Substantiation of informative amplitudes during registration of acoustic emission signals from the friction zone of tribosystems
DOI:
https://doi.org/10.31891/2079-1372-2021-99-1-6-12Keywords:
tribosystem; probability density; acoustic emission; cluster analysis; informative frequency; informative amplitude; autocorrelation functionAbstract
In this work, the dependence of the change in the probability density of the distribution of the number of pulses and amplitudes of acoustic emission (AE) signals from the friction zone at the steady-state operation of the tribosystem is obtained. Acoustic vibrations that the tribosystem generates during operation are due to the impact interaction of the roughness of the friction surfaces of their elastoplastic deformation, processes of formation and destruction of frictional links, structural and phase rearrangement of materials, the formation and development of microcracks in the surface layers of contacting bodies, separation of wear particles. The dependence allows you to determine a sufficient number of pulses in the signal frame and their amplitude values for diagnosing tribosystems during their operation. The values of the informative amplitudes of the clusters are experimentally substantiated К2, К3, К4 in relation to the base cluster К1. It is shown that an increase in the informative frequency fAE(fix) from 250 to 500 kHz, increases the value of the informative amplitude to 17,6…43,75%. Based on the results obtained, it was concluded that this fact must be taken into account when developing methods, which will increase the accuracy of diagnosing tribosystems.
The autocorrelation coefficient characterizes the closeness of the linear relationship of the current and previous frames of the series for each of the analyzed clusters. By the value of the autocorrelation coefficient, one can judge the presence of a linear relationship between the values of the recorded amplitudes, their reproducibility in terms of recording time in the steady-state operation of the tribosystem.
To confirm the sufficiency of the selected number of pulses in the clusters of the AE signal frame, as well as the reproducibility of the results of the analysis of frames when they shift in time of registration, an expression is obtained for calculating the autocorrelation function, which reflects the relationship between successive levels of the time series. Based on the results of the experimental data, the values of the autocorrelation coefficients were calculated, equal to 0,82…0,92, which indicates the robustness of the chosen diagnostic technique.
References
2. Shevchenka S.A. Klasyfikatsiya ta obgruntuvannya vymoh do akustyko-emisiynykh oznak defektiv par tertya mekhanizmiv, Visnyk Kharkivsʹkoho natsionalʹnoho tekhnichnoho universytetu silʹsʹkoho hospodar-stva im.P.Vasylenka, 2012, vyp.121, s.159-163. [Ukraine]
3. Abdullah M., D. Al-Ghamd, Zhechkov, D. Mba. A comparative experimental study on the use of Acoustic Emission and vibration analysis for bearing defect identification and estimation of defect size, Mechanical System and Signal Processing, 2006, No.7, pp.1537–1571. [English]
4. Mazal P., V.Koula, F.Hort, F.Vlasic. Applications of continuous sampling of AE signal for detection of fatigue damage, NDT in Progress, 2009, No.4. –8 p. [English]
5. Yanhui Feng. Discrete wavelet-based thresholding study on acoustic emission signals to detect bearing defect on a rotating machine, The Thirteen International Congress of Sound and Vibration. Vienna, Austria, 2-6 July, 2006. –8 p. [English]
6. Faris Elasha., Matthew Greaves, David Mba, Abdulmajid Addali. Application of Acoustic Emission in Diagnostic of Bearing Faults within a Helicopter gearbox, The Fourth International Conference on Through-life Engineering Services. Procedia CIRP, 2015, Vol.38, рp. 30-36. [English]
7. Seyed A. Niknam, Tomcy Thomas, J. Wesley Hines, Rapinder Sawhney. Analysis of Acoustic Emission Data for Bearings subject to Unbalance, International Journal of Prognostics and Health Management, 2013, Vol. 15, pp. 1–10. [English]
8. Badgujar M.P., Patil A.V. Fault Diagnosis of Roller Bearing Using Acoustic Emission Technique and Fuzzy Logic, International Journal of Latest Trends in Engineering and Technology, 2014,Vol. 3, Issue 4, pp.170–175. [English]
9. Rao V.V., Ratnam Ch. A Comparative Experimental Study on Identification of Defect Severity in Rolling Element Bearings using Acoustic Emission and Vibration Analysis, Tribology in Industry, 2015, Vol. 37, No. 2, pp.176-185. [English]
10. Zahari Taha., Indro Pranoto. Acoustic Emission - Research and Applications. Chapter 4 – Acoustic Emission Application for Monitoring Bearing Defects, InTech. 2013, pp.71–90. http://dx.doi.org/10.5772/55434 [English]
11. Nienhaus K., Boos F.D., Garate K., Baltes R. Development of Acoustic Emission (AE) based defect parameters for slow rotating roller bearings, Journal of Physics: Conference Series. 364. 2012. 012034. 1-10. doi:10.1088/1742-6596/364/1/012034 [English]
12. Yongyong He., Xinming Zhang, Michael I. Friswell. Defect Diagnosis for Rolling Element Bearings Using Acoustic Emission, Journal of Vibration and Acoustics, 2009, Vol. 131 / 061012. [English]
13. Fenenko K.A. Cluster analysis of acoustic emission signals from the friction zone of tribosystems / Problems of Tribology, V. 25, No 2/96-2020, 25-33 DOI: https://doi.org/10.31891/2079-1372-2020-96-2-25-33 [English]
14. Fenenko K.A. The determination of the information frequencies in the frame of the acoustic emission signals from the friction zone of tribosystems / Problems of Tribology, V. 25, No 3/97-2020, 6-13 DOI: https://doi.org/10.31891/2079-1372-2020-97-3-6-13 [English]
