Open Access
Issue
MATEC Web Conf.
Volume 182, 2018
17th International Conference Diagnostics of Machines and Vehicles
Article Number 01007
Number of page(s) 10
Section Diagnostics of Machines and Vehicles
DOI https://doi.org/10.1051/matecconf/201818201007
Published online 30 July 2018
  1. R. Ahmad, S. Kamarudin, An overview of time-based and condition - based maintenance in industrial application. Computers & Industrial Engineering (2012).. [Google Scholar]
  2. I. Birger, Technical diagnostics. Nauka, Moscov (1978). [Google Scholar]
  3. C. Cempel, Basics of vibroacoustic diagnostics of machines. WNT, Warsaw (1982). [Google Scholar]
  4. G. Box, G. Jenkins, Time series analysis, forecasting and control, London (1970). [Google Scholar]
  5. L. Bowerman, R.T. O'Connel, Forecasting and Time Series. Doxbury Press, USA (1979). [Google Scholar]
  6. C. Cempel, H.G. Natke, An introduction to the holistic dynamics of operating systems. Progress Report, No.2 (1996). [Google Scholar]
  7. C. Cempel, M. Tabaszewski, Multidimensional condition monitoring of machines in nonstationary operation. Mechanical Systems and Signal Processing (21), (2007). [CrossRef] [Google Scholar]
  8. N.R. Draper, H. Smith, Regression analysis of used. BNInż., Warsaw (1973). [Google Scholar]
  9. P. Eykhoff, Identification in dynamical systems, BNInż., Warsaw(1980). [Google Scholar]
  10. W. Findeisen, System analysis - base and methodology. PWN Warsaw (1985). [Google Scholar]
  11. K. Mańczak, Methods for identifying the multidimensional of control objects. WNT Warsaw (1971). [Google Scholar]
  12. S. Niziński, R. Michalski, Diagnosis of technical objects. ITE Radom (2002). [Google Scholar]
  13. T. Uhl, J. Giergiel, Identification of mechanical systems. PWN Warsaw (1990). [Google Scholar]
  14. A.K.S. Jardine, D. Lin, D. Banjevic, A review on machinery diagnostics and prognostics implementing condition-based maintenance, Mechanical Systems and Signal Processing, 20 (2006). [Google Scholar]
  15. J. Luo, M. Namburu, K. Pattipati, L. Qiao, M. Kawamoto, Model-based prognostic techniques, in: Proceedings of the IEEE. Systems Readiness Technology Conference (2003). [Google Scholar]
  16. Ł. Muślewski, Study and assessment of transport system operation efficiency. Journal of KONES Powertrain and Transport, Vol. 17, No. 4, Warsaw (2010). [Google Scholar]
  17. V.T. Tran, B.S. Yang, M.S. Oh, Machine condition prognosis based on regression trees and one-step-ahead prediction. Mechanical Systems and Signal Processing, 22, (2008). [Google Scholar]
  18. J. Qu, M.J. Zuo, An LSSVR - based algorithm for online system condition prognostics. Expert Systems with Applications, 39 (2) (2012). [Google Scholar]
  19. H. Tylicki, B. Żółtowski, Determination methods of the next diagnosis term of transport vehicle. Archives of Transport, Warsaw (2001). [Google Scholar]
  20. A. Zeliaś, Prognosis theory. PWE Warsaw (1984). [Google Scholar]
  21. B. Żółtowski, The methods of virtual engineering in the research risks status, safety and environmental operated machines. UTP Bydgoszcz (2012). [Google Scholar]
  22. B. Żółtowski, The study of environmental risks losing the suitability of technical systems. UTP Bydgoszcz (2013). [Google Scholar]
  23. M. Żółtowski, Management information systems in engineering and manufacturing. ITE - PIB, Radom (2011). [Google Scholar]
  24. M. Żółtowski, Investigations of harbour brick structures by using operational modal analysis. Polish Maritime Research, No. 1/ (81), vol.21 (2014). [Google Scholar]
  25. M. Żółtowski, Assessment State of Masonry Components Degradation. Applied Mechanics and Materials Vol. 617 (2014). [Google Scholar]
  26. B. Żółtowski, M. Żółtowski, Vibrations in the Assessment of Construction State. Applied Mechanics and Materials Vol. 617 (2014). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.