analysis of crankshaft speed fluctuations. SAE Int. J. Engines 98:1572-81.
DOI: https://doi.org/10.4271/890884
Schmidt M., Kimmich F., Straky H., Isermann R. 2000. Combustion supervision by evaluating the crankshaft speed and acceleration (No. 2000-01-0558). SAE Technical Paper.
DOI: https://doi.org/10.4271/2000-01-0558
Staszak Ż., Selech J., Marcinkiewicz J., Romek D., Włodarczyk K., Gierz Ł., Wojciesza k D. 2018. The dia gnostic informa tion va lua tion method in servicing tra ctors. In: MA TEC Web of Conferences (Vol. 182, p. 01013). EDP Sciences.
DOI: https://doi.org/10.1051/matecconf/201818201013
Subasinghe L.U., Mendis D., Chandima T., Jayaweera N., De Silva S. 2015. A mathematical model for simulating instantaneous angular speed variation of a crankshaft of an automobile engine (No. 2015-01-0209). SAE Technical Paper.
Taglialatela F., Cesario N., Lavorgna M., Merola S.S., Vaglieco B.M. 2009. Use of engine crankshaft speed for determination of cylinder pressure parameters (No. 2009-24-0108). SAE Technical Paper.
DOI: https://doi.org/10.4271/2009-24-0108
Taraza D. 1993. Possibilities to reconstruct indicator diagrams by analysis of the angular motion of the crankshaft (No. 932414). SAE Technical Paper.
DOI: https://doi.org/10.4271/932414
Williams J. 1996. An overview of misfiring cylinder engine diagnostic techniques based on crankshaft angular velocity measurements (No. 960039). SAE Technical Paper.
DOI: https://doi.org/10.4271/960039
Williams J., Witter M.C. 2001. Individual cylinder IMEP estimation using crankshaft angular velocity measurements (No. 2001-01-0990). SAE Technical Paper.
DOI: https://doi.org/10.4271/2001-01-0990
Zangiev A.A., Didmanidze O.N., Mitiagin G.E., 2001. Increasing the efficiency of the service centers for machine and technological stations. Agrokonsalt Publ., Moscow.
Zhang Y.G., Su T.X., Li W.H., Mao H.P., Zhou N. 2013. Dynamic analysis of diesel engine piston based on time spectral element method. In: Applied mechanics and materials, Vol. 415. Trans Tech Publications Ltd., pp. 565-568.
DOI: https://doi.org/10.4028/www.scientific.net/AMM.415.565
How to Cite
Gabitov, I. (2021) “Diagnostic method and device for evaluating and forecasting the technical condition of farm machinery in operation”, Journal of Agricultural Engineering, 52(4). doi: 10.4081/jae.2021.1158.
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