Technology, energy, agriculture transport AIC

Veselovska Nataliya – PhD, Professor of the Department "Machinery and Equipment of Agricultural Production" of the Vinnytsia National Agrarian University (3, Solnychna str., Vinnytsia, Ukraine, 21008, e-mail: wnatalia@ukr.net).
O. Yalina – post-graduate student of the Department "Machinery and Equipment of Agricultural Production" of Vinnytsia National Agrarian University (3 Sonyachna Street, Vinnytsia, 21008, Ukraine.
Yanishevskiy Vasyliy – Yanishevsky Vasyl Yuriyovych - post-graduate student of the Department "Machinery and Equipment of Agricultural Production" of Vinnytsia National Agrarian University (3 Sonyachna Street, Vinnytsia, 21008, Ukraine.

Intensive use of agricultural production technologies has recognized the intensive characteristics of agricultural producers, which allowed to present the need for frequent diagnostics, maintenance and repair.
This transition is due to objective trends in the development of agricultural machinery. The solution to this problem involves the development and creation of effective tools for monitoring and diagnosing the state of the transmission elements of agricultural machinery; assessment of the value of the residual life of individual elements of the system and the study of energy conversion processes in these elements; construction of mathematical models on the basis of which it is possible to predict the occurrence of dangerous damage depending on the operating conditions of the equipment. Diagnostic means must ensure the assessment of the condition of the working surfaces of the interacting transmission elements, control of wear of their surfaces and assessment of the performance of the unit as a whole or the kinematic pair without their disassembly during operation. The time of collection of diagnostic information and allocation of informative signs and parameters of signals should be minimized. Control methods should be as simple as possible, and the means that implement them - compact, with built-in algorithms for information processing and decision-making.
The most informative parameter that carries the maximum information about the state of the unit of a working machine or unit is mechanical oscillations (vibrations) - elastic waves propagating in continuous media.
Measurement of vibroacoustic characteristics on bearing supports of mechanisms allows to recognize such defects and damages as imbalance and skew of shafts; damage to plain and rolling bearings; damage to gears in gears; damage to couplings; damage to electrical machines.
With the systematic use of modern diagnostic methods it is possible to avoid serious damage to the transmission elements and reduce operating costs for maintenance of agricultural machinery due to the fact that the repair is carried out only when the measurement results indicate its need.

[1]    Konovalov, A. Y., Lukianenko, O.N., Voitiuk, V. D. (2010). Tekhnycheskaia dyahnostyka tranportnikh sredstv [Technical diagnostics of vehicles]. K. Nezhyn: Aspekt – polyhrafyia, 120p. [In Russian]
[2]    Smashniuk, O. V. (2010). Analitychni doslidzhennia zminy tekhnichnoho stanu silskohospodarskoi tekhniky dlia prohnozuvannia yii ekspluatatsiinoi nadiinosti. [Analytical studies of changes in the technical condition of agricultural machinery to predict its operational reliability]  Visn. KhNTUSH im. P. Vasylenka. X.,  Vyp. 100: Problemy nadiinosti mashyn ta zasobiv mekhanizatsii silskohospodarskoho vyrobnytstva. S. 174-178. [in Ukrainian]
[3]    Voitiuk, V. D., Shcherbatyi, P. B. (2010). Perspektyvni napriamy rozvytku metodiv i zasobiv tekhnichnoi diahnostyky silskohospodarskykh mashyn. [Promising directions of development of methods and means of technical diagnostics of agricultural machines.]  Visnyk KhNTUSH im. P. Vasylenka. X., 2010. Vyp. 93: Mekhanizatsiia silskohospodarskoho vyrobnytstva, t. 2. S. 201 - 208. [in Ukrainian]
[4]    Veselovska, N. R., Skorkin, O. A., Kotliar, O. V., Klochko, Yu. O., Starchenko, O. P. (2018). Informatsiinyi fond relevantnosti konstruktorsko-tekhnolohichnykh vydiv krupnohabarytnykh ziednan dlia peredachi krutnoho momentu. [Information fund of relevance of design and technological types of large-size connections for torque transmission.] Visnyk Natsionalnoho tekhnichnoho universytetu «KhPI». Seriya: Tekhnolohii v mashynobuduvanni № 34 (1310). [in Ukrainian]
[5]    Iskovych-Lototskyi, R. D., Zelinska, O. V., Veselovskyi, Ya. P. (2017). Otsiniuvannia efektyvnosti funktsionuvannia tekhnolohichnoho kompleksu z vykorystanniam systemnoho pidkhodu. [Evaluation of the efficiency of the technological complex using a systems approach.] Vseukrainskyi naukovo-tekhnichnyi zhurnal: "Tekhnika, enerhetyka, transport v APK. №2 (97). Vinnytsia. S.109-114. [in Ukrainian]
[6]    Petrov, V. M. (2005). Sotsialni pytannia u tekhnichnomu pereosnashchenni silskohospodarskoho vyrobnytstva. [Social issues in the technical re-equipment of agricultural production.] Ekonomika APK. No 3.  S. 118-125. [in Ukrainian]
[7]    Wilfried Reimche, Ulrich Sьdmersen, Oliver Pietsch, Christian Scheer, Fiedrich-Wilhelm Bach (2003). Basics of vibration monitoring for fault detection and process control. 3rd Pan-American Conference for Nondestructive Testing. PANNDT, 2-6 June. 10 p.
[8]    Torbatian, M., Kahaei, M. H., Poshtan, J. (2003). Bearing fault detection using Morlet wavelets. 18th international Conference on electricity, Tehran, Tavanir. 21-23 October . 5 p.
[9]    Kahaei, M. H., Torbatian, M., Poshtan, J. (2004). Fault detection of bearings using Haar wavelets. Proceedings of IEEE international conference, GCC. PP. 176-179.
[10]    Marc Thuillard (2000). A review of wavelet networks, wavenets, fuzzy wavenets and their applications.  ESIT 2000, Aachen, Germany. 14-15 September. 12 p.
[11]    Khalid, F. Al-Raheem, Asok Roy, K. P. Ramachandran, D. K. Harrison, Steven Grainger. (2007) Rolling element bearing fault diagnosis using Laplace-wavelet envelope power spectrum. EURASIP Journal on Applied Signal Processing. Vol. 2007, Issue 1. 14 p.
[12]    Garcha-prada, J. C., Castejyn, C., Lara O. J. (2007). Incipient bearing fault diagnosis using DWT for feature extraction . 12th Iftomm world congress in mechanism and machine science, Besamon (France).  17-21 June. 6 p.
[13]    Liu Qipeng, Yu Xiaoling And Feng Quanke. (2003). Fault diagnosis using wavelet neural networks.  Neural Processing Letters. Vol. 18, Issue 2. PP. 115-123.
[14]    Brian, T., Holm-Hansen, Robert X. Gao, Li Zhang. (2004). Customized wavelet for bearing defect detection. Journal of Dynamic Systems, Measurement, and Control. Vol. 126, Issue 4. PP. 740 - 745.