Wavelet Based Signal Demodulation Technique for Bearing Fault Detection

• Autorzy: Klepka A
• Języki publikacji: EN

Abstrakty

EN

Diagnostics of rolling elements under varying operational conditions, where disturbances and other rotating elements have strong influence on correctness of analysis, requires engagement of advanced signal processing techniques. Extraction of signal components generated by bearing faults has been proven to be an exceptionally promising method for rolling element bearing fault detection. In this paper, wavelet signal demodulation diagnostic techniques is presented. The method is based on the wavelet transform as a method of signal demodulation. Properties of time–frequency representation of the signal enables extraction of typical damage signatures from the signal. First step of this method is a wavelet filtration, which uses Continuous Wavelet Transform (CWT). For this transformation, the Morlet wavelet function has been used. Next, the envelope function of a decoupled frequency component is estimated from wavelet coefficients. Finally, the Discrete Wavelet Transform (DWT) has been used as a post–processing method.

Słowa kluczowe

EN

continuous wavelet transform (CWT); bearing diagnostics; signal processing; discrete wavelet transform

PL

ciągła transformata falkowa; diagnostyka łożysk; przetwarzanie sygnału; dyskretna transformata falkowa

• Wydawca: Wydawnictwo Politechniki Łódzkiej
• Czasopismo: Mechanics and Mechanical Engineering
• Rocznik: 2011
• Tom: Vol. 15, nr 4
• Strony: 61--69
• Opis fizyczny: Bibliogr. 16 poz.

Twórcy

autor

Klepka A

• AGH University of Science and Technology al. Mickiewicza 30, 30-059 Kraków

Bibliografia

• [1] Ho, D. and Randall, R.B.: Optimization of bearing diagnostic techniques using simulated and actual bearing fault signals, Mechanical Systems and Signal Processing, 14(5), 768-788, 2000.
• [2] Jabłoński, A.: Development of algorithms of generating an envelope spectrum of a vibration signal in the frequency domain for rolling element bearing fault detection , Department of Automatics and Robotics, AGH University of Science and Technology, Krakow, Poland, 2008.
• [3] Randall, R.B.: Frequency Analysis, Bruel & Kjær, 3rd edition, 1978.
• [4] Smith III, J.: Mathematics of the Discrete Fourier Transform (DFT) with Audio Applications, 2nd edition, Center for Computer Research in Music and Acoustics, (CCRMA), Department of Music, Stanford University, Stanford, California 94305, USA, 2007.
• [5] Randall, B.: Applications of Spectral Kurtosis in Machine Diagnostics and Prognostics, Key Engineering Materials, Vols. 293-294, pp 21-32, 2005.
• [6] Bartelmus, W. and Zimroz, R.: Diagnostyka układów napędowych maszyn górnictwa odkrywkowego, Górnictwo Odkrywkowe, 5-6, 13-25, 2007.
• [7] Peled, R., Braunand, S. and Zacksenhouse, M.: PASVA3: A blind deconvolution separation of multiple sources, with application to bearing diagnostics, Mechanical Systems and Signal Processing, 19, 1181-1195, 2005.
• [8] Jerome Antoni: Cyclostationarity by examples, Mechanical Systems and Signal Processing, 23, 987-1036, 2009.
• [9] Staszewski, W.J.: Identi_cation of non - linear systems using Ridges and Skeletons of the Wavelet Transform, Journal of Sound and Vibration, 241(4), 639-658, 1998
• [10] Uhl, T. and Klepka, A.: Application of wavelet transform for identification of modal parameters of nonstationary systems, Journal of theoretical and applied mechanics, 43, 2, 277 - 296, Warszawa, textbf2005.
• [11] Staszewski, W. J.: Identification of damping in MDOF systems using time – scale decomposition, Journal of Sound and Vibration, 203(2), 283 - 305, 1997.
• [12] Klepka, A.: Identyfikacja parametrów modalnych układów mechanicznych w warunkach niestacjonarnych, PhD, AGH, 2006.
• [13] Uhl, T. and Klepka, A.: The use of wavelet transform for in-flight modal analysis, XII International Conference on Noise and Vibration Engineering, Leuven, 2006.
• [14] Liu Ziran, He Tao, Jiang Guoxing: Analysis of wavelet envelope spectrum to vibration signal in the gearbox, Mechanic Automation and Control Engineering (MACE) - International Conference on Electrical and Control Engineering, 2509 -2511, 2010.
• [15] Klepka, A., Barszcz, T. and Jabłoński, A.: Comparison of advanced signal analysis techniques for bearing fault detection, The 16th International Congress on Sound and Vibration : Recent developments in acoustics, noise and vibration, Kraków, Poland, 2009.
• [16] Barszcz, T.: Decomposition of vibration signals into deterministic and nondeterministic components and its capabilities of fault detection and identification, Int. J. Appl. Math. Comput. Sci., Vol. 19, No. 2, 327-335, 2009.