This article presents an analysis of the composition of the machine and tractor fleet of enterprises by model and age. Modern systems for monitoring the operation of combines - AGROTRONIK - are considered. The parameters that are controlled by this system are determined. A system for processing these parameters based on the use of a neural network is proposed. A block diagram of the monitoring system. It is noted that in order to increase the efficiency of the system, it is proposed to use the analysis of data received from vibration of rotating and mating units. Vibration frequency ranges have been determined for defects in belt drives in a combine harvester. The analysis of vibration frequencies as signs of defects has shown that mainly defects in these transmissions of the Acros -530 combine are determined in the frequency range from 1 to 42 Hz. Thus, wear of the driving pulleys mainly appears at vibration frequencies from 8 to 52 Hz, wear of the driven pulleys at vibration frequencies from 2 to 27 Hz, and belt defects at vibration frequencies from 1 to 22 Hz. The largest number of defects in belt drives of the Akros combine are manifested by vibration in the frequency range from 2 to 22 Hz. The analysis made it possible to construct a diagnostic map based on vibration frequencies. Which can be used to diagnose the technical condition of belt and chain drives of the Acros-530 combine using a neural network. The use of such a system creates the opportunity to use a technical condition monitoring system, due to their ability to classify a specific defect by analyzing the image of vibration spectrograms, in the absence of a diagnostic expert.
technical service, neural networks, belt drives, vibration diagnostics
1. Redreev G.V., Boltovskiy S.N. Ispol'zovanie diagnosticheskoy informacii dlya povysheniya effektivnosti ekspluatacii zernouborochnyh kombaynov // Vestnik Omskogo gosudarstvennogo agrarnogo universiteta. 2018. № 4(32). S. 85-92. EDN: https://elibrary.ru/PLQSRW
2. Osobennosti podgotovki tehniki k dlitel'nomu hraneniyu v Omskoy oblasti / E.Yu. Miroshnik, D.N. Vinogradov, Ya.D. Vysockiy [i dr.] // Rol' nauchno-issledovatel'skoy raboty obuchayuschihsya v razvitii APK: sb. IV Vseros. (nac.) nauch.-prakt. konf., Omsk, 28 fevralya 2023 goda. Omsk: Omskiy gosudarstvennyy agrarnyy universitet imeni P.A. Stolypina, 2023. S. 205-208. EDN: https://elibrary.ru/VESXLY
3. Gerike P.B., Gerike B.L. Modelirovanie degradacii fakticheskogo sostoyaniya podshipnikov na osnove ispol'zovaniya edinogo diagnosticheskogo kriteriya // Gornaya promyshlennost'. 2023. № S2. S. 32-36. DOI:https://doi.org/10.30686/1609-9192-2023-S2-32-36 EDN: https://elibrary.ru/ICAWAX
4. Shimohin A.V. Sovershenstvovanie organizacii remontnoy sluzhby predpriyatiy mashinostroeniya // Vestnik Sibirskoy gosudarstvennoy avtomobil'no-dorozhnoy akademii. 2017. № 1(53). S. 190-197. EDN: https://elibrary.ru/VZSPNL
5. Shamal' M.A. Diagnostika tehnicheskogo sostoyaniya elektricheskih mashin glavnyh privodov moschnyh ekskavatorov na osnove neyronnyh setey // Izvestiya vysshih uchebnyh zavedeniy. Gornyy zhurnal. 2005. № 2. S. 44-46. EDN: https://elibrary.ru/JUUAQJ
6. Luk'yanov A.V., Kaimov E.V. Povyshenie nadezhnosti vspomogatel'nyh mashin elektrovozov na osnove ispol'zovaniya dannyh vibrodiagnostiki // Sovremennye tehnologii. Sistemnyy analiz. Modelirovanie. 2023. № 2(78). S. 115-126. DOI:https://doi.org/10.26731/1813-9108.2023.2(78).115-126 EDN: https://elibrary.ru/NHGATF
7. Dergachev K.A. Vibrodiagnostika promyshlennogo oborudovaniya pri pomoschi iskusctvennogo intellekta // Dinamika vzaimootnosheniy razlichnyh oblastey nauki v sovremennyh usloviyah: sb. st. Mezhdunar. nauch.-prakt. konf., Novosibirsk, 29 maya 2018 goda. Ch. 1. Novosibirsk: OMEGA SAYNS, 2018. S. 19-21. EDN: https://elibrary.ru/XNPVSH
8. Shimohin A.V. Metodika opredeleniya optimal'nyh srokov remonta transporta s ispol'zovaniem tehnologii neyronnyh setey // Vestnik Omskogo gosudarstvennogo agrarnogo universiteta. 2023. № 2(50). S. 153-160. EDN: https://elibrary.ru/YYOPXG
9. Pomogaev V.M., Redreev G.V. Informacionnoe obespechenie v sisteme tehnicheskogo obsluzhivaniya i remonta mobil'nyh mashin v sel'skom hozyaystve // Vestnik OmGAU. 2022. № 2(46). S. 145-152. EDN: https://elibrary.ru/IPRTCH
10. Karakulov I.V. Klassifikaciya tehnicheskogo sostoyaniya vodonasosnogo oborudovaniya pri pomoschi svertochnyh neyronnyh setey // Prikladnaya matematika i voprosy upravleniya. 2022. № 2. S. 37-53. DOI:https://doi.org/10.15593/2499-9873/2022.2.02 EDN: https://elibrary.ru/LIOBNY
11. Rogachev A.F., Korotkov A.A., D'yakova N.V. Prognozirovanie urozhaynosti zernovyh kul'tur s primeneniem iskusstvennyh neyronnyh setey // Ispol'zovanie innovacionnyh tehnologiy dlya resheniya problem APK v sovremennyh usloviyah: sb. materialov Mezhdunar. nauch.-prakt. konf., posvyasch. 65-letiyu obrazovaniya Volgogradskoy gosudarstvennoy sel'skohozyaystvennoy akademii. Volgograd. 2009. S. 254-259. EDN: https://elibrary.ru/XDAQCF
