Stochastic modeling of time series with application to local damage detection in rotating machinery

Key Engineering Materials

Volumn 569-570, Issue , 2013, Pages 441-448

  Obuchowski, Jakub ^a   Wylomanska, Agnieszka ^a   Zimroz, Radoslaw ^a  

^a WROCLAW UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

Local damage; Processes separation; Rotating machines; Time series modeling; Vibration

Indexed keywords

ROTATING MACHINERY; SIGNAL PROCESSING; STATISTICAL TESTS; STOCHASTIC SYSTEMS; TIME SERIES; TIME SERIES ANALYSIS; VIBRATION ANALYSIS;

INDUSTRIAL CONDITIONS; LOCAL DAMAGE; ROTATING MACHINE; SIGNALS CLASSIFICATIONS; STATISTICAL PROPERTIES; TIME SERIES MODELING; TIME-FREQUENCY REPRESENTATIONS; VIBRATION;

DAMAGE DETECTION;

EID: 84883719277     PISSN: 10139826     EISSN: 16629795     Source Type: Book Series    
DOI: 10.4028/www.scientific.net/KEM.569-570.441     Document Type: Conference Paper

References (32)

1. Samuel, P.D., Pines, D.J., A review of vibration-based techniques for helicopter transmission diagnostics, Journal of Sound and Vibration 282 (1-2) (2005) 475-508.
2. Randall, R.B., Antoni, J., Rolling element bearing diagnostics-A tutorial, Mechanical Systems and Signal Processing, 25 (2) (2011) pp. 485-520.
3. Poulimenos, A.G., Fassois, S.D., Parametric time-domain methods for non-stationary random vibration modeling and analysis - A critical survey and comparison, Mechanical Systems and Signal Processing 20 (4) (2006) pp. 763-816.
4. Antoni, J., Bonnardot, F., Raad, A., El Badaoui, M., Cyclostationary modeling of rotating machine vibration signals, Mechanical Systems and Signal Processing 18 (6) (2004) pp. 1285-1314.
5. Baillie, D.C., Mathew, J., A comparison of autoregressive modeling techniques for fault diagnosis of rolling element bearings, Mechanical Systems and Signal Proc. 10 (1) (1996) pp. 1-17.
6. Zhan, Y., Makis, V., Jardine, A.K.S., Adaptive state detection of gearboxes under varying load conditions based on parametric modeling, Mech. Syst. and Signal Proc. 20 (1) (2006) pp. 188-221.
7. Zhan, Y., Mechefske, C.K., Robust detection of gearbox deterioration using compromised autoregressive modeling and Kolmogorov-Smirnov test statistic-Part I: Compromised autoregressive modeling with the aid of hypothesis tests and simulation analysis, Mechanical Systems and Signal Processing 21 (5) (2007) pp. 1953-1982.
8. Makowski, R.A., Zimroz, R., Adaptive bearings vibration modeling for diagnosis, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6943 LNAI (2011) pp. 248-259.
9. Makowski R.A., Zimroz R., A procedure for weighted summation of the derivatives of reflection coefficients in adaptive Schur filter with application to fault detection in rolling element bearings, Mechanical Systems and Signal Processing, (2012) http://dx.doi.org/10.1016/ j.ymssp.2012.05.005.
10. McFadden, P.D., Detecting fatigue cracks in gears by amplitude and phase demodulation of the meshing vibration, Journal of vibration, acoustics, stress, and reliability in design 108 (2) (1986) pp. 165-170.
11. McFadden, P.D., Examination of a technique for the early detection of failure in gears by signal processing of the time domain average of the meshing vibration Mechanical Systems and Signal Processing 1 (2) (1987) pp. 173-183.
12. Chaari, F., Bartelmus, W., Zimroz, R., Fakhfakh, T., Haddar, M., Gearbox vibration signal amplitude and frequency modulation, Shock and Vibration 19 (4) (2012) pp. 635-652.
13. Antoni, J., Cyclostationarity by examples, Mechanical Systems and Signal Processing 23 (4) (2009) pp. 987-1036.
14. Lin, J., Zuo, M.J., Gearbox fault diagnosis using adaptive wavelet filter, Mechanical Systems and Signal Processing, 17 (6) (2003) pp. 1259-1269.
15. Su, W., Wang, F., Zhu, H., Zhang, Z., Guo, Z., Rolling element bearing faults diagnosis based on optimal Morlet wavelet filter and autocorrelation enhancement, Mechanical Systems and Signal Processing, 24 (5) (2010) pp. 1458-1472.
16. He, W., Jiang, Z.-N., Feng, K., Bearing fault detection based on optimal wavelet filter and sparse code shrinkage, Measurement: Journal of the International Measurement Confederation, 42 (7) (2009) pp. 1092-1102.
17. Urbanek, J., Barszcz, T., Zimroz, R., Antoni, J., Application of averaged instantaneous power spectrum for diagnostics of machinery operating under non-stationary operational conditions, Measurement: Journal of the International Measurement Confederation 45 (7) (2012) 1782-1791.
18. Urbanek J., Barszcz T., Antoni J., Time-frequency approach to extraction of selected second-order cyclostationary vibration components for varying operational conditions, Measurement (2012) DOI: http://dx.doi.org/10.1016/j. measurement.2012.11.042.
19. Zimroz, R., Bartelmus, W., Gearbox condition estimation using cyclo-stationary properties of vibration signal, Key Engineering Materials, 413-414, (2009) pp. 471-478.
20. Antoni J., Randall R.B., Differential diagnosis of gear and bearing faults, ASME J. Vib. Acoust., 124 (2) (2002) pp. 165-171.
21. 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., 19 (2) (2009) pp. 327-335.
22. Combet, F., Gelman, L., Optimal filtering of gear signals for early damage detection based on the spectral kurtosis, Mechanical Systems and Signal Processing 23 (3) (2009) pp. 652-668.
23. Zimroz, R., Bartelmus, W., 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, Diffusion and Defect Data Pt. B: Solid State Phenomena 180 (2012) pp. 250-257.
24. Barszcz, T., Jabłoński, A., A novel method for the optimal band selection for vibration signal demodulation and comparison with the Kurtogram, Mechanical Systems and Signal Processing, 25 (1) (2011) pp. 431-451.
25. Urbanek, J., Antoni, J., Barszcz, T., Detection of signal component modulations using modulation intensity distribution, Mechanical Systems and Signal Proc. 28 (2012) pp. 399-413.
26. Bryc W., The Normal Distribution: Characterizations with Applications, Springer-Verlag, 1995.
27. Allen, J., Short term spectral analysis, synthesis, and modification by discrete Fourier transform, Acoustics, Speech and Signal Processing, IEEE Transactions on 25 (3) (1977) pp. 235-238.
28. Corder G.W., Foreman, D.I, Nonparametric Statistics for Non-Statisticians: A Step-by-Step Approach, Wiley, 2009.
29. Anderson T.W., Darling D.A., Asymptotic theory of certain "goodness-of-fit" criteria based on stochastic processes, Annals of Mathematical Statistics 23 (1952) pp. 193-212.
30. Stephens M.A., Test of fit for the logistic distribution based on the empirical distribution function, Biometrika, 66(3) (1979) pp. 591-595.
31. Burnecki K., Wyłomańska A., Beletskii A., Gonchar V., Chechkin A., Recognition of stable distribution with Levy index alpha close to 2, Phys. Rev. E 85 (2012) 056711.
32. Cleveland W.S., The Elements of Graphing Data, Hobart Press, 1994.