Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
Minimum Entropy Deconvolution (MED) has been recently introduced to the machine condition mon- itoring field to enhance fault detection in rolling element bearings and gears. MED proved to be an excellent aid to the extraction of these impulses and diagnosing their origin, i.e. the defective component of the bearing. In this paper, MED is revisited and re-introduced with further insights into its application to fault detection and diagnosis in rolling element bearings. The MED parameter selection as well as its combination with pre-whitening is discussed. Two main cases are presented to illustrate the benefits of the MED technique. The first one was taken from a fan bladed test rig. The second case was taken from a wind turbine with an inner race fault. The usage of the MED technique has shown a strong enhancement for both fault detection and diagnosis. The paper contributes to the knowledge of fault detection of rolling element bearings through providing an insight into the usage of MED in rolling element bearings diag- nostic. This provides a guide for the user to select optimum parameters for the MED filter and illustrates these on new interesting cases both from a lab environment and an actual case.
Czasopismo
Rocznik
Tom
Strony
131--141
Opis fizyczny
Bibliogr. 26 poz., rys., tab., wyk.
Twórcy
autor
autor
- AGH University of Science and Technology Al. Mickiewicza 30, 30-059 Krakow, Poland, tbarszcz@agh.edu.pl
Bibliografia
- 1. Antoni J., Randall R.B. (2004a), Unsupervised noise cancellation for vibration signals: Part I - evaluation of adaptive algorithms, Mechanical Systems and Signal Processing, 18, 89-101.
- 2. Antoni J., Randall R.B. (2004b), Unsupervised noise cancellation for vibration signals: Part II - a novel frequency domain algorithm, Mechanical Systems and Signal Processing, 18, 103-117.
- 3. Antoni J., Randall R.B. (2006), The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines, Mechanical Systems and Signal Processing, 20, 308-331.
- 4. Antoni J. (2007), Cyclic spectral analysis in practice, Mechanical Systems and Signal Processing, 21, 597-630.
- 5. Antoni J. (2009), Cyclostationarity by examples, Mechanical Systems and Signal Processing, 23, 987-1036.
- 6. Barszcz T. (2009), Decomposition of vibration signals into deterministic and nondeterministic components and its capabilities for fault detection and identification, International Journal of Applied Mathematics and Computer Science, 19, 327-335.
- 7. Barszcz T., Jabłoński A. (2011), A novel method for the optimal band selection for vibration signal demodulation and comparison with the Kurtogram, Mechanical Systems and Signal Processing, 20, 308-331.
- 8. Boumahdi M., Lacoume J. (1995), Blind identification using the Kurtosis: Results of field data processing, IEEE Trans of Signal Processing, 0-7803-2431-5/95, pp. 1960-1983.
- 9. Braun S. (2010), The synchronous (time domain) average revisited, The 24th International Conference on T. Barszcz, N. Sawalhi - Fault Detection Enhancement in Rolling Element Bearings Using the MED 141 Noise and Vibration engineering (ISMA2010), Leuven (Belgium), 20-22 September 2010.
- 10. Cempel C. (2008), Decomposition of symptom observation matrix and grey forecasting in vibration condition monitoring of machines, International Journal of Applied Mathematics and Computer Science, 18, 569-579.
- 11. Darlow M.S., Badgley R.H., Hogg G.W. (1974), Application of high frequency resonance techniques for bearing diagnostics in helicopter gearboxes, Technical Report, US Army Air Mobility Research and Development Laboratory, pp. 74-77.
- 12. Endo H., Randall R.B. (2007), Application of a minimum entropy deconvolution filter to enhance Autoregressive model based gear tooth fault detection technique, Mechanical Systems and Signal Processing, 21, 906-919.
- 13. Gajetzki M. (2006), SeaCom - Digital Measurement and Communication Systems, SeaCom, Herne.
- 14. Gibiec M. (2006), An application of acoustic measurements to quality control of low power electrical motors, Archives of Acoustics, 31, 4, 521-528.
- 15. Hau E. (2006), Wind Turbines. Fundamentals, Technologies, Applications, Economics, 2nd Edition, Springer Verlag, Berlin Heisenberg.
- 16. Heng A., Zhang S., Tan A.C.C., Mathew J. (2009), Rotating machinery prognostics: State of the art, challenges and opportunities, Mechanical Systems and Signal Processing, 23, 724-739.
- 17. Ho D., Randall R.B. (2000), Optimisation of bearing diagnostic techniques using simulated and actual bearing fault signals, Mechanical Systems and Signal Processing, 14, 763-788.
- 18. Klein U. (2003), Vibrodiagnostic assessment of machines and devices [in German: Schwingungsdiagnostische Beurteilung von Maschinen und Anlagen], Stahleisen Verlag, Duesseldorf 2003.
- 19. Krzyworzeka P., Cioch W. (2006), Demodulation of non-stationary machine vibration using cycle-time scale, Archives of Acoustics, 31, 2, 167-177.
- 20. Lee J.Y., Nandi A.K. (2000), Extraction of impacting signals using blind deconvolution, Journal of Sound and Vibration, 232, 945-962.
- 21. Nandi A.K., Mampel D., Roscher B. (1997), Blind deconvolution of ultrasonic signals in non-destructive testing applications, IEEE Trans of Signal Processing, 45, 1382-1390.
- 22. Randall R.B., Antoni J., Chobsaard S. (2001), The relationship between spectral correlation and envelope analysis in the diagnostics of bearing faults and other cyclostationary machine signals, Mechanical Systems and Signal Processing, 15, 945-962.
- 23. Randall R.B., Antoni J. (2011), Rolling element bearing diagnostics - A tutorial, Mechanical Systems and Signal Processing, 25, 485-520.
- 24. Sawalhi N., Randall R.B., Endo H. (2007), The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis, Mechanical Systems and Signal Processing, 21, 2616-2633.
- 25. Wang W., Wong A.K. (2002), Autoregressive model- based gear fault diagnosis, Transaction of ASME, Journal of Vibration and Acoustics, 124, 172-179.
- 26. Wiggins R.A. (1978), Minimum entropy deconvolution, Geoexploration, 16, 21-35
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUS8-0022-0001
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