Науковий вісник НГУ

A hybrid ICEEMDAN and OMEDA-based vibrodiagnosis method for the bearing of rolling stock

• 
• 

User Rating:  / 0

PoorBest 

Details

Category: Content №2 2024

Last Updated on 01 May 2024

Published on 30 November -0001

Hits: 2325

Authors:

V.H.Puzyr, orcid.org/0000-0001-6096-9049, Ukrainian State University of Railway Transport, Kharkiv, Ukraine, е-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

S.V.Mykhalkiv*, orcid.org/0000-0002-0425-6295, Ukrainian State University of Railway Transport, Kharkiv, Ukraine, е-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

O.A.Plakhtii, orcid.org/0000-0002-1535-8991, Ukrainian State University of Railway Transport, Kharkiv, Ukraine, е-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

* Corresponding author e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

повний текст / full article

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2024, (2): 074 - 081

https://doi.org/10.33271/nvngu/2024-2/074

Abstract:

Purpose. To develop a hybrid method based on ICEEMDAN and OMEDA for high-fidelity detection of the diagnostic features of the technical condition of roller bearing.

Methodology. Due to digital signal processing the search was made of the informative components on the broadband spectra, envelope spectra, squared envelope spectra. The usage of the methods of mathematical statistics for the selection of informative IMFs as a result of iterative calculate procedures. The introduction of blind deconvolution method with a proper objective function for the enhancement of impulse fault features.

Findings. The use of traditional spectral methods provided an incomplete list of diagnostic features of bearing roller damage. The empirical mode decomposition (EMD) method and its minor versions provide the series of intrinsic mode functions (IMFs). On the broadband vibration spectra of the selected low-frequency IMFs, diagnostic features were not detected in a sufficient number. Further research focused on the development of a hybrid method that takes into account the demodulation procedure of the high-frequency components of those IMFs that were selected according to the complex evaluation index. The Fast Kurtogram technique determined an informative frequency band of 7.2–7.5 kHz for demodulation and construction of the squared envelope spectra, where harmonics of the roller spin frequency and some side bands with a width equal to the separator rotation frequency appeared. The use of OMEDA caused the reduction of residual noise on the squared envelope spectra and bilateral sidebands to appear around all harmonics of the roller spin frequency.

Originality. For the first time, the whole list of the diagnostic features of roller faults of the axle-box bearing was identified using the developed hybrid method, which involves steps of noise reduction for enhancing a physical meaning of a proper frequency components, associated with faults. The usage of the optimal minimum entropy deconvolution adjusted method helped to eliminate noise and provide all diagnostic features of the technical condition on the squared envelope spectrum.

Practical value. The results of the study help to control the quality of repair of the axle-box rolling bearings on the test rig at the workshop of the freight railway car depot.

Keywords: car, vibration, diagnostics, bearing, deconvolution, decomposition, spectrum

References.

1. Wang, Y-Z., Qin, Y., Zhao, X.-J., Zhang, S.-J., & Cheng, X.-Q. (2020). Bearing fault diagnosis with impulsive noise based on EMD and cyclic correntropy. Proceedings of the 9 ^th International Conference on Green Intelligent Transportation Systems and Safety. https://doi.org/10.1007/978-981-15-0644-4_112.

2. Rihi, A., Baïna, S., Mhada, F.-z., Elbachari, E., Tagemouati, H., Guerboub, M., & Benzakour, I. (2022). Predictive maintenance in mining industry: grinding mill case study. Procedia Computer Science, 207, 2483-2492. https://doi.org/10.1016/j.procs.2022.09.306.

3. Qin, Y., & Jia, L. (2019). Active safety methodologies of rail transportation. Advances in high-speed rail technology. Singapore: Springer. ISBN: 978-981-13-2259-4. https://doi.org/10.1007/978-981-13-2260-0.

4. An, G., Tong, Q., Zhang, Y., Liu, R., Li, W., Cao, J., …, & Pu, X. (2021). A Parameter-Optimized Variational Mode Decomposition Investigation for Fault Feature Extraction of Rolling Element Bearings. Mathematical Problems in Engineering, 2021, 6629474. https://doi.org/10.1155/2021/6629474.

5. Dybała, J., & Gałęzia, A. (2014). A Novel Method of Gearbox Health Vibration Monitoring Using Empirical Mode Decomposition. Proceedings of the Third International Conference on Condition Monitoring of Machinery in Non-Stationary Operations CMMNO 2013. https://doi.org/10.1007/978-3-642-39348-8_19.

6. Tarek, K., Abderrazek, D., Khemissi, B. M., Cherif, D. M., Lilia, C., & Nouredine, O. (2020). Comparative study between cyclostationary analysis, EMD, and CEEMDAN for the vibratory diagnosis of rotating machines in industrial environment. The International Journal of Advanced Manufacturing Technology, 109(9-12), 2747-2775. https://doi.org/10.1007/s00170-020-05848-z.

7. Cheng, J., Yang, Y., Li, X., & Cheng, J. (2021). Adaptive periodic mode decomposition and its application in rolling bearing fault diagnosis. Mechanical Systems and Signal Processing, 161, 107943. https://doi.org/10.1016/j.ymssp.2021.107943.

8. Zhang, X., Zhao, J., Ni, X., Sun, F., & Ge, H. (2019). Fault diagnosis for gearbox based on EMD-MOMEDA. International Journal of System Assurance Engineering and Management, 10(4), 836-847. https://doi.org/10.1007/s13198-019-00818-5.

9. Li, H., Liu, T., Wu, X., & Chen, Q. (2019). Application of EEMD and improved frequency band entropy in bearing fault feature extraction. ISA Transactions, 88, 170-185. https://doi.org/10.1016/j.isatra.2018.12.002.

10. Guo, W., Tse, P. W., & Djordjevich, A. (2012). Faulty bearing signal recovery from large noise using a hybrid method based on spectral kurtosis and ensemble empirical mode decomposition. Measurement, 45(5), 1308-1322. https://doi.org/10.1016/j.measurement.2012.01.001.

11. Kedadouche, M., Thomas, M., & Tahan, A. (2014). Monitoring Machines by Using a Hybrid Method Combining MED, EMD, and TKEO. Advances in Acoustics and Vibration, 2014, 1-10. https://doi.org/10.1155/2014/592080.

12. Ahn, J.-H., Kwak, D.-H., & Koh, B.-H. (2014). Fault Detection of a Roller-Bearing System through the EMD of a Wavelet Denoised Signal. Sensors, 14(8), 15022-15038. https://doi.org/10.3390/s140815022.

13. Bouhalais, M. L., Djebala, A., Ouelaa, N., & Babouri, M. K. (2018). CEEMDAN and OWMRA as a hybrid method for rolling bearing fault diagnosis under variable speed. The International Journal of Advanced Manufacturing Technology, 94(5-8), 2475-2489. https://doi.org/10.1007/s00170-017-1044-0.

14. He, C., Niu, P., Yang, R., Wang, C., Li, Z., & Li, H. (2019). Incipient rolling element bearing weak fault feature extraction based on adaptive second-order stochastic resonance incorporated by mode decomposition. Measurement, 145, 687-701. https://doi.org/10.1016/j.measurement.2019.05.052.

15. Saidi, L., Ali, J. B., Benbouzid, M., & Bechhoefer, E. (2016). The use of SESK as a trend parameter for localized bearing fault diagnosis in induction machines. ISA Transactions, 63, 436-447. https://doi.org/10.1016/j.isatra.2016.02.019.

16. Smith, W. A., & Randall, R. B. (2015). Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study. Mechanical Systems and Signal Processing, 64-65, 100-131. https://doi.org/10.1016/j.ymssp.2015.04.021.

17. Klausen, A., Robbersmyr, K. G., & Karimi, H. R. (2017). Autonomous Bearing Fault Diagnosis Method based on Envelope Spectrum. IFAC-PapersOnLine, 50(1), 13378-13383. https://doi.org/10.1016/j.ifacol.2017.08.2262.

18. Xu, Y., Feng, G., Tang, X., Yang, S., Fengshou, G., & Ball, A. D. (2023). A Modulation Signal Bispectrum Enhanced Squared Envelope for the detection and diagnosis of compound epicyclic gear faults. Structural Health Monitoring, 22(1), 562-580. https://doi.org/10.1177/14759217221098577.

19. Randall, R. B. (2021). Vibration-based condition monitoring. NJ: John Wiley & Sons Ltd. ISBN: 978-1-119-47755-6.

20. Colominas, M. A., Schlotthauer, G., & Torres, M. E. (2014). Improved complete ensemble EMD: A suitable tool for biomedical signal processing. Biomedical Signal Processing and Control, 14, 19-29. https://doi.org/10.1016/j.bspc.2014.06.009.

21. Rabah, A., & Abdelhafid, K. (2018). Rolling bearing fault diagnosis based on improved complete ensemble empirical mode of decomposition with adaptive noise combined with minimum entropy deconvol ution. Journal of Vibroengineering, 20(1), 240-257. https://doi.org/10.21595/jve.2017.18762.

22. Lei, Y., Liu, Z., Ouazri, J., & Lin, J. (2017). A fault diagnosis method of rolling element bearings based on CEEMDAN. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 231(10), 1804-1815. https://doi.org/10.1177/0954406215624126.

23. Gao, K., Xu, X., Li, J., Jiao, S., & Shi, N. (2021). Application of multi-layer denoising based on ensemble empirical mode decomposition in extraction of fault feature of rotating machinery. PLoS ONE, 16(7), e0254747. https://doi.org/10.1371/journal.pone.0254747.

24. Miao, Y., Zhang, B., Lin, J., Zhao, M., Liu, H., Liu, Z., & Li, H. (2022). A review on the application of blind deconvolution in machinery fault diagnosis. Mechanical Systems and Signal Processing, 163, 108202. https://doi.org/10.1016/j.ymssp.2021.108202.

25. He, L., Yi, C., Wang, D., Wang, F., & Lin, J.-h. (2021). Optimized minimum generalized Lp/Lq deconvolution for recovering repetitive impacts from a vibration mixture. Measurement, 168, 108329. https://doi.org/10.1016/j.measurement.2020.108329.

26. McDonald, G. L., & Zhao, Q. (2017). Multipoint Optimal Minimum Entropy Deconvolution and Convolution Fix: Application to vibration fault detection. Mechanical Systems and Signal Processing, 82, 461-477. https://doi.org/10.1016/j.ymssp.2016.05.036.

27. Cheng, Y., Wang, Z., Zhang, W., & Huang, G. (2019). Particle swarm optimization algorithm to solve the deconvolution problem for rolling element bearing fault diagnosis. ISA Transactions, 90, 244-267. https://doi.org/10.1016/j.isatra.2019.01.012.

• < Prev
• Next >