Application of the wavelet multifractal analysis of vibration signal for rotating machinery diagnosis

• Autorzy: Puchalski Andrzej , Komorska Iwona
• Języki publikacji: EN

Abstrakty

EN

The paper proposes the WLMF (Wavelet Leaders Multifractal Formalism) method enabling the adoption of multifractal parameters mapped by vibration signal log-cumulants as diagnostic features, in the procedure of automatic classification of assembly errors and wear of demonstration gearbox. In the analysis of vibration time signals, initially, a multifractal formalism was used based on the study of time series local regularity, which is measured by Holder exponents. The presented test results relate to time-frequency multifractal analysis, the starting point of which was a continuous wavelet transform. Discrete wavelet transform allowed for much better grounded multifractal formalism and more accurate estimation of multifractal parameters using wavelet leaders, which are determined based on wavelet coefficients and are representatives of Holder exponents.

Słowa kluczowe

EN

rotating machines; multifractal analysis of vibration signal

PL

maszyny wirujące; analiza multifraktalna sygnału drganiowego

• Wydawca: Poznan University of Technology. Institute of Applied Mechanics
• Czasopismo: Vibrations in Physical Systems
• Rocznik: 2019
• Tom: Vol. 30, nr 2
• Strony: art. no. 2019207
• Opis fizyczny: Bibliogr. 9 poz., 1 fot., 1 rys., wykr.

Twórcy

autor

Puchalski Andrzej

• University of Technology and Humanities in Radom, ul. Malczewskiego 29, 26-600 Radom

autor

Komorska Iwona

• University of Technology and Humanities in Radom, ul. Malczewskiego 29, 26-600 Radom

Bibliografia

• 1. I. W. Kantelhardt, Fractal and Multifractal Time Series, Mathematics of Complexity and Dynamical Systems, Springer-Verlag, New York (2011) 463 – 487.
• 2. S. J. Loutridis, An algorithm for the characterization of time-series based on local regularity, Physica A, 381 (2007) 383 – 398.
• 3. S. Zhang, Y. He, J. Zhang, Y. Zhao, Multi-fractal based fault diagnosis method of rotating machinery, Applied Mechanics and Materials, 130(2) (2012) 571 – 574.
• 4. A. Puchalski, I. Komorska, A generalised entropy in multifractal time signals analysis of mechanical vibration, JVE Journal of Vibroegineering, 20(4) (2018) 1667 – 1675.
• 5. H. Liu, X. Wang, C. Lu, Rolling bearing fault diagnosis based on LCD-TEO and multifractal detrended fluctuation analysis, Mechanical Systems and Signal Processing, 60-61 (2015) 273 – 288.
• 6. A. Puchalski, I. Komorska, Stable Distributions and Fractal Diagnostic Models of Vibration Signals of Rotating Systems, Appl. Condition Monitoring (Springer Int. Pub. AG, 9 (2018) 91 – 101.
• 7. A. J. Chen, Z Li, J. Pan, G. Chen, Y. Zi, J. Yuan, B. Chen, Z. He, Wavelet transform based on inner product in fault diagnosis of rotating machinery, Mechanical Systems and Signal Processing, 70-71 (2016) 1 – 35.
• 8. W. Du, J. Tao, Y. Li, C. Liu, Wavelet leaders multifractal features based fault diagnosis of rotating mechanism, Mechanical Systems and Signal Processing, 43 (2014) 57 – 75.
• 9. H. Wendt, S. G. Roux, S. Jaffard, P. Abry, Wavelet leaders and bootstrap for multifractal analysis of images, Signal Processing, 89(6) (2009) 1100 – 1114.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).