Novel 2D representation of vibration for local damage detection
1
Diagnostics and Vibro-Acoustics Science Laboratory, Wroclaw University of Technology
2
Hugo Steinhaus Center, Institute of Mathematics and Computer Science, Wroclaw University of Technology
Corresponding author
Grzegorz Żak
Diagnostics and Vibro-Acoustics Science Laboratory, Wroclaw University of Technology, Na Grobli 15, 50-421 Wroclaw, Poland
Diagnostics and Vibro-Acoustics Science Laboratory, Wroclaw University of Technology, Na Grobli 15, 50-421 Wroclaw, Poland
Mining Science 2014;21:105-113
KEYWORDS
ABSTRACT
In this paper a new 2D representation for local damage detection is presented. It is based on a vibration time series analysis. A raw vibration signal is decomposed via short-time Fourier transform and new time series for each frequency bin are differentiated to decorrelate them. For each time series, autocorrelation function is calculated. In the next step ACF maps are constructed. For healthy bearing ACF map should not have visible horizontal lines indicating damage. The method is illustrated by analysis of real data containing signals from damaged bearing and healthy for comparison.
REFERENCES (29)
1.
ALLEN J.B., 1977. Short term spectral analysis, synthesis, and modification by discrete Fourier transform, Acoustics, Speech and Signal Processing, IEEE Transactions on, Vol. 25, No. 3, 235-238.
2.
ANTONI J., 2009. Cyclostationarity by examples, Mechanical Systems and Signal Processing, Vol. 23, No. 4, 987-1036.
3.
BOX G.E.P., JENKINS G.M., REINSEL G.C., 1994. Time Series Analysis: Forecasting and Control. 3rd edition. Upper Saddle River, NJ: Prentice-Hall, New York.
5.
COHEN L, 1989. Time-frequency distributions - A review, Proceedings of the IEEE, Vol. 77, No. 7, 941-981.
6.
COLLIS W.B., WHITE P.R., HAMMOND J.K., 1998. Higher-order spectra: the bispectrum and trispectrum, Mechanical Systems and Signal Processing, Vol. 12, No. 3, 375-394.
7.
DE SENA A., ROCCHESSO D., 200 A Fast Mellin and Scale Transform, EURASIP Journal on Advances in Signal Processing Vol. 2007:89170.
8.
ENDO H., RANDALL R.B., 2007. Enhancement of autoregressive model based gear tooth fault detection technique by the use of minimum entropy deconvolution filter, Mechanical Systems and Signal Processing, Vol. 21, No. 2, 906-919.
9.
FENG Z., LIANG M., CHU F., 2013. Recent advances in time–frequency analysis methods for machin-ery fault diagnosis: A review with application examples, Mechanical Systems and Signal Processing, Vol. 38, No. 1, 165-205.
10.
FLANDRIN P., 1999. Time–frequency/Time–Scale Analysis, Wavelet Analysis and its Applications, Vol. 10, Academic Press, San Diego.
11.
GUOJI S., MCLAUGHLIN S., YONGCHENG X., WHITE, P. 2014. Theoretical and experimental analysis of bispectrum of vibration signals for fault diagnosis of gears, Mechanical Systems and Sig-nal Processing, Vol. 43, No. 1-2, 76-89.
12.
HICKEY D., WORDEN K., PLATTEN M.F., WRIGHT J.R., COOPER J.E., 2009. Higher-order spectra for identification of nonlinear modal coupling, Mechanical Systems and Signal Processing, Vol. 23, No. 4, 1037-1061.
13.
KLEIN R., MASAD E., RUDYK, E., WINKLER, I., 2014. Bearing diagnostics using image processing methods, Mechanical Systems and Signal Processing, Vol. 45, No. 1, 105-1.
14.
LIN S.T., MCFADDEN P.D., 1997. Gear vibration analysis by b-spline wavelet-based linear wavelet transform, Mechanical Systems and Signal Processing, Vol. 11, No. 4, 603-609.
15.
MAKOWSKI R.A., ZIMROZ R., 2011. Adaptive bearings vibration modelling for diagnosis, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6943 LNAI, 248-259.
16.
MAKOWSKI R., ZIMROZ R., 2014. Parametric time-frequency map and its processing for local dam-age detection in rotating machinery, Key Engineering Materials, Vol. 588, 214-222.
17.
MARTIN N., 1986. An AR Spectral Analysis of Non Stationary Signals. Signal Processing, Vol.10, 61-74.
18.
OBUCHOWSKI J., WYŁOMAŃSKA A., ZIMROZ R., 2014. Selection of informative frequency band in local damage detection in rotating machinery, Mechanical Systems and Signal Processing, Vol. 48, No. 1-2, 138-152.
19.
OBUCHOWSKI J., WYŁOMAŃSKA A., ZIMROZ R., 2014. The local maxima method for enhance-ment of time–frequency map and its application to local damage detection in rotating machines, Me-chanical Systems and Signal Processing, Vol. 46, No. 2, 389-405.
20.
PENG Z., CHU F., HE Y., 2002. Vibration signal analysis and feature extraction based on reassigned wavelet scalogram, Journal of Sound and Vibration, Vol. 253, No. 5, 1087-1100.
21.
POULIMENOS A.G., FASSOIS S.D., 2006. Parametric time-domain methods for non-stationary ran-dom vibration modelling and analysis – A critical survey and comparison, Mechanical Systems and Signal Processing, Vol. 20, No. 4, 763-816.
22.
SPIRIDONAKOS M.D., FASSOIS S.D., 2014. Non-stationary random vibration modelling and analysis via functional series time-dependent ARMA (FS-TARMA) models – A critical survey, Mechanical Systems and Signal Processing, Vol. 47, No. 1-2, 175-224.
23.
STASZEWSKI W.J., WORDEN K., TOMLINSON G.R., 1997. Time-frequency analysis in gearbox fault detection using the Wigner-Ville distribution and pattern recognition, Mechanical Systems and Sig-nal Processing, Vol. 11, No. 5, 673-692.
24.
URBANEK J., ANTONI J., BARSZCZ T., 2012. Detection of signal component modulations using modulation intensity distribution, Mechanical Systems and Signal Processing, Vol. 28, 399-413.
25.
WANG W., WONG A.K., 2002. Autoregressive model-based gear fault diagnosis, Journal of Vibration and Acoustics, Transactions of the ASME, Vol. 124, No. 2, 172-179.
26.
WANG W., 2008. Autoregressive model-based diagnostics for gears and bearings, Insight: Non-Destructive Testing and Condition Monitoring, Vol. 50, No. 8, 414-418.
27.
WANG X., MAKIS V., 2009. Autoregressive model-based gear shaft fault diagnosis using the Kolmogorov–Smirnov test, Journal of Sound and Vibration, Vol. 327, No. 3-5, 413-423.
28.
WYŁOMAŃSKA A., OBUCHOWSKI J., ZIMROZ R., HURD H., 2014. Periodic autoregressive modeling of vibration time series from planetary gearbox used in bucket wheel excavator, in: Cyclostation-arity: Theory and Methods Lecture Notes in Mechanical Engineering, Fakher Chaari et al. (eds.), 171-186, Springer, Berlin.
29.
ZIMROZ R., BARTELMUS W., 2012. Application of adaptive filtering for weak impulsive signal recovery for bearings local damage detection in complex mining mechanical systems working under condition of varying load, Solid State Phenomena, Vol. 18.
| eISSN: | 2353-5423 |
| ISSN: | 2300-9586 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
