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Variational Mode Decomposition (VMD) is a useful tool for decomposing complex multi-component signals. However, one major drawback of VMD is the need to accurately determine the value of sub-signals (IMFs) before starting the process of segmentation. In fact, achieving optimal reconstruction of the denoised original signals depends on the determining optimal number of IMFs (K). This requirement poses a challenge in the capability of analyzing non-stationary or noisy signals. In this paper, a new approach to optimize the variational mode decomposition technique is proposed. This approach automatically estimates the optimal K and also effectively detects the characteristic frequencies associated with faulty bearings. This method is a combination of two algorithms which are based on cross-correlation and root mean square (RMS) statistical analysis. To confirm the efficacy of the proposed method, the bearing vibration dataset from the Case School of Engineering are used. Then, the K obtained through the proposed method are compared with other methods. The results demonstrate that the proposed approach exhibits superior robustness and precision when autonomously evaluating the optimal K for effective identification of bearing fault.
Czasopismo
Rocznik
Tom
Strony
art. no. 2024208
Opis fizyczny
Bibliogr. 24 poz., rys.
Twórcy
autor
- Laboratory of Electromechanical Systems, Department of Electromechanics, Faculty of Science and Technology, Badji Mokhtar-Annaba University, Annaba, Algeria
autor
- Mathematics and Their Interactions Laboratory, Abdelhafid Boussouf University Center of Mila, Algeria
autor
- University of Science and Technology Houari-Boumédiène, Algeria
autor
- Catholic University of Louvain, Belgium
autor
- Laboratory of Electromechanical Systems, Department of Electromechanics, Faculty of Science and Technology, Badji Mokhtar-Annaba University, Annaba, Algeria
autor
- Research Center in Industrial Technologies CRTI P.O. Box 64 Cheraga, Algeria
Bibliografia
- 1. Gangsar P, Tiwari R. Signal based condition monitoring techniques for fault detection and diagnosis of induction motors: A state-of-the-art review. Mechanical Systems and Signal Processing 2020; 144: 106908. https://doi.org/10.1016/j.ymssp.2020.106908.
- 2. Qin SR, Zhong YM. Research on the unified mathematical model for FT, STFT and WT and its applications. Mechanical Systems and Signal Processing 2004; 18(6): 1335-47. https://doi.org/10.1016/j.ymssp.2003.12.002.
- 3. Liu D, Cheng W, Wen W. Rolling bearing fault diagnosis via STFT and improved instantaneous frequency estimation method. Procedia Manufacturing 2020; 49: 166-72. https://doi.org/10.1016/j.promfg.2020.07.014.
- 4. Yan R, Gao RX, Chen X. Wavelets for fault diagnosis of rotary machines: A review with applications. Signal Processing 2014; 96: 1-15. https://doi.org/10.1016/j.sigpro.2013.04.015.
- 5. Saidi L, Ali JB, Fnaiech F. Bi-spectrum based-EMD applied to the non-stationary vibration signals for bearing faults diagnosis. ISA transactions 2014; 53(5): 1650-60. https://doi.org/10.1016/j.isatra.2014.06.002.
- 6. Yeh JR, Shieh JS, Huang NE. Complementary ensemble empirical mode decomposition: a novel noise enhanced data analysis method. Advances in Adaptive Data Analysis 2010; 02(02): 135-56. https://doi.org/10.1142/S1793536910000422.
- 7. Yongbo LI SS. Review of local mean decomposition and its application in fault diagnosis of rotating machinery. Journal of Systems Engineering and Electronics 2019; 30(4): 799-814. https://doi.org/10.21629/JSEE.2019.04.17.
- 8. Smith JS. The local mean decomposition and its application to EEG perception data. Journal of The Royal Society Interface 2005; 2(5): 443-54. https://doi.org/10.1098/rsif.2005.0058.
- 9. Hao R, Li F, A new method to suppress the EMD end point effect, Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis 2018, 38(2): 341-345.
- 10. Yu D, Cheng J, Yang Y. Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings. Mechanical Systems and Signal Processing 2005; 19(2): 259-70. https://doi.org/10.1016/S0888-3270(03)00099-2.
- 11. Dragomiretskiy K, Zosso D. Variational Mode Decomposition. IEEE Transactions on Signal Processing 2014; 62(3): 531-44. https://doi.org/10.1109/TSP.2013.2288675.
- 12. Isham MF, Leong MS, Lim MH, Ahmad ZA. Variational mode decomposition: mode determination method for rotating machinery diagnosis. Journal of Vibroengineering 2018; 20(7): 2604-21. https://doi.org/10.21595/jve.2018.19479.
- 13. Yang H, Liu S, Zhang H. Adaptive estimation of VMD modes number based on cross correlation coefficient. Journal of Vibroengineering 2017; 19(2): 1185-96. https://doi.org/10.21595/jve.2016.17236.
- 14. Ni Q, Ji JC, Feng K, Halkon B. A fault informationguided variational mode decomposition (FIVMD) method for rolling element bearings diagnosis. Mechanical Systems and Signal Processing 2022; 164: 108216. https://doi.org/10.1016/j.ymssp.2021.108216.
- 15. Liu S, Tang G, Wang X, He Y. Time-Frequency Analysis Based on Improved Variational Mode Decomposition and Teager Energy Operator for Rotor System Fault Diagnosis. Mathematical Problems in Engineering 2016; 2016: e1713046. https://doi.org/10.1155/2016/1713046.
- 16. Zheng X, Zhou G, Wang J, Ren H, Li D. Variational mode decomposition applied to offshore wind turbine rolling bearing fault diagnosis. 2016 35th Chinese Control Conference (CCC) 2016 p. 6673-7. https://doi.org/10.1109/ChiCC.2016.7554407.
- 17. G Tang, X Wang, Y He, S Liu, rolling bearing fault diagnosis based on variational mode decomposition and permutation entropy, 13th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), IEEE, 2016: 626-631. https://doi.org/10.1109/URAI.2016.7625792.
- 18. Li H, Liu T, Wu X, Chen Q. An optimized VMD method and its applications in bearing fault diagnosis. Measurement 2020; 166: 108185. https://doi.org/10.1016/j.measurement.2020.108185.
- 19. Wang R, Xu L, Liu F. Bearing fault diagnosis based on improved VMD and DCNN. Journal of Vibroengineering 2020; 22: 1055-68. https://doi.org/10.21595/jve.2020.21187.
- 20. Shi W, Wen G, Huang X, Zhang Z, Zhou Q. The VMD-scale space based hoyergram and its application in rolling bearing fault diagnosis. Measurement Science and Technology 2020; 31(12): 125006. https://doi.org/10.1088/1361-6501/aba70c.
- 21. Liu C, Wu Y, Zhen C. Rolling bearing fault diagnosis based on variational mode decomposition and fuzzy C means clustering. Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering 2015; 35: 3358-65. https://doi.org/10.13334/j.0258-8013.pcsee.2015.13.020.
- 22. Wu S, Feng F, Zhu J, Wu C, Zhang G. A Method for Determining Intrinsic Mode Function Number in Variational Mode Decomposition and Its Application to Bearing Vibration Signal Processing. Shock and Vibration 2020; 2020: e8304903. https://doi.org/10.1155/2020/8304903.
- 23. Case Western Reserve University Bearing Data Center website, 48k Drive End Bearking Fault Data | Case School of Engineering | Case Western Reserve University.
- 24. Smith WA, Randall RB. Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study. Mechanical Systems and Signal Processing 2015; 64-65: 100-31. https://doi.org/10.1016/j.ymssp.2015.04.021.
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Bibliografia
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