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Inferring the global and partial system condition by means of multidimensional condition monitoring

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Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Konferencja
Symposium Vibrations In Physical Systems (23 ; 28-31.05.2008 ; Będlewo koło Poznania ; Polska)
Języki publikacji
EN
Abstrakty
EN
Machines have many faults which evolve during its life (operation). If one observe some number of symptoms during the machine operation it is possible to capture needed fault oriented information. One of the methods to extract fault information from such symptom observation matrix (SOM) is to apply the singular value decomposition (SVD), obtaining in this way the generalized fault symptoms. The problem of this paper is to use the total damage symptom, being a sum of all generalized symptoms, and the first generalized symptom to infer better on machine condition. There was some new software created for this purpose, and some cases of machine condition monitoring have been considered as examples. Considering these it seems to the author, that both generalized symptoms should be used for the inference on machine condition. They are complimentary each other in some way, and should be use together to increase the reliability of diagnostic decision.
Rocznik
Tom
Strony
79--94
Opis fizyczny
Bibliogr. 23 poz., wykr.
Twórcy
autor
  • Poznan University of Technology, Institute of Applied Mechanics, Division of Vibroacoustics and Biodynamics of Systems, Piotrowo 3 Street 60-965 Poznań, Poland
Bibliografia
  • 1. Cempel C., Innovative developments in systems condition monitoring, Keynote Lecture, Proceedings of DAMAS'99, Dublin 1999, Key Engineering Materials, Vols. 167-168 (1999) pp. 172-189.
  • 2. Cempel C., Implementing Multidimensional Inference Capability in Vibration Condition Monitoring, Proceedings of Conference: Acoustical and Vibratory Surveillance, Senlis - France, October 2004.
  • 3. Cempel C., Natke H. G., Yao J. P. T., Symptom Reliability and Hazard for Systems Condition Monitoring, Mechanical Systems and Signal Processing, Vol. 14, No 3, 2000, pp 495-505.
  • 4. Korbicz J., et all, (eds.), Fault Diagnosis – Models, Artificial Intelligence, Applications, Springer Verlag, Berlin - Heidelberg, 2004, p828.
  • 5. Tumer I. Y., Huff E. M., Principal component analysis of tri-axial vibration data from helicopter transmission, 56th Meeting of the Society of Machine Failure Prevention Technology, 2002.
  • 6. Jasiński M., Empirical models in gearbox diagnostics (in Polish), PhD Thesis, Warsaw University of Technology, Warsaw, December 2004.
  • 7. Cempel C., Vibroacoustic Condition Monitoring, Ellis Horwood Press, New York, 1991, p212.
  • 8. Cempel C., Simple condition forecasting techniques in vibroacoustical diagnostics, Mechanical Systems and Signal Processing, 1987, pp 75 – 82.
  • 9. Berry M. W., Drmac Z., Jessup E. R., “Matrices, Vector Spacer, and Information Retrieval”, SIAM Review, 1999, Vol. 41 No 2, pp 335-362.
  • 10. Wen K. L., Chang T. C., The research and development of completed GM(1,1) model toolbox using Matlab, International Journal of Computational Cognition, 2005, Vol. 3, No 3, pp 42-48.
  • 11. Zhang L., Wang Z., Zhao S., Short-term fault prediction of mechanical rotating parts on the basis of fuzzy-grey optimizing method, Mechanical Systems and Signal Processing, 2007, Vol.21, pp 856-865.
  • 12. Yao A. W. L., Chi S.C., Analysis and design of a Taguchi-Grey based electricity demand predictor for energy management systems, Energy Conversion& Management, 2004, Vol.45, pp 1205-1217.
  • 13. Deng J-L., Control Problems of Grey Systems, Systems and Control Letters, Vol. 1, No 5, North Holland, Amsterdam, 1982.
  • 14. Deng J-L., Introduction to grey system theory, The Journal of Grey System, 1989,Vol. 1, No 1, pp 1-24.
  • 15. Deng J-L., The Course on Grey Systems Theory, Publishing House, Huazhong University of Technology, Wuhan, (in Chinese), 1990.
  • 16. Zhang H., Li Z., Chen Z., Application of grey modeling method to fitting and forecasting wear trend of marine diesel engines, Tribology International, 2003,Vol.36, pp 753 – 756.
  • 17. Wang T. C., Liou M. C., Hung H. H., Application of grey theory on forecasting the exchange rate between TWD and USD, Internet 2005, pp 1 – 8.
  • 18. Tabaszewski M. Forecasting of residual life of the fan mill by means of neural nets (in Polish), Diagnostyka, vol. 3, (39), 2006, s. 149-156.
  • 19. Cempel C., Tabaszewski M., Multidimensional condition monitoring of the machines in non-stationary operation, Mechanical Systems and Signal Processing, Vol. 21, 2007, pp 1233-1247.
  • 20. Cempel C., Tabaszewski M., Grey System Theory in Application to Modeling and Forecasting in Machine Condition Monitoring, (sent to Bulletin of PAN – Technical Sciences, January 2008).
  • 21. Hall D. L., Llinas J., An introduction to multisensor data fusion, Proceedings of the IEEE, Vol. 85, Jan. 1997, pp 6-23.
  • 22. Roemer M. J., Kacprzyński G. J., Orsagh R. F., Assessment of data and knowledge fusion strategies for prognostic and health management, Internet, http://www.impact-tex.com/data/Publications/f054_gjk.pdf
  • 23. Will T., Hanger matrix, two-thirds theorem, Internet; http://www.uwlax.edu/faculty/will/svd/svd/index.html , June 2005; (see also: SVD ingredients Mathematica, April 2004)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-bc15bab5-2096-4e00-9685-28f2f90e3b25
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