Cluster analysis of acoustic emission signals from the friction zone of tribosystems

  • K.A. Fenenko Petro Vasylenko Kharkiv National Technical University of Agriculture, Kharkiv
Keywords: tribosystem; wear rate; coefficient of friction; acoustic emission; cluster analysis; peak factor; transient processes; running-in.


The directions of the use of the method of acoustic emission (AE) for the study of stationary and transient processes in tribosystems during the operation have been considered here. It has been shown that the application of this method will allow obtaining information about the state of friction surfaces and the rate of wear during transients (running-in) processing online.

To substantiate the choice of the informative parameters of AE, the cluster analysis of the frames of AE signals from the friction zone of the tribosystem with the separation of the signal into the groups of the sources of its generation has been performed. The correlation dependence between the friction coefficient fтр and the values of the peak factor of cluster K2, the correlation coefficient r = 0.99, and the rate of volumetric wear I, m3/h, and the values of the peak factor of cluster K3, the correlation coefficient r = 0.99 have been established. The values of the peak factor of cluster K4 correlate with the rate of volumetric wear during the running-in, the correlation coefficient r = 0.98.

It has been experimentally confirmed that the cluster analysis of acoustic emission signals from the friction zone of the tribosystem allows one to identify the surface processes during wear, thereby increasing the robustness and information content of the AE method. This analysis can serve as the basis for the development of a diagnostic technique for tribosystems during their operation, which will make it possible to measure the wear rate at any time and calculate the tribosystem resource


1. Ferrer C., Salas F., Pascal M., Orozco J. Descrete acoustic emission waves during stick-slip friction between steel samples, Tribology International, 2010, No.43, pp.1–6. [English]
2. Shevchenko S.A.. Classification and justification of requirements for acoustic emission characteristics of friction pairs defects of mechanisms, Bulletin of Kharkiv National Technical University of Agriculture named after P. Vasylenko, 2012, No.121, p.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, Me-chanical 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. [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. Voitov V.A., Biekirov, А. Sh., Voitov A.V. Choice of informative acoustic emission parameters for determining the wear rate of tribosystems in transient modes. Technical service of agriculture, forestry and transport systems 2019. № 15. p. 190–202. [Ukraine]
14. Chechel’nitskii V.Y., Troyansky A.V. Peak factor of multi-frequency noise-like signals encoded by equivalent classes of modern binary gratings, Proceedings of Odessa Polytechnic University, 2005, No.2(24), p.181-186. [Russian]
15. Zagigaev L.S., Kishiyan А.А., Romanikov Y.I. Methods of planning and processing the results of a physical experiment, M.: Atomizdat, 1978. – 232 p. [Russian]
How to Cite
Fenenko, K. (2020). Cluster analysis of acoustic emission signals from the friction zone of tribosystems. Problems of Tribology, 25(2/96), 25-33.