Engineering, Energy, 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).

Shargorodskiy Serhiy – PhD, Associate Professor of the Department "Machinery and Equipment of Agricultural Production" of the Vinnytsia National Agrarian University (3, Solnychna str., Vinnytsia, Ukraine, 21008, e-mail: sergey20@vsau.vin.ua).

Bratslavets Bohdan – post-graduate student of the Department "Machinery and Equipment of Agricultural Production" of Vinnytsia National Agrarian University (3 Sonyachna Street, Vinnytsia, 21008, Ukraine)

Yalina Olha – post-graduate student of the Department "Machinery and Equipment of Agricultural Production" of Vinnytsia National Agrarian University (3 Sonyachna Street, Vinnytsia, 21008, Ukraine)

Today the vibrodiagnostic method achieves the highest efficiency and manufacturability for the operation of the technical condition of the technological equipment of the agro-industrial complex. At the same time, this method is one of the most modern methods of technical diagnostics, indicating the kinematic warehouses of diagnostic objects.
Vibration analysis is a fundamental tool for diagnostic monitoring of bearings. The vibration signal of defective rolling bearings and its spectrum contain characteristic features by which it is possible to fairly correctly identify the type and location of the defect. At the moment the defective element passes through the loaded zone of the rolling bearing, a pronounced peak, an energy impulse, appears in the vibration. Thus, when a bearing with internal defects is operating, characteristic components appear in vibration - harmonics with natural frequencies, the numerical values of which can be calculated using theoretical formulas using the geometric dimensions of the bearing elements and the rotor speed of the mechanism.
In a loaded bearing, four characteristic frequencies can be distinguished that are used for diagnostics - the frequency of the outer bearing cage, the frequency of the inner cage, the cage frequency and the rolling element frequencies.
The complexity of the analysis of vibration signals of rolling bearings for the purpose of their diagnostics lies in the fact that the signs of a defective bearing are distributed over a wide range of frequencies, have low vibrational energy and are somewhat random in nature. In addition, the vibration signal is, of course, removed from the body of the equipment containing the bearing, and therefore contains not only information useful from the point of view of bearing diagnostics, but also noise - vibrations produced by other parts of the mechanism.
The analysis of methods for diagnosing bearing defects based on wavelet analysis of their vibration signals allows us to single out the most promising direction, which consists in the fact that the bearing vibration signal is decomposed into coefficients using wavelet analysis, after which the most significant coefficients are selected from these coefficients.

[1]    Petrov, V. M. (2005). Sotsialni pytannia u tekhnichnomu pereosnashchenni silskohospodarskoho vyrobnytstva [Social issues in the technical re-equipment of agricultural production]: Ekonomika APK. 3. 118–125. [in Ukrainian].
[2]    Tandon, N., Choudhury, A. (1999). A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings: Tribology International. 32. 469–480.[in English]
[3]    Wilfried Reimche, Ulrich Sdmersen, 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. [in English]
[4]    Rioul, O., Vetterli, M. (1991). Wavelets and signal processing: IEEE SP Magazine. 14–38. [in English]
[5]    Malla, S. (2005). Vivlet v obrobtsi syhnaliv [Wavelets in signal processing]: M.: Mir. [In Russian]
[6]    Torbatian, M.,. Kahaei. M. H, Poshtan, J. (2003). Bearing fault detection using Morlet wavelets: 18th international Conference on electricity, Tehran, Tavanir. 21-23 October. [in English]
[7]    Kahaei, M. H., Torbatian, M., Poshtan, J. (2004). Fault detection of bearings using Haar wavelets: Proceedings of IEEE international conference, GCC. 176–179. [in English]
[8]    Marc Thuillard. (2000). A review of wavelet networks, wavenets, fuzzy wavenets and their applications: ESIT 2000, Aachen, Germany. 14-15 September, 12 p. [in English]
[9]    Khalid, F. Al-Raheem, Asok Roy, Ramachandran, K. P., Harrison, D. K., Steven Grainger (2007). Rolling element bearing fault diagnosis using Laplace-wavelet envelope power spectrum. EURASIP Journal on Applied Signal Processing. 27, Issue 1.  [in English]
[10]    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.. [in English]
[11]    Liu Qipeng, Yu Xiaoling And Feng Quanke (2003). Fault diagnosis using wavelet neural networks. Neural Processing Letters, 18(2). 115–123. [in English]
[12]    Brian, T. Holm-Hansen, Robert, X. Gao, Li Zhang. (2004). Customized wavelet for bearing defect detection. Journal of Dynamic Systems, Measurement, and Control, 126(4). 740–745. [in English]
[13]    Rutkevich, V. S. (2017). Adaptivnij gidravlichnij privod blochno-porcijnogo vidokremlyuvacha konservovanogo kormu [Adaptive hydraulic drive of the block-portion separator of canned food], Tehnika, energetika, transport APK, 4(99), 108–113, [in Ukrainian]
[14]    Lyubin, M. V., Tokarchuk, O. A., Yaropud, V. M.  (2019). Gidrotransport v galuzyah agropromislovogo virobnictva [Hydraulic transport in the fields of agro-industrial production], Tehnichnij progres u tvarinnictvi ta kormovirobnictvi: VII Vseukrayin. nauk.-tehn. konf.,(smt Glevaha Kiyivskoyi oblasti-Kiyiv. Ukrayina, 5-28 grud. 2018 r).-Glevaha-Kiyiv. 61–63. [in Ukrainian].
[15]    Shargorodskiy, S. A., Yanishevskiy, V. Y., Yalina, O. O. (2020). Ohliad tekhnolohii ta tekhnolohichnykh zasobiv dlia zagotivli sinazhu [Overview of technologies and technological equipment for hayme processing], Tehnika, energetika, transport APK, 3(110), 145–152, [in Ukrainian]