NOISE CONTROL OF THE BEGINNING AND DEVELOPMENT DYNAMICS OF FAULTS IN THE RUNNING GEAR OF THE ROLLING STOCK

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Transport Problems

Silesian University of Technology

Subject: Economics , Transportation , Transportation Science & Technology

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ISSN: 1896-0596
eISSN: 2300-861X

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VOLUME 15 , ISSUE 2 (June 2020) > List of articles

NOISE CONTROL OF THE BEGINNING AND DEVELOPMENT DYNAMICS OF FAULTS IN THE RUNNING GEAR OF THE ROLLING STOCK

Telman ALIEV * / Tofig BABAYEV / Tahir ALIZADA / Narmin RZAYEVA

Keywords : rail transport; running gear; rolling stock; noise; control; fault; accident; intelligent systems; informative attributes; reference; sets; correlation; spectral analysis

Citation Information : Transport Problems. Volume 15, Issue 2, Pages 83-91, DOI: https://doi.org/10.21307/tp-2020-022

License : (CC BY 4.0)

Received Date : 23-February-2019 / Accepted: 08-June-2020 / Published Online: 18-June-2020

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ABSTRACT

In contrast to traditional systems for monitoring fault of the running gear of the rolling stock, this paper proposes a technology of noise control at the onset of defects. The authors consider the possibility of creating an intelligent system that can perform noise diagnostics with the indication of the beginning of the latent period of the initiation of typical defects preceding faults. To this end, using the noise technology, sets of reference informative attributes are created in the training process. The reference sets, in turn, are used to determine the condition of the object at the beginning of the development of defects by comparing them with current noise estimates. It also allows controlling the dynamics of the development of defects.

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1. Aliyev, T. Noise Control of the Beginning and Development Dynamics of Accidents. Springer. 2019. 201 p. DOI: https://www.doi.org/10.1007/978-3-030-12512-7.

2. Melke, J. & Kramer, S. Diagnostic methods in the control of railway noise and vibration. Journal of Sound and Vibration. 1983. Vol. 87. No. 2. P. 377-386. DOI: https://www.doi.org/10.1016/0022-460X(83)90577-1.

3. Marquez, F.P.G. & Weston, P. & Roberts, C. Failure analysis and diagnostics for railway trackside equipment. Engineering Failure Analysis. 2007. Vol. 14. No. 8. P. 1411-1426. DOI: https://www.doi.org/10.1016/j.engfailanal.2007.03.005.

4. Lukasik, Z & Nowakowski, W. & Ciszewski, T & Freimane, J. A fault diagnostic methodology for railway automatics systems. Procedia Computer Science. 2019. Vol. 149. P. 159-166. DOI: https://www.doi.org/10.1016/j.procs.2019.01.119.

5. Moghaddam, A. A review on the current methods of railway induced vibration attenuations. International Journal of Science and Engineering Applications. 2017. Vol. 6. No. 4. P. 123-128. DOI: https://www.doi.org/10.7753/IJSEA0604.1001.

6. Wojciechowski, S. & Maruda, R.W. & Krolczyk, G.M & Niesłonyc, P. Application of signal to noise ratio and grey relational analysis to minimize forces and vibrations during precise ball end milling. Precision Engineering. 2008. Vol. 51. P. 582-596. DOI: https://doi.org/10.1016/j.precisioneng.2017.10.014.

7. Fushun, L. & Gao, S. & Han, H. & Tian, Z. & Peng, L. Interference reduction of high-energy noise for modal parameter identification of offshore wind turbines based on iterative signal extraction. Ocean Engineering. 2019. Vol. 183. P. 372-383. DOI: https://doi.org/10.1016/j.oceaneng.2019.05.009.

8. Багиров, С.М. & Манафов, Э.К. Нечеткая экспертная система для диагностики неисправностей букс подвижного состава. Вестник РГУПС. 2009. No. 4. P. 76-79. [In Russian: Bagirov, S.M. & Manafov E.K. Fuzzy expert system for diagnosing malfunctions of axleboxes of rolling stock. Bulletin of the RSUPS].

9. Jackson, P. Introduction to Expert Systems. Addison-Wesley. 1999. 542 p.

10. Aliev, T.A. & Alizada, T.A. & Rzayeva, N.E. Noise technologies and systems for monitoring the beginning of the latent period of accidents on fixed platforms. Mechanical Systems and Signal Processing. 2017. Vol. 87. Part A. P. 111-123. DOI: https://doi.org/10.1016/j.ymssp.2016.10.014.

11. Bendat, J.S. & Piersol, A.G. Random data: analysis and measurement procedures. 4th edn. Hoboken: Wiley. 2010. DOI: https://www.doi.org/10.1002/9781118032428.ch11.

12. Proakis, J.G. & Manolakis, D.G. Digital signal processing: principles, algorithms, and applications. 4th edn. Upper Saddle River: Pearson Prentice Hall. 2006.

13. Vetterli, M. & Kovacevic, J. & Goyal, V.K. Foundations of signal processing. 3rd edn. Cambridge: Cambridge University Press. 2014.

14. Owen, M. Practical signal processing. Cambridge: Cambridge University Press. 2012.

15. Kay, S.M. Fundamentals of statistical signal processing. Volume III: practical algorithm development. 1st edn. Prentice Hall. Westford. 2013.

16. Smith, S. Digital signal processing: a practical guide for engineers and scientists. 1st edn. Amsterdam: Newnes. 2002. DOI: https://doi.org/10.1016/B978-0-7506-7444-7.X5036-5.

17. Manolakis, D.G. & Ingle, V.K. Applied digital signal processing: theory and practice. 1st edn. Cambridge: Cambridge University Press. 2011. DOI: https://www.doi.org/10.1017/cbo9780511835261.

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